Install
openclaw skills install @li152/xg-quant-pt小果(微信：xg_quant)量化交易平台助手技能。小果量化是一个专业的量化交易系统，提供完整的回测、模拟交易、社区策略分享等功能。 【核心功能】 - 📊 历史行情数据获取（股票、ETF、可转债） - 📈 多维度因子数据提取（技术指标、Alpha因子、动量因子等） - 💰 财务数据分析（资产负债表、利润表、现金流量表等） - 🔄 多种策略回测（定投、动量、资产配置、网格、海龟、均值方差等） - 🤖 模拟交易和社区策略管理 - 📉 多标的量化分析（相关性矩阵、协方差矩阵、投资组合优化） - 📊 股票组合收益分析（绩效指标、夏普比率、最大回撤等） - ⏰ 分钟级K线数据
小果量化平台（XG Quant Platform）
一、平台简介
小果量化数据API是一个基于量化数据接口，提供股票历史数据、因子数据、财务数据以及多种量化策略回测功能。
主要功能
| 功能模块 | 说明 |
|---|---|
| 📊 历史行情数据 | 获取股票、ETF、可转债的日线历史数据 |
| 📈 因子数据提取 | 数百种技术指标、Alpha因子、动量因子 |
| 💰 财务数据查询 | 资产负债表、利润表、现金流量表、估值数据 |
| 🔄 策略回测 | 定投、动量、资产配置、网格、海龟等9种策略 |
| 🤖 模拟交易 | 个人策略模拟交易和社区策略分享 |
| 📉 量化分析 | 相关性矩阵、协方差矩阵、投资组合优化 |
| 📊 组合分析 | 完整绩效指标（50+项） |
二、快速开始
1. 安装客户端
使用教程https://gitcode.com/qq_50882340/xg_quant_backtrader_data
一、介绍
小果量化数据API是一个基于量化数据接口，提供股票历史数据、因子数据、财务数据以及多种量化策略回测功能。
主要功能 📊 历史行情数据获取
📈 因子数据提取
💰 财务数据查询
🔄 多种策略回测（定投、动量、资产配置、网格、海龟等）
🤖 模拟交易和社区策略
用户操作
1获取用户信息
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例49：获取用户信息
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
返回数据：
username - 用户名
expiry - 账户到期时间
days_until_expiry - 剩余天数
expiry_warning - 是否即将到期
"""
print("\n" + "=" * 60)
print("📊 获取用户信息")
print("=" * 60)
result = client.get_user_info()
print("用户信息：")
print(result)
2检查授权码有效性
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例50：检查授权码有效性
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
返回数据：
status - 状态（success/failed）
message - 消息
user_info - 用户信息
"""
print("\n" + "=" * 60)
print("📊 检查授权码有效性")
print("=" * 60)
result = client.check_password_is_av_user()
print("授权码检查结果：")
print(result)
3读取个人全部模拟策略
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例71：读取个人全部模拟策略
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
返回数据：
strategies - 策略列表（包含策略类型、名称、建立时间等）
total - 策略总数
"""
print("\n" + "=" * 60)
print("📊 读取个人全部模拟策略")
print("=" * 60)
result = client.get_all_moni_trader_data(
user='小果'
)
print("模拟策略列表：")
print(f"策略总数: {result.get('total', 0)}")
strategies = result.get('strategies', [])
for i, s in enumerate(strategies, 1):
print(f" {i}. {s.get('策略类型')} - {s.get('策略名称')} (建立时间: {s.get('建立时间')})")
4读取个人全部社区策略
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例72：读取个人全部社区策略
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
返回数据：
strategies - 策略列表（包含策略类型、名称、建立时间等）
total - 策略总数
"""
print("\n" + "=" * 60)
print("📊 读取个人全部社区策略")
print("=" * 60)
result = client.get_all_moni_trader_data_sq(
user='小果'
)
print("社区策略列表：")
print(f"策略总数: {result.get('total', 0)}")
strategies = result.get('strategies', [])
for i, s in enumerate(strategies, 1):
print(f" {i}. {s.get('策略类型')} - {s.get('策略名称')} (建立时间: {s.get('建立时间')})")
5删除单个模拟策略
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例67：删除单个模拟策略
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
st_type: str = '定投策略' - 策略类型
st_name: str = '小果定投模拟策略公开' - 策略名称
open_show: str = '是' - 是否公开策略
策略类型可选值：
'定投策略'、'动量策略'、'资产配置策略'、
'资产配置平衡策略'、'网格策略'、'海龟策略'、
'综合动量策略'、'条件因子策略'、'排序多因子策略'、
'均值方差策略'
"""
print("\n" + "=" * 60)
print("📊 删除单个模拟策略")
print("=" * 60)
result = client.del_moni_trader_data(
user='小果',
st_type='定投策略',
st_name='小果定投模拟策略公开',
open_show='是'
)
print("删除结果：")
print(result)
6删除单个社区策略
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例68：删除单个社区策略
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
st_type: str = '定投策略' - 策略类型
st_name: str = '小果定投模拟策略公开' - 策略名称
open_show: str = '是' - 是否公开策略
"""
print("\n" + "=" * 60)
print("📊 删除单个社区策略")
print("=" * 60)
result = client.del_moni_trader_data_sq(
user='小果',
st_type='定投策略',
st_name='小果定投模拟策略公开',
open_show='是'
)
print("删除结果：")
print(result)
7删除个人全部模拟策略
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例69：删除全部模拟策略
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
confirm: str = '是' - 确认删除（必须为'是'才能执行）
⚠️ 警告：此操作将删除该用户的所有模拟策略，不可恢复！
"""
print("\n" + "=" * 60)
print("📊 删除全部模拟策略")
print("=" * 60)
result = client.del_all_moni_trader_data(
user='小果',
confirm='是'
)
print("删除结果：")
print(result)
8删除全部社区策略
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例70：删除全部社区策略
# ============================================================
"""
参数说明：
user: str = '小果' - 用户名称
confirm: str = '是' - 确认删除（必须为'是'才能执行）
⚠️ 警告：此操作将删除该用户的所有社区策略，不可恢复！
"""
print("\n" + "=" * 60)
print("📊 删除全部社区策略")
print("=" * 60)
result = client.del_all_moni_trader_data_sq(
user='小果',
confirm='是'
)
print("删除结果：")
print(result)
二、使用教程
1. 安装依赖并初始化
# ============================================================
# 完整示例1：初始化客户端
# ============================================================
"""
参数说明：
url: str = "数据库服务器" - 服务器地址
port: int = 数据库端口 - 服务器端口
user: str = "小果" - 用户名
password: str = "小果" - 密码
auth_code: str = "小果" - 授权码
"""
import requests
import json
import pandas as pd
import numpy as np
from typing import Optional, Dict, Any, List, Union
from datetime import datetime
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
print("✅ 客户端初始化成功！")
print(f"📡 服务器地址: http://数据库服务器:数据库端口")
2. 获取股票历史行情数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例2：获取历史行情数据
# ============================================================
"""
参数说明：
stock: str = '600031.SH' - 股票代码，格式：代码.市场（SH/SZ）
start_date: str = '20200101' - 开始日期，格式YYYYMMDD
end_date: str = '20261231' - 结束日期，格式YYYYMMDD
返回字段：
date - 交易日期
open - 开盘价
high - 最高价
low - 最低价
close - 收盘价
volume - 成交量
amount - 成交金额
zdf - 涨跌幅
pct_chg - 百分比变化
"""
print("\n" + "=" * 60)
print("📊 获取历史行情数据")
print("=" * 60)
# 获取单只股票历史数据
result = client.get_stock_hist_data(
stock='513100.SH', # 平安银行
start_date='20240101',
end_date='20500101'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
print(f"\n数据列: {df.columns.tolist()}")
3读取ETF基金历史行情数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例2：获取历史行情数据
# ============================================================
"""
参数说明：
stock: str = '513100.SH' - 股票代码，格式：代码.市场（SH/SZ）
start_date: str = '20200101' - 开始日期，格式YYYYMMDD
end_date: str = '20261231' - 结束日期，格式YYYYMMDD
返回字段：
date - 交易日期
open - 开盘价
high - 最高价
low - 最低价
close - 收盘价
volume - 成交量
amount - 成交金额
zdf - 涨跌幅
pct_chg - 百分比变化
"""
print("\n" + "=" * 60)
print("📊 获取历史行情数据")
print("=" * 60)
# 获取单只股票历史数据
result = client.get_stock_hist_data(
stock='513100.SH', # 平安银行
start_date='20240101',
end_date='20500101'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
print(f"\n数据列: {df.columns.tolist()}")
4读取可转债历史行情数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例2：获取历史行情数据
# ============================================================
"""
参数说明：
stock: str = '513100.SH' - 股票代码，格式：代码.市场（SH/SZ）
start_date: str = '20200101' - 开始日期，格式YYYYMMDD
end_date: str = '20261231' - 结束日期，格式YYYYMMDD
返回字段：
date - 交易日期
open - 开盘价
high - 最高价
low - 最低价
close - 收盘价
volume - 成交量
amount - 成交金额
zdf - 涨跌幅
pct_chg - 百分比变化
"""
print("\n" + "=" * 60)
print("📊 获取历史行情数据")
print("=" * 60)
# 获取单只股票历史数据
result = client.get_stock_hist_data(
stock='128136.SZ',
start_date='20240101',
end_date='20500101'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
print(f"\n数据列: {df.columns.tolist()}")
5. 获取股票因子数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例3：获取因子数据
# ============================================================
"""
参数说明：
stock: str = '600031.SH' - 股票代码
start_date: str = '20200101' - 开始日期，格式YYYYMMDD
end_date: str = '20261231' - 结束日期，格式YYYYMMDD
columns: str = 'date,close,open,high,low,volume,amount' - 选择字段，逗号分隔
【基础因子字段】
date - 交易日期
证券代码 - 股票代码
close - 收盘价
open - 开盘价
high - 最高价
low - 最低价
volume - 成交量
amount - 成交金额
zdf - 涨跌幅
【涨跌幅因子】
5日涨跌幅 - 5日涨跌幅
10日涨跌幅 - 10日涨跌幅
20日涨跌幅 - 20日涨跌幅
30日涨跌幅 - 30日涨跌幅
60日涨跌幅 - 60日涨跌幅
120日涨跌幅 - 120日涨跌幅
250日涨跌幅 - 250日涨跌幅
【价格距离均线涨跌幅】
价格距离5日均线涨跌幅 - 价格距离5日均线涨跌幅
价格距离10日均线涨跌幅 - 价格距离10日均线涨跌幅
价格距离20日均线涨跌幅 - 价格距离20日均线涨跌幅
价格距离30日均线涨跌幅 - 价格距离30日均线涨跌幅
价格距离60日均线涨跌幅 - 价格距离60日均线涨跌幅
价格距离120日均线涨跌幅 - 价格距离120日均线涨跌幅
【均线距离涨跌幅】
5日均线距离10日均线涨跌幅 - 5日均线距离10日均线涨跌幅
10日均线距离20日均线涨跌幅 - 10日均线距离20日均线涨跌幅
20日均线距离30日均线涨跌幅 - 20日均线距离30日均线涨跌幅
30日均线距离60日均线涨跌幅 - 30日均线距离60日均线涨跌幅
60日均线距离120日均线涨跌幅 - 60日均线距离120日均线涨跌幅
【移动平均线】
5日均线 - 5日均线
10日均线 - 10日均线
20日均线 - 20日均线
30日均线 - 30日均线
60日均线 - 60日均线
120日均线 - 120日均线
【均线交叉信号】
5日10日金叉 - 5日10日均线金叉
10日20日金叉 - 10日20日均线金叉
20日30日金叉 - 20日30日均线金叉
30日60日金叉 - 30日60日均线金叉
60日120日金叉 - 60日120日均线金叉
5日10日死叉 - 5日10日均线死叉
10日20日死叉 - 10日20日均线死叉
20日30日死叉 - 20日30日均线死叉
30日60日死叉 - 30日60日均线死叉
60日120日死叉 - 60日120日均线死叉
【价格位置判断】
价格在5均线上 - 价格是否在5日均线上
价格在10均线上 - 价格是否在10日均线上
价格在20均线上 - 价格是否在20日均线上
价格在30均线上 - 价格是否在30日均线上
价格在60均线上 - 价格是否在60日均线上
价格在120均线上 - 价格是否在120日均线上
5均线在10均线上 - 5日均线是否在10日均线上
10均线在20均线上 - 10日均线是否在20日均线上
20均线在30均线上 - 20日均线是否在30日均线上
30均线在60均线上 - 30日均线是否在60日均线上
60均线在120均线上 - 60日均线是否在120日均线上
【技术指标 - KDJ】
KDJ_K - KDJ指标K值
KDJ_D - KDJ指标D值
KDJ_J - KDJ指标J值
KDJ_KD金叉 - KDJ金叉信号
KDJ_KD死叉 - KDJ死叉信号
【技术指标 - MACD】
MACD_DIF - MACD平滑异同平均线DIF
MACD_DEA - MACD平滑异同平均线DEA
MACD_MACD - MACD平滑异同平均线MACD
MACD_金叉 - MACD金叉信号
MACD_死叉 - MACD死叉信号
【技术指标 - RSI】
RSI1 - RSI相对强弱RSI1
RSI2 - RSI相对强弱RSI2
RSI3 - RSI相对强弱RSI3
RSI_金叉 - RSI金叉信号
RSI_死叉 - RSI死叉信号
【技术指标 - BOLL布林线】
BOLL_BOLL - BOLL布林线中轨
BOLL_UB - BOLL布林线上轨
BOLL_LB - BOLL布林线下轨
【技术指标 - CCI】
CCI - CCI商品路径指标
【技术指标 - MFI】
MFI - MFI资金流量指标
【技术指标 - MTM】
MTM_MTM - MTM动量线MTM值
MTM_MTMMA - MTM动量线MTMMA值
【技术指标 - SKDJ】
SKDJ_K - SKDJ慢速随机K值
SKDJ_D - SKDJ慢速随机D值
【技术指标 - WR】
WR1 - WR威廉指标WR1
WR2 - WR威廉指标WR2
WR_金叉 - WR金叉信号
WR_死叉 - WR死叉信号
【技术指标 - PSY】
PSY_PSY - PSY心理线PSY
PSY_PSYMA - PSY心理线PSYMA
PSY_金叉 - PSY金叉信号
PSY_死叉 - PSY死叉信号
【技术指标 - BIAS乖离率】
BIAS1 - BIAS乖离率BIAS1
BIAS2 - BIAS乖离率BIAS2
BIAS3 - BIAS乖离率BIAS3
BIAS_QL_BIAS - BIAS_QL乖离率传统版BIAS值
BIAS_QL_BIASMA - BIAS_QL乖离率传统版BIASMA值
BIAS36_BIAS36 - BIAS36三六乖离BIAS36
BIAS36_BIAS612 - BIAS36三六乖离BIAS612
BIAS36_MABIAS - BIAS36三六乖离MABIAS
【技术指标 - DMI】
DMI_PDI - DMI趋向指标PDI
DMI_MDI - DMI趋向指标MDI
DMI_ADX - DMI趋向指标ADX
DMI_ADXR - DMI趋向指标ADXR
【技术指标 - DMA】
DMA_XT_DIF - DMA_XT平均差DIF
DMA_XT_DIFMA - DMA_XT平均差DIFMA
【技术指标 - DPO】
DPO_DPO - DPO区间震荡线DPO
DPO_MADPO - DPO区间震荡线MADPO
【技术指标 - EMV】
EMV_EMV - EMV简易波动指标EMV
EMV_MAEMV - EMV简易波动指标MAEMV
【技术指标 - TRIX】
TRIX_TRIX - TRIX三重指数平均线TRIX
TRIX_MATRIX - TRIX三重指数平均线MATRIX
【技术指标 - UOS】
UOS_UOS - UOS终极指标UOS
UOS_MAUOS - UOS终极指标MAUOS
【技术指标 - VPT】
VTP_VPT - VPT量价曲线VPT
VTP_MAVP - VPT量价曲线MAVP
【技术指标 - WVAD】
WVAD_WVAD - WVAD威廉变异离散量WVAD
WVAD_MAWVAD - WVAD威廉变异离散量MAWVAD
【技术指标 - BRAR】
BRAR_BR - BRAR情绪指标BR
BRAR_AR - BRAR情绪指标AR
【技术指标 - CR】
CR_CR - CR带状能量线CR
CR_MA1 - CR带状能量线MA1
CR_MA2 - CR带状能量线MA2
CR_MA3 - CR带状能量线MA3
CR_MA4 - CR带状能量线MA4
【技术指标 - MASS】
MASS_MASS - MASS梅斯线MASS
MASS_MAMASS - MASS梅斯线MAMASS
【技术指标 - VR】
VR_VR - VR成交量变异率VR
VR_MAVR - VR成交量变异率MAVR
【技术指标 - OBV】
OBV_OBV - OBV累积能量线OBV
OBV_MAOBV - OBV累积能量线MAOBV
【技术指标 - VOL成交量】
VOL_XT_MAVOL1 - VOL成交量MAVOL1
VOL_XT_MAVOL2 - VOL成交量MAVOL2
【技术指标 - VRSI】
VRSI1 - VRSI相对强弱量RSI1
VRSI2 - VRSI相对强弱量RSI2
VRSI3 - VRSI相对强弱量RSI3
【技术指标 - HSL换手线】
HSL_HSL - HSL换手线HSL
HSL_MAHSL - HSL换手线MAHSL
【技术指标 - ACD】
ACD_ACD - ACD升降线ACD
ACD_MAACD - ACD升降线MAACD
【技术指标 - BBI】
BBI - BBI多空均线
【技术指标 - EXPMA】
EXPMA_EXP1 - EXPMA指数平均线EXP1
EXPMA_EXP2 - EXPMA指数平均线EXP2
【技术指标 - SAR】
SAR - SAR抛物线指标
【技术指标 - AMO成交金额】
AMO_AMOW - AMO成交金额AMOW
AMO_AMO1 - AMO成交金额AMO1
AMO_AMO2 - AMO成交金额AMO2
【技术指标 - MIKE】
MIKE_STOR - MIKE麦克支撑压力STOR
MIKE_MIDR - MIKE麦克支撑压力MIDR
MIKE_WEKR - MIKE麦克支撑压力WEKR
MIKE_WEKS - MIKE麦克支撑压力WEKS
MIKE_MIDS - MIKE麦克支撑压力MIDS
MIKE_STOS - MIKE麦克支撑压力STOS
【技术指标 - ENE】
ENE_UPPER - ENE轨道线上轨
ENE_LOWER - ENE轨道线下轨
ENE_ENE - ENE轨道线ENE
【技术指标 - PBX瀑布线】
PBX_PBX1 - PBX瀑布线PBX1
PBX_PBX2 - PBX瀑布线PBX2
PBX_PBX3 - PBX瀑布线PBX3
PBX_PBX4 - PBX瀑布线PBX4
PBX_PBX5 - PBX瀑布线PBX5
PBX_PBX6 - PBX瀑布线PBX6
【技术指标 - XS薛斯通道】
XS_SUP - XS薛斯通道SUP
XS_SDN - XS薛斯通道SDN
XS_LUP - XS薛斯通道LUP
XS_LDN - XS薛斯通道LDN
【技术指标 - TQN唐奇安通道】
TQN_周期高点 - TQN唐奇安通道周期高点
TQN_周期低点 - TQN唐奇安通道周期低点
TQN_平空开多 - TQN唐奇安通道平空开多信号
TQN_平多开空 - TQN唐奇安通道平多开空信号
【技术指标 - ALLIGAT鳄鱼线】
ALLIGAT_上唇 - ALLIGAT鳄鱼线上唇
ALLIGAT_牙齿 - ALLIGAT鳄鱼线牙齿
ALLIGAT_下颚 - ALLIGAT鳄鱼线下颚
【技术指标 - GMMA顾比均线】
GMMA_MA3 - GMMA顾比均线MA3
GMMA_MA5 - GMMA顾比均线MA5
GMMA_MA8 - GMMA顾比均线MA8
GMMA_MA10 - GMMA顾比均线MA10
GMMA_MA12 - GMMA顾比均线MA12
GMMA_MA15 - GMMA顾比均线MA15
GMMA_MA30 - GMMA顾比均线MA30
GMMA_MA35 - GMMA顾比均线MA35
GMMA_MA40 - GMMA顾比均线MA40
GMMA_MA45 - GMMA顾比均线MA45
GMMA_MA50 - GMMA顾比均线MA50
GMMA_MA60 - GMMA顾比均线MA60
【技术指标 - VMACD】
VMACD_DIF - VMACD量平滑异同平均线DIF
VMACD_DEA - VMACD量平滑异同平均线DEA
VMACD_MACD - VMACD量平滑异同平均线MACD
【技术指标 - SMACD】
SMACD_DEA - SMACD单线平滑异同平均线DEA
SMACD_MACD - SMACD单线平滑异同平均线MACD
【技术指标 - QACD】
QACD_DIF - QACD快速异同平均线DIF
QACD_MACD - QACD快速异同平均线MACD
QACD_DDIF - QACD快速异同平均线DDIF
【技术指标 - 成交量相关】
连续上涨天数 - 连续上涨天数
连续下跌天数 - 连续下跌天数
【技术指标 - 偏度峰度】
5日偏度 - 5日偏度
10日偏度 - 10日偏度
20日偏度 - 20日偏度
30日偏度 - 30日偏度
60日偏度 - 60日偏度
120日偏度 - 120日偏度
5日峰度 - 5日峰度
10日峰度 - 10日峰度
20日峰度 - 20日峰度
30日峰度 - 30日峰度
60日峰度 - 60日峰度
120日峰度 - 120日峰度
【Alpha因子 - 世界金融实验室101因子】
Alpha001 至 Alpha191 - 世界金融实验室101因子（共191个）
【交易信号因子】
六脉神剑 - 六脉神剑交易信号
小波段交易 - 小波段交易信号
大波段交易 - 大波段交易信号
波段超级买卖 - 波段超级买卖信号
【回归分析因子】
3日回归动量 - 3日回归动量
5日回归动量 - 5日回归动量
7日回归动量 - 7日回归动量
9日回归动量 - 9日回归动量
12日回归动量 - 12日回归动量
15日回归动量 - 15日回归动量
18日回归动量 - 18日回归动量
20日回归动量 - 20日回归动量
23日回归动量 - 23日回归动量
25日回归动量 - 25日回归动量
28日回归动量 - 28日回归动量
30日回归动量 - 30日回归动量
35日回归动量 - 35日回归动量
40日回归动量 - 40日回归动量
45日回归动量 - 45日回归动量
50日回归动量 - 50日回归动量
60日回归动量 - 60日回归动量
【回归斜率】
5日回归斜率 - 5日回归斜率
10日回归斜率 - 10日回归斜率
20日回归斜率 - 20日回归斜率
30日回归斜率 - 30日回归斜率
60日回归斜率 - 60日回归斜率
120日回归斜率 - 120日回归斜率
【标准差】
5日标准差 - 5日标准差
10日标准差 - 10日标准差
20日标准差 - 20日标准差
30日标准差 - 30日标准差
60日标准差 - 60日标准差
120日标准差 - 120日标准差
【最高最低值周期】
5日最高值到当前周期 - 5日最高值到当前周期
10日最高值到当前周期 - 10日最高值到当前周期
20日最高值到当前周期 - 20日最高值到当前周期
30日最高值到当前周期 - 30日最高值到当前周期
60日最高值到当前周期 - 60日最高值到当前周期
120日最高值到当前周期 - 120日最高值到当前周期
5日最低值到当前周期 - 5日最低值到当前周期
10日最低值到当前周期 - 10日最低值到当前周期
20日最低值到当前周期 - 20日最低值到当前周期
30日最低值到当前周期 - 30日最低值到当前周期
60日最低值到当前周期 - 60日最低值到当前周期
120日最低值到当前周期 - 120日最低值到当前周期
【Alpha系数】
5日Alpha - 5日Alpha
10日Alpha - 10日Alpha
20日Alpha - 20日Alpha
30日Alpha - 30日Alpha
60日Alpha - 60日Alpha
120日Alpha - 120日Alpha
【Beta系数】
5日Beta - 5日Beta
10日Beta - 10日Beta
20日Beta - 20日Beta
30日Beta - 30日Beta
60日Beta - 60日Beta
120日Beta - 120日Beta
【夏普比率】
5日夏普比率 - 5日夏普比率
10日夏普比率 - 10日夏普比率
20日夏普比率 - 20日夏普比率
30日夏普比率 - 30日夏普比率
60日夏普比率 - 60日夏普比率
120日夏普比率 - 120日夏普比率
【年化波动率】
5日年化波动率 - 5日年化波动率
10日年化波动率 - 10日年化波动率
20日年化波动率 - 20日年化波动率
30日年化波动率 - 30日年化波动率
60日年化波动率 - 60日年化波动率
120日年化波动率 - 120日年化波动率
【最大回撤】
5日最大回撤 - 5日最大回撤
10日最大回撤 - 10日最大回撤
20日最大回撤 - 20日最大回撤
30日最大回撤 - 30日最大回撤
60日最大回撤 - 60日最大回撤
120日最大回撤 - 120日最大回撤
【上涨/下跌捕获率】
5日上涨捕获率 - 5日上涨捕获率
10日上涨捕获率 - 10日上涨捕获率
20日上涨捕获率 - 20日上涨捕获率
30日上涨捕获率 - 30日上涨捕获率
60日上涨捕获率 - 60日上涨捕获率
120日上涨捕获率 - 120日上涨捕获率
5日下跌捕获率 - 5日下跌捕获率
10日下跌捕获率 - 10日下跌捕获率
20日下跌捕获率 - 20日下跌捕获率
30日下跌捕获率 - 30日下跌捕获率
60日下跌捕获率 - 60日下跌捕获率
120日下跌捕获率 - 120日下跌捕获率
【庄家/主力指标】
ZJTJ_无庄控盘 - ZJTJ庄家抬轿无庄控盘
ZJTJ_开始控盘 - ZJTJ庄家抬轿开始控盘
ZJTJ_有庄控盘 - ZJTJ庄家抬轿有庄控盘
ZJTJ_主力出货 - ZJTJ庄家抬轿主力出货
CYW - CYW主力控盘
ZLJC_JCS - ZLJC主力进出JCS
ZLJC_JCM - ZLJC主力进出JCM
ZLJC_JCL - ZLJC主力进出JCL
ZLMM_MMS - ZLMM主力买卖MMS
ZLMM_MMM - ZLMM主力买卖MMM
ZLMM_MML - ZLMM主力买卖MML
LHXJ_主力弃盘 - LHXJ猎狐先觉主力弃盘
LHXJ_主力控盘 - LHXJ猎狐先觉主力控盘
LYJH_机构做空能量线 - LYJH猎鹰歼狐机构做空能量线
LYJH_机构做多能量线 - LYJH猎鹰歼狐机构做多能量线
【智能交易信号】
BDZX_AK - BDZX波段之星AK
BDZX_AD1 - BDZX波段之星AD1
BDZX_AJ - BDZX波段之星AJ
BDZX_买进 - BDZX波段之星买进信号
BDZX_卖出 - BDZX波段之星卖出信号
CYHT_SK - CYHT财运亨通SK
CYHT_SD - CYHT财运亨通SD
CYHT_卖出 - CYHT财运亨通卖出信号
CYHT_买进 - CYHT财运亨通买进信号
BSQJ_B买 - BSQJ买卖区间B买信号
BSQJ_持仓 - BSQJ买卖区间持仓信号
BSQJ_S卖 - BSQJ买卖区间S卖信号
BSQJ_空仓 - BSQJ买卖区间空仓信号
JFZX_多头力量 - JFZX飓风智能中线多头力量
JFZX_空头力量 - JFZX飓风智能中线空头力量
XJDX_J - XJDX超级短线J
XJDX_D - XJDX超级短线D
XJDX_K - XJDX超级短线K
【其他特色指标】
CYS - CYS市场盈亏
CYR_CYR - CYR市场强弱CYR
CYR_MACYR - CYR市场强弱MACYR
CYE_CYEL - CYE市场趋势CYEL
CYE_CYES - CYE市场趋势CYES
CYS - CYS市场盈亏
RAD_RADER1 - RAD威力雷达RADER1
RAD_RADERMA - RAD威力雷达RADERMA
SG_XDT_QR - SG_XDT心电图QR
SG_XDT_MQR1 - SG_XDT心电图MQR1
SG_XDT_MQR2 - SG_XDT心电图MQR2
SG_NDB_DK - SG_NDB脑电波DK
SG_NDB_MDK1 - SG_NDB脑电波MDK1
SG_NDB_MDK2 - SG_NDB脑电波MDK2
SG_SMX_ZY1 - SG_SMX生命线ZY1
SG_SMX_ZY2 - SG_SMX生命线ZY2
SG_SMX_ZY3 - SG_SMX生命线ZY3
SG_LB_量比 - SG_LB量比
SG_LB_MA5 - SG_LB量比MA5
SG_LB_MA10 - SG_LB量比MA10
SG_PF - SG_PF强势股评分
SLZT_白龙 - SLZT神龙在天白龙
SLZT_黄龙 - SLZT神龙在天黄龙
SLZT_紫龙 - SLZT神龙在天紫龙
SLZT_青龙 - SLZT神龙在天青龙
SLZT_红龙 - SLZT神龙在天红龙
SLZT_蓝龙 - SLZT神龙在天蓝龙
ADVOL_ADVOL - ADVOL龙系离散量ADVOL
ADVOL_MA1 - ADVOL龙系离散量MA1
ADVOL_MA2 - ADVOL龙系离散量MA2
JAX_J - JAX济安线J
JAX_A - JAX济安线A
JAX_X - JAX济安线X
LON_LON - LON龙系长线LON
LON_LONMA - LON龙系长线LONMA
LON_LONT - LON龙系长线LONT
SHT_SHT - SHT龙系短线SHT
SHT_SHTMA - SHT龙系短线SHTMA
CDP_STD_CDP - CDP_STD逆势操作CDP
CDP_STD_AH - CDP_STD逆势操作AH
CDP_STD_NH - CDP_STD逆势操作NH
CDP_STD_NL - CDP_STD逆势操作NL
CDP_STD_AL - CDP_STD逆势操作AL
"""
print("\n" + "=" * 60)
print("📈 获取因子数据")
print("=" * 60)
# 获取基础因子数据
result = client.get_stock_factor_data(
stock='513100.SH', # 纳指ETF
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,high,low,volume,amount'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
# 获取涨跌幅因子数据
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='159915.SZ', # 创业板ETF
start_date='20220101',
end_date='20241231',
columns='date,证券代码,5日涨跌幅,10日涨跌幅,20日涨跌幅,60日涨跌幅'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
# 获取技术指标因子数据
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,MACD_DIF,MACD_DEA,MACD_MACD,KDJ_K,KDJ_D,KDJ_J,RSI1,RSI2,RSI3,BOLL_BOLL,BOLL_UB,BOLL_LB'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（技术指标）")
print(df.head())
# 获取均线系统因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,5日均线,10日均线,20日均线,30日均线,60日均线,120日均线'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（均线系统）")
print(df.head())
# 获取Alpha因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,Alpha001,Alpha002,Alpha003,Alpha004,Alpha005'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（Alpha因子）")
print(df.head())
# 获取动量因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,3日回归动量,5日回归动量,10日回归动量,20日回归动量,30日回归动量,60日回归动量'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（动量因子）")
print(df.head())
6读取ETF基金因子数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例3：获取因子数据
# ============================================================
"""
参数说明：
stock: str = '600031.SH' - 股票代码
start_date: str = '20200101' - 开始日期，格式YYYYMMDD
end_date: str = '20261231' - 结束日期，格式YYYYMMDD
columns: str = 'date,close,open,high,low,volume,amount' - 选择字段，逗号分隔
【基础因子字段】
date - 交易日期
证券代码 - 股票代码
close - 收盘价
open - 开盘价
high - 最高价
low - 最低价
volume - 成交量
amount - 成交金额
zdf - 涨跌幅
【涨跌幅因子】
5日涨跌幅 - 5日涨跌幅
10日涨跌幅 - 10日涨跌幅
20日涨跌幅 - 20日涨跌幅
30日涨跌幅 - 30日涨跌幅
60日涨跌幅 - 60日涨跌幅
120日涨跌幅 - 120日涨跌幅
250日涨跌幅 - 250日涨跌幅
【价格距离均线涨跌幅】
价格距离5日均线涨跌幅 - 价格距离5日均线涨跌幅
价格距离10日均线涨跌幅 - 价格距离10日均线涨跌幅
价格距离20日均线涨跌幅 - 价格距离20日均线涨跌幅
价格距离30日均线涨跌幅 - 价格距离30日均线涨跌幅
价格距离60日均线涨跌幅 - 价格距离60日均线涨跌幅
价格距离120日均线涨跌幅 - 价格距离120日均线涨跌幅
【均线距离涨跌幅】
5日均线距离10日均线涨跌幅 - 5日均线距离10日均线涨跌幅
10日均线距离20日均线涨跌幅 - 10日均线距离20日均线涨跌幅
20日均线距离30日均线涨跌幅 - 20日均线距离30日均线涨跌幅
30日均线距离60日均线涨跌幅 - 30日均线距离60日均线涨跌幅
60日均线距离120日均线涨跌幅 - 60日均线距离120日均线涨跌幅
【移动平均线】
5日均线 - 5日均线
10日均线 - 10日均线
20日均线 - 20日均线
30日均线 - 30日均线
60日均线 - 60日均线
120日均线 - 120日均线
【均线交叉信号】
5日10日金叉 - 5日10日均线金叉
10日20日金叉 - 10日20日均线金叉
20日30日金叉 - 20日30日均线金叉
30日60日金叉 - 30日60日均线金叉
60日120日金叉 - 60日120日均线金叉
5日10日死叉 - 5日10日均线死叉
10日20日死叉 - 10日20日均线死叉
20日30日死叉 - 20日30日均线死叉
30日60日死叉 - 30日60日均线死叉
60日120日死叉 - 60日120日均线死叉
【价格位置判断】
价格在5均线上 - 价格是否在5日均线上
价格在10均线上 - 价格是否在10日均线上
价格在20均线上 - 价格是否在20日均线上
价格在30均线上 - 价格是否在30日均线上
价格在60均线上 - 价格是否在60日均线上
价格在120均线上 - 价格是否在120日均线上
5均线在10均线上 - 5日均线是否在10日均线上
10均线在20均线上 - 10日均线是否在20日均线上
20均线在30均线上 - 20日均线是否在30日均线上
30均线在60均线上 - 30日均线是否在60日均线上
60均线在120均线上 - 60日均线是否在120日均线上
【技术指标 - KDJ】
KDJ_K - KDJ指标K值
KDJ_D - KDJ指标D值
KDJ_J - KDJ指标J值
KDJ_KD金叉 - KDJ金叉信号
KDJ_KD死叉 - KDJ死叉信号
【技术指标 - MACD】
MACD_DIF - MACD平滑异同平均线DIF
MACD_DEA - MACD平滑异同平均线DEA
MACD_MACD - MACD平滑异同平均线MACD
MACD_金叉 - MACD金叉信号
MACD_死叉 - MACD死叉信号
【技术指标 - RSI】
RSI1 - RSI相对强弱RSI1
RSI2 - RSI相对强弱RSI2
RSI3 - RSI相对强弱RSI3
RSI_金叉 - RSI金叉信号
RSI_死叉 - RSI死叉信号
【技术指标 - BOLL布林线】
BOLL_BOLL - BOLL布林线中轨
BOLL_UB - BOLL布林线上轨
BOLL_LB - BOLL布林线下轨
【技术指标 - CCI】
CCI - CCI商品路径指标
【技术指标 - MFI】
MFI - MFI资金流量指标
【技术指标 - MTM】
MTM_MTM - MTM动量线MTM值
MTM_MTMMA - MTM动量线MTMMA值
【技术指标 - SKDJ】
SKDJ_K - SKDJ慢速随机K值
SKDJ_D - SKDJ慢速随机D值
【技术指标 - WR】
WR1 - WR威廉指标WR1
WR2 - WR威廉指标WR2
WR_金叉 - WR金叉信号
WR_死叉 - WR死叉信号
【技术指标 - PSY】
PSY_PSY - PSY心理线PSY
PSY_PSYMA - PSY心理线PSYMA
PSY_金叉 - PSY金叉信号
PSY_死叉 - PSY死叉信号
【技术指标 - BIAS乖离率】
BIAS1 - BIAS乖离率BIAS1
BIAS2 - BIAS乖离率BIAS2
BIAS3 - BIAS乖离率BIAS3
BIAS_QL_BIAS - BIAS_QL乖离率传统版BIAS值
BIAS_QL_BIASMA - BIAS_QL乖离率传统版BIASMA值
BIAS36_BIAS36 - BIAS36三六乖离BIAS36
BIAS36_BIAS612 - BIAS36三六乖离BIAS612
BIAS36_MABIAS - BIAS36三六乖离MABIAS
【技术指标 - DMI】
DMI_PDI - DMI趋向指标PDI
DMI_MDI - DMI趋向指标MDI
DMI_ADX - DMI趋向指标ADX
DMI_ADXR - DMI趋向指标ADXR
【技术指标 - DMA】
DMA_XT_DIF - DMA_XT平均差DIF
DMA_XT_DIFMA - DMA_XT平均差DIFMA
【技术指标 - DPO】
DPO_DPO - DPO区间震荡线DPO
DPO_MADPO - DPO区间震荡线MADPO
【技术指标 - EMV】
EMV_EMV - EMV简易波动指标EMV
EMV_MAEMV - EMV简易波动指标MAEMV
【技术指标 - TRIX】
TRIX_TRIX - TRIX三重指数平均线TRIX
TRIX_MATRIX - TRIX三重指数平均线MATRIX
【技术指标 - UOS】
UOS_UOS - UOS终极指标UOS
UOS_MAUOS - UOS终极指标MAUOS
【技术指标 - VPT】
VTP_VPT - VPT量价曲线VPT
VTP_MAVP - VPT量价曲线MAVP
【技术指标 - WVAD】
WVAD_WVAD - WVAD威廉变异离散量WVAD
WVAD_MAWVAD - WVAD威廉变异离散量MAWVAD
【技术指标 - BRAR】
BRAR_BR - BRAR情绪指标BR
BRAR_AR - BRAR情绪指标AR
【技术指标 - CR】
CR_CR - CR带状能量线CR
CR_MA1 - CR带状能量线MA1
CR_MA2 - CR带状能量线MA2
CR_MA3 - CR带状能量线MA3
CR_MA4 - CR带状能量线MA4
【技术指标 - MASS】
MASS_MASS - MASS梅斯线MASS
MASS_MAMASS - MASS梅斯线MAMASS
【技术指标 - VR】
VR_VR - VR成交量变异率VR
VR_MAVR - VR成交量变异率MAVR
【技术指标 - OBV】
OBV_OBV - OBV累积能量线OBV
OBV_MAOBV - OBV累积能量线MAOBV
【技术指标 - VOL成交量】
VOL_XT_MAVOL1 - VOL成交量MAVOL1
VOL_XT_MAVOL2 - VOL成交量MAVOL2
【技术指标 - VRSI】
VRSI1 - VRSI相对强弱量RSI1
VRSI2 - VRSI相对强弱量RSI2
VRSI3 - VRSI相对强弱量RSI3
【技术指标 - HSL换手线】
HSL_HSL - HSL换手线HSL
HSL_MAHSL - HSL换手线MAHSL
【技术指标 - ACD】
ACD_ACD - ACD升降线ACD
ACD_MAACD - ACD升降线MAACD
【技术指标 - BBI】
BBI - BBI多空均线
【技术指标 - EXPMA】
EXPMA_EXP1 - EXPMA指数平均线EXP1
EXPMA_EXP2 - EXPMA指数平均线EXP2
【技术指标 - SAR】
SAR - SAR抛物线指标
【技术指标 - AMO成交金额】
AMO_AMOW - AMO成交金额AMOW
AMO_AMO1 - AMO成交金额AMO1
AMO_AMO2 - AMO成交金额AMO2
【技术指标 - MIKE】
MIKE_STOR - MIKE麦克支撑压力STOR
MIKE_MIDR - MIKE麦克支撑压力MIDR
MIKE_WEKR - MIKE麦克支撑压力WEKR
MIKE_WEKS - MIKE麦克支撑压力WEKS
MIKE_MIDS - MIKE麦克支撑压力MIDS
MIKE_STOS - MIKE麦克支撑压力STOS
【技术指标 - ENE】
ENE_UPPER - ENE轨道线上轨
ENE_LOWER - ENE轨道线下轨
ENE_ENE - ENE轨道线ENE
【技术指标 - PBX瀑布线】
PBX_PBX1 - PBX瀑布线PBX1
PBX_PBX2 - PBX瀑布线PBX2
PBX_PBX3 - PBX瀑布线PBX3
PBX_PBX4 - PBX瀑布线PBX4
PBX_PBX5 - PBX瀑布线PBX5
PBX_PBX6 - PBX瀑布线PBX6
【技术指标 - XS薛斯通道】
XS_SUP - XS薛斯通道SUP
XS_SDN - XS薛斯通道SDN
XS_LUP - XS薛斯通道LUP
XS_LDN - XS薛斯通道LDN
【技术指标 - TQN唐奇安通道】
TQN_周期高点 - TQN唐奇安通道周期高点
TQN_周期低点 - TQN唐奇安通道周期低点
TQN_平空开多 - TQN唐奇安通道平空开多信号
TQN_平多开空 - TQN唐奇安通道平多开空信号
【技术指标 - ALLIGAT鳄鱼线】
ALLIGAT_上唇 - ALLIGAT鳄鱼线上唇
ALLIGAT_牙齿 - ALLIGAT鳄鱼线牙齿
ALLIGAT_下颚 - ALLIGAT鳄鱼线下颚
【技术指标 - GMMA顾比均线】
GMMA_MA3 - GMMA顾比均线MA3
GMMA_MA5 - GMMA顾比均线MA5
GMMA_MA8 - GMMA顾比均线MA8
GMMA_MA10 - GMMA顾比均线MA10
GMMA_MA12 - GMMA顾比均线MA12
GMMA_MA15 - GMMA顾比均线MA15
GMMA_MA30 - GMMA顾比均线MA30
GMMA_MA35 - GMMA顾比均线MA35
GMMA_MA40 - GMMA顾比均线MA40
GMMA_MA45 - GMMA顾比均线MA45
GMMA_MA50 - GMMA顾比均线MA50
GMMA_MA60 - GMMA顾比均线MA60
【技术指标 - VMACD】
VMACD_DIF - VMACD量平滑异同平均线DIF
VMACD_DEA - VMACD量平滑异同平均线DEA
VMACD_MACD - VMACD量平滑异同平均线MACD
【技术指标 - SMACD】
SMACD_DEA - SMACD单线平滑异同平均线DEA
SMACD_MACD - SMACD单线平滑异同平均线MACD
【技术指标 - QACD】
QACD_DIF - QACD快速异同平均线DIF
QACD_MACD - QACD快速异同平均线MACD
QACD_DDIF - QACD快速异同平均线DDIF
【技术指标 - 成交量相关】
连续上涨天数 - 连续上涨天数
连续下跌天数 - 连续下跌天数
【技术指标 - 偏度峰度】
5日偏度 - 5日偏度
10日偏度 - 10日偏度
20日偏度 - 20日偏度
30日偏度 - 30日偏度
60日偏度 - 60日偏度
120日偏度 - 120日偏度
5日峰度 - 5日峰度
10日峰度 - 10日峰度
20日峰度 - 20日峰度
30日峰度 - 30日峰度
60日峰度 - 60日峰度
120日峰度 - 120日峰度
【Alpha因子 - 世界金融实验室101因子】
Alpha001 至 Alpha191 - 世界金融实验室101因子（共191个）
【交易信号因子】
六脉神剑 - 六脉神剑交易信号
小波段交易 - 小波段交易信号
大波段交易 - 大波段交易信号
波段超级买卖 - 波段超级买卖信号
【回归分析因子】
3日回归动量 - 3日回归动量
5日回归动量 - 5日回归动量
7日回归动量 - 7日回归动量
9日回归动量 - 9日回归动量
12日回归动量 - 12日回归动量
15日回归动量 - 15日回归动量
18日回归动量 - 18日回归动量
20日回归动量 - 20日回归动量
23日回归动量 - 23日回归动量
25日回归动量 - 25日回归动量
28日回归动量 - 28日回归动量
30日回归动量 - 30日回归动量
35日回归动量 - 35日回归动量
40日回归动量 - 40日回归动量
45日回归动量 - 45日回归动量
50日回归动量 - 50日回归动量
60日回归动量 - 60日回归动量
【回归斜率】
5日回归斜率 - 5日回归斜率
10日回归斜率 - 10日回归斜率
20日回归斜率 - 20日回归斜率
30日回归斜率 - 30日回归斜率
60日回归斜率 - 60日回归斜率
120日回归斜率 - 120日回归斜率
【标准差】
5日标准差 - 5日标准差
10日标准差 - 10日标准差
20日标准差 - 20日标准差
30日标准差 - 30日标准差
60日标准差 - 60日标准差
120日标准差 - 120日标准差
【最高最低值周期】
5日最高值到当前周期 - 5日最高值到当前周期
10日最高值到当前周期 - 10日最高值到当前周期
20日最高值到当前周期 - 20日最高值到当前周期
30日最高值到当前周期 - 30日最高值到当前周期
60日最高值到当前周期 - 60日最高值到当前周期
120日最高值到当前周期 - 120日最高值到当前周期
5日最低值到当前周期 - 5日最低值到当前周期
10日最低值到当前周期 - 10日最低值到当前周期
20日最低值到当前周期 - 20日最低值到当前周期
30日最低值到当前周期 - 30日最低值到当前周期
60日最低值到当前周期 - 60日最低值到当前周期
120日最低值到当前周期 - 120日最低值到当前周期
【Alpha系数】
5日Alpha - 5日Alpha
10日Alpha - 10日Alpha
20日Alpha - 20日Alpha
30日Alpha - 30日Alpha
60日Alpha - 60日Alpha
120日Alpha - 120日Alpha
【Beta系数】
5日Beta - 5日Beta
10日Beta - 10日Beta
20日Beta - 20日Beta
30日Beta - 30日Beta
60日Beta - 60日Beta
120日Beta - 120日Beta
【夏普比率】
5日夏普比率 - 5日夏普比率
10日夏普比率 - 10日夏普比率
20日夏普比率 - 20日夏普比率
30日夏普比率 - 30日夏普比率
60日夏普比率 - 60日夏普比率
120日夏普比率 - 120日夏普比率
【年化波动率】
5日年化波动率 - 5日年化波动率
10日年化波动率 - 10日年化波动率
20日年化波动率 - 20日年化波动率
30日年化波动率 - 30日年化波动率
60日年化波动率 - 60日年化波动率
120日年化波动率 - 120日年化波动率
【最大回撤】
5日最大回撤 - 5日最大回撤
10日最大回撤 - 10日最大回撤
20日最大回撤 - 20日最大回撤
30日最大回撤 - 30日最大回撤
60日最大回撤 - 60日最大回撤
120日最大回撤 - 120日最大回撤
【上涨/下跌捕获率】
5日上涨捕获率 - 5日上涨捕获率
10日上涨捕获率 - 10日上涨捕获率
20日上涨捕获率 - 20日上涨捕获率
30日上涨捕获率 - 30日上涨捕获率
60日上涨捕获率 - 60日上涨捕获率
120日上涨捕获率 - 120日上涨捕获率
5日下跌捕获率 - 5日下跌捕获率
10日下跌捕获率 - 10日下跌捕获率
20日下跌捕获率 - 20日下跌捕获率
30日下跌捕获率 - 30日下跌捕获率
60日下跌捕获率 - 60日下跌捕获率
120日下跌捕获率 - 120日下跌捕获率
【庄家/主力指标】
ZJTJ_无庄控盘 - ZJTJ庄家抬轿无庄控盘
ZJTJ_开始控盘 - ZJTJ庄家抬轿开始控盘
ZJTJ_有庄控盘 - ZJTJ庄家抬轿有庄控盘
ZJTJ_主力出货 - ZJTJ庄家抬轿主力出货
CYW - CYW主力控盘
ZLJC_JCS - ZLJC主力进出JCS
ZLJC_JCM - ZLJC主力进出JCM
ZLJC_JCL - ZLJC主力进出JCL
ZLMM_MMS - ZLMM主力买卖MMS
ZLMM_MMM - ZLMM主力买卖MMM
ZLMM_MML - ZLMM主力买卖MML
LHXJ_主力弃盘 - LHXJ猎狐先觉主力弃盘
LHXJ_主力控盘 - LHXJ猎狐先觉主力控盘
LYJH_机构做空能量线 - LYJH猎鹰歼狐机构做空能量线
LYJH_机构做多能量线 - LYJH猎鹰歼狐机构做多能量线
【智能交易信号】
BDZX_AK - BDZX波段之星AK
BDZX_AD1 - BDZX波段之星AD1
BDZX_AJ - BDZX波段之星AJ
BDZX_买进 - BDZX波段之星买进信号
BDZX_卖出 - BDZX波段之星卖出信号
CYHT_SK - CYHT财运亨通SK
CYHT_SD - CYHT财运亨通SD
CYHT_卖出 - CYHT财运亨通卖出信号
CYHT_买进 - CYHT财运亨通买进信号
BSQJ_B买 - BSQJ买卖区间B买信号
BSQJ_持仓 - BSQJ买卖区间持仓信号
BSQJ_S卖 - BSQJ买卖区间S卖信号
BSQJ_空仓 - BSQJ买卖区间空仓信号
JFZX_多头力量 - JFZX飓风智能中线多头力量
JFZX_空头力量 - JFZX飓风智能中线空头力量
XJDX_J - XJDX超级短线J
XJDX_D - XJDX超级短线D
XJDX_K - XJDX超级短线K
【其他特色指标】
CYS - CYS市场盈亏
CYR_CYR - CYR市场强弱CYR
CYR_MACYR - CYR市场强弱MACYR
CYE_CYEL - CYE市场趋势CYEL
CYE_CYES - CYE市场趋势CYES
CYS - CYS市场盈亏
RAD_RADER1 - RAD威力雷达RADER1
RAD_RADERMA - RAD威力雷达RADERMA
SG_XDT_QR - SG_XDT心电图QR
SG_XDT_MQR1 - SG_XDT心电图MQR1
SG_XDT_MQR2 - SG_XDT心电图MQR2
SG_NDB_DK - SG_NDB脑电波DK
SG_NDB_MDK1 - SG_NDB脑电波MDK1
SG_NDB_MDK2 - SG_NDB脑电波MDK2
SG_SMX_ZY1 - SG_SMX生命线ZY1
SG_SMX_ZY2 - SG_SMX生命线ZY2
SG_SMX_ZY3 - SG_SMX生命线ZY3
SG_LB_量比 - SG_LB量比
SG_LB_MA5 - SG_LB量比MA5
SG_LB_MA10 - SG_LB量比MA10
SG_PF - SG_PF强势股评分
SLZT_白龙 - SLZT神龙在天白龙
SLZT_黄龙 - SLZT神龙在天黄龙
SLZT_紫龙 - SLZT神龙在天紫龙
SLZT_青龙 - SLZT神龙在天青龙
SLZT_红龙 - SLZT神龙在天红龙
SLZT_蓝龙 - SLZT神龙在天蓝龙
ADVOL_ADVOL - ADVOL龙系离散量ADVOL
ADVOL_MA1 - ADVOL龙系离散量MA1
ADVOL_MA2 - ADVOL龙系离散量MA2
JAX_J - JAX济安线J
JAX_A - JAX济安线A
JAX_X - JAX济安线X
LON_LON - LON龙系长线LON
LON_LONMA - LON龙系长线LONMA
LON_LONT - LON龙系长线LONT
SHT_SHT - SHT龙系短线SHT
SHT_SHTMA - SHT龙系短线SHTMA
CDP_STD_CDP - CDP_STD逆势操作CDP
CDP_STD_AH - CDP_STD逆势操作AH
CDP_STD_NH - CDP_STD逆势操作NH
CDP_STD_NL - CDP_STD逆势操作NL
CDP_STD_AL - CDP_STD逆势操作AL
"""
print("\n" + "=" * 60)
print("📈 获取因子数据")
print("=" * 60)
# 获取基础因子数据
result = client.get_stock_factor_data(
stock='513100.SH', # 纳指ETF
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,high,low,volume,amount'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
# 获取涨跌幅因子数据
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='159915.SZ', # 创业板ETF
start_date='20220101',
end_date='20241231',
columns='date,证券代码,5日涨跌幅,10日涨跌幅,20日涨跌幅,60日涨跌幅'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
# 获取技术指标因子数据
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,MACD_DIF,MACD_DEA,MACD_MACD,KDJ_K,KDJ_D,KDJ_J,RSI1,RSI2,RSI3,BOLL_BOLL,BOLL_UB,BOLL_LB'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（技术指标）")
print(df.head())
# 获取均线系统因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,5日均线,10日均线,20日均线,30日均线,60日均线,120日均线'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（均线系统）")
print(df.head())
# 获取Alpha因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,Alpha001,Alpha002,Alpha003,Alpha004,Alpha005'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（Alpha因子）")
print(df.head())
# 获取动量因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,3日回归动量,5日回归动量,10日回归动量,20日回归动量,30日回归动量,60日回归动量'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（动量因子）")
print(df.head())
7读取可转债因子数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例3：获取因子数据
# ============================================================
"""
参数说明：
stock: str = '600031.SH' - 股票代码
start_date: str = '20200101' - 开始日期，格式YYYYMMDD
end_date: str = '20261231' - 结束日期，格式YYYYMMDD
columns: str = 'date,close,open,high,low,volume,amount' - 选择字段，逗号分隔
【基础因子字段】
date - 交易日期
证券代码 - 股票代码
close - 收盘价
open - 开盘价
high - 最高价
low - 最低价
volume - 成交量
amount - 成交金额
zdf - 涨跌幅
【涨跌幅因子】
5日涨跌幅 - 5日涨跌幅
10日涨跌幅 - 10日涨跌幅
20日涨跌幅 - 20日涨跌幅
30日涨跌幅 - 30日涨跌幅
60日涨跌幅 - 60日涨跌幅
120日涨跌幅 - 120日涨跌幅
250日涨跌幅 - 250日涨跌幅
【价格距离均线涨跌幅】
价格距离5日均线涨跌幅 - 价格距离5日均线涨跌幅
价格距离10日均线涨跌幅 - 价格距离10日均线涨跌幅
价格距离20日均线涨跌幅 - 价格距离20日均线涨跌幅
价格距离30日均线涨跌幅 - 价格距离30日均线涨跌幅
价格距离60日均线涨跌幅 - 价格距离60日均线涨跌幅
价格距离120日均线涨跌幅 - 价格距离120日均线涨跌幅
【均线距离涨跌幅】
5日均线距离10日均线涨跌幅 - 5日均线距离10日均线涨跌幅
10日均线距离20日均线涨跌幅 - 10日均线距离20日均线涨跌幅
20日均线距离30日均线涨跌幅 - 20日均线距离30日均线涨跌幅
30日均线距离60日均线涨跌幅 - 30日均线距离60日均线涨跌幅
60日均线距离120日均线涨跌幅 - 60日均线距离120日均线涨跌幅
【移动平均线】
5日均线 - 5日均线
10日均线 - 10日均线
20日均线 - 20日均线
30日均线 - 30日均线
60日均线 - 60日均线
120日均线 - 120日均线
【均线交叉信号】
5日10日金叉 - 5日10日均线金叉
10日20日金叉 - 10日20日均线金叉
20日30日金叉 - 20日30日均线金叉
30日60日金叉 - 30日60日均线金叉
60日120日金叉 - 60日120日均线金叉
5日10日死叉 - 5日10日均线死叉
10日20日死叉 - 10日20日均线死叉
20日30日死叉 - 20日30日均线死叉
30日60日死叉 - 30日60日均线死叉
60日120日死叉 - 60日120日均线死叉
【价格位置判断】
价格在5均线上 - 价格是否在5日均线上
价格在10均线上 - 价格是否在10日均线上
价格在20均线上 - 价格是否在20日均线上
价格在30均线上 - 价格是否在30日均线上
价格在60均线上 - 价格是否在60日均线上
价格在120均线上 - 价格是否在120日均线上
5均线在10均线上 - 5日均线是否在10日均线上
10均线在20均线上 - 10日均线是否在20日均线上
20均线在30均线上 - 20日均线是否在30日均线上
30均线在60均线上 - 30日均线是否在60日均线上
60均线在120均线上 - 60日均线是否在120日均线上
【技术指标 - KDJ】
KDJ_K - KDJ指标K值
KDJ_D - KDJ指标D值
KDJ_J - KDJ指标J值
KDJ_KD金叉 - KDJ金叉信号
KDJ_KD死叉 - KDJ死叉信号
【技术指标 - MACD】
MACD_DIF - MACD平滑异同平均线DIF
MACD_DEA - MACD平滑异同平均线DEA
MACD_MACD - MACD平滑异同平均线MACD
MACD_金叉 - MACD金叉信号
MACD_死叉 - MACD死叉信号
【技术指标 - RSI】
RSI1 - RSI相对强弱RSI1
RSI2 - RSI相对强弱RSI2
RSI3 - RSI相对强弱RSI3
RSI_金叉 - RSI金叉信号
RSI_死叉 - RSI死叉信号
【技术指标 - BOLL布林线】
BOLL_BOLL - BOLL布林线中轨
BOLL_UB - BOLL布林线上轨
BOLL_LB - BOLL布林线下轨
【技术指标 - CCI】
CCI - CCI商品路径指标
【技术指标 - MFI】
MFI - MFI资金流量指标
【技术指标 - MTM】
MTM_MTM - MTM动量线MTM值
MTM_MTMMA - MTM动量线MTMMA值
【技术指标 - SKDJ】
SKDJ_K - SKDJ慢速随机K值
SKDJ_D - SKDJ慢速随机D值
【技术指标 - WR】
WR1 - WR威廉指标WR1
WR2 - WR威廉指标WR2
WR_金叉 - WR金叉信号
WR_死叉 - WR死叉信号
【技术指标 - PSY】
PSY_PSY - PSY心理线PSY
PSY_PSYMA - PSY心理线PSYMA
PSY_金叉 - PSY金叉信号
PSY_死叉 - PSY死叉信号
【技术指标 - BIAS乖离率】
BIAS1 - BIAS乖离率BIAS1
BIAS2 - BIAS乖离率BIAS2
BIAS3 - BIAS乖离率BIAS3
BIAS_QL_BIAS - BIAS_QL乖离率传统版BIAS值
BIAS_QL_BIASMA - BIAS_QL乖离率传统版BIASMA值
BIAS36_BIAS36 - BIAS36三六乖离BIAS36
BIAS36_BIAS612 - BIAS36三六乖离BIAS612
BIAS36_MABIAS - BIAS36三六乖离MABIAS
【技术指标 - DMI】
DMI_PDI - DMI趋向指标PDI
DMI_MDI - DMI趋向指标MDI
DMI_ADX - DMI趋向指标ADX
DMI_ADXR - DMI趋向指标ADXR
【技术指标 - DMA】
DMA_XT_DIF - DMA_XT平均差DIF
DMA_XT_DIFMA - DMA_XT平均差DIFMA
【技术指标 - DPO】
DPO_DPO - DPO区间震荡线DPO
DPO_MADPO - DPO区间震荡线MADPO
【技术指标 - EMV】
EMV_EMV - EMV简易波动指标EMV
EMV_MAEMV - EMV简易波动指标MAEMV
【技术指标 - TRIX】
TRIX_TRIX - TRIX三重指数平均线TRIX
TRIX_MATRIX - TRIX三重指数平均线MATRIX
【技术指标 - UOS】
UOS_UOS - UOS终极指标UOS
UOS_MAUOS - UOS终极指标MAUOS
【技术指标 - VPT】
VTP_VPT - VPT量价曲线VPT
VTP_MAVP - VPT量价曲线MAVP
【技术指标 - WVAD】
WVAD_WVAD - WVAD威廉变异离散量WVAD
WVAD_MAWVAD - WVAD威廉变异离散量MAWVAD
【技术指标 - BRAR】
BRAR_BR - BRAR情绪指标BR
BRAR_AR - BRAR情绪指标AR
【技术指标 - CR】
CR_CR - CR带状能量线CR
CR_MA1 - CR带状能量线MA1
CR_MA2 - CR带状能量线MA2
CR_MA3 - CR带状能量线MA3
CR_MA4 - CR带状能量线MA4
【技术指标 - MASS】
MASS_MASS - MASS梅斯线MASS
MASS_MAMASS - MASS梅斯线MAMASS
【技术指标 - VR】
VR_VR - VR成交量变异率VR
VR_MAVR - VR成交量变异率MAVR
【技术指标 - OBV】
OBV_OBV - OBV累积能量线OBV
OBV_MAOBV - OBV累积能量线MAOBV
【技术指标 - VOL成交量】
VOL_XT_MAVOL1 - VOL成交量MAVOL1
VOL_XT_MAVOL2 - VOL成交量MAVOL2
【技术指标 - VRSI】
VRSI1 - VRSI相对强弱量RSI1
VRSI2 - VRSI相对强弱量RSI2
VRSI3 - VRSI相对强弱量RSI3
【技术指标 - HSL换手线】
HSL_HSL - HSL换手线HSL
HSL_MAHSL - HSL换手线MAHSL
【技术指标 - ACD】
ACD_ACD - ACD升降线ACD
ACD_MAACD - ACD升降线MAACD
【技术指标 - BBI】
BBI - BBI多空均线
【技术指标 - EXPMA】
EXPMA_EXP1 - EXPMA指数平均线EXP1
EXPMA_EXP2 - EXPMA指数平均线EXP2
【技术指标 - SAR】
SAR - SAR抛物线指标
【技术指标 - AMO成交金额】
AMO_AMOW - AMO成交金额AMOW
AMO_AMO1 - AMO成交金额AMO1
AMO_AMO2 - AMO成交金额AMO2
【技术指标 - MIKE】
MIKE_STOR - MIKE麦克支撑压力STOR
MIKE_MIDR - MIKE麦克支撑压力MIDR
MIKE_WEKR - MIKE麦克支撑压力WEKR
MIKE_WEKS - MIKE麦克支撑压力WEKS
MIKE_MIDS - MIKE麦克支撑压力MIDS
MIKE_STOS - MIKE麦克支撑压力STOS
【技术指标 - ENE】
ENE_UPPER - ENE轨道线上轨
ENE_LOWER - ENE轨道线下轨
ENE_ENE - ENE轨道线ENE
【技术指标 - PBX瀑布线】
PBX_PBX1 - PBX瀑布线PBX1
PBX_PBX2 - PBX瀑布线PBX2
PBX_PBX3 - PBX瀑布线PBX3
PBX_PBX4 - PBX瀑布线PBX4
PBX_PBX5 - PBX瀑布线PBX5
PBX_PBX6 - PBX瀑布线PBX6
【技术指标 - XS薛斯通道】
XS_SUP - XS薛斯通道SUP
XS_SDN - XS薛斯通道SDN
XS_LUP - XS薛斯通道LUP
XS_LDN - XS薛斯通道LDN
【技术指标 - TQN唐奇安通道】
TQN_周期高点 - TQN唐奇安通道周期高点
TQN_周期低点 - TQN唐奇安通道周期低点
TQN_平空开多 - TQN唐奇安通道平空开多信号
TQN_平多开空 - TQN唐奇安通道平多开空信号
【技术指标 - ALLIGAT鳄鱼线】
ALLIGAT_上唇 - ALLIGAT鳄鱼线上唇
ALLIGAT_牙齿 - ALLIGAT鳄鱼线牙齿
ALLIGAT_下颚 - ALLIGAT鳄鱼线下颚
【技术指标 - GMMA顾比均线】
GMMA_MA3 - GMMA顾比均线MA3
GMMA_MA5 - GMMA顾比均线MA5
GMMA_MA8 - GMMA顾比均线MA8
GMMA_MA10 - GMMA顾比均线MA10
GMMA_MA12 - GMMA顾比均线MA12
GMMA_MA15 - GMMA顾比均线MA15
GMMA_MA30 - GMMA顾比均线MA30
GMMA_MA35 - GMMA顾比均线MA35
GMMA_MA40 - GMMA顾比均线MA40
GMMA_MA45 - GMMA顾比均线MA45
GMMA_MA50 - GMMA顾比均线MA50
GMMA_MA60 - GMMA顾比均线MA60
【技术指标 - VMACD】
VMACD_DIF - VMACD量平滑异同平均线DIF
VMACD_DEA - VMACD量平滑异同平均线DEA
VMACD_MACD - VMACD量平滑异同平均线MACD
【技术指标 - SMACD】
SMACD_DEA - SMACD单线平滑异同平均线DEA
SMACD_MACD - SMACD单线平滑异同平均线MACD
【技术指标 - QACD】
QACD_DIF - QACD快速异同平均线DIF
QACD_MACD - QACD快速异同平均线MACD
QACD_DDIF - QACD快速异同平均线DDIF
【技术指标 - 成交量相关】
连续上涨天数 - 连续上涨天数
连续下跌天数 - 连续下跌天数
【技术指标 - 偏度峰度】
5日偏度 - 5日偏度
10日偏度 - 10日偏度
20日偏度 - 20日偏度
30日偏度 - 30日偏度
60日偏度 - 60日偏度
120日偏度 - 120日偏度
5日峰度 - 5日峰度
10日峰度 - 10日峰度
20日峰度 - 20日峰度
30日峰度 - 30日峰度
60日峰度 - 60日峰度
120日峰度 - 120日峰度
【Alpha因子 - 世界金融实验室101因子】
Alpha001 至 Alpha191 - 世界金融实验室101因子（共191个）
【交易信号因子】
六脉神剑 - 六脉神剑交易信号
小波段交易 - 小波段交易信号
大波段交易 - 大波段交易信号
波段超级买卖 - 波段超级买卖信号
【回归分析因子】
3日回归动量 - 3日回归动量
5日回归动量 - 5日回归动量
7日回归动量 - 7日回归动量
9日回归动量 - 9日回归动量
12日回归动量 - 12日回归动量
15日回归动量 - 15日回归动量
18日回归动量 - 18日回归动量
20日回归动量 - 20日回归动量
23日回归动量 - 23日回归动量
25日回归动量 - 25日回归动量
28日回归动量 - 28日回归动量
30日回归动量 - 30日回归动量
35日回归动量 - 35日回归动量
40日回归动量 - 40日回归动量
45日回归动量 - 45日回归动量
50日回归动量 - 50日回归动量
60日回归动量 - 60日回归动量
【回归斜率】
5日回归斜率 - 5日回归斜率
10日回归斜率 - 10日回归斜率
20日回归斜率 - 20日回归斜率
30日回归斜率 - 30日回归斜率
60日回归斜率 - 60日回归斜率
120日回归斜率 - 120日回归斜率
【标准差】
5日标准差 - 5日标准差
10日标准差 - 10日标准差
20日标准差 - 20日标准差
30日标准差 - 30日标准差
60日标准差 - 60日标准差
120日标准差 - 120日标准差
【最高最低值周期】
5日最高值到当前周期 - 5日最高值到当前周期
10日最高值到当前周期 - 10日最高值到当前周期
20日最高值到当前周期 - 20日最高值到当前周期
30日最高值到当前周期 - 30日最高值到当前周期
60日最高值到当前周期 - 60日最高值到当前周期
120日最高值到当前周期 - 120日最高值到当前周期
5日最低值到当前周期 - 5日最低值到当前周期
10日最低值到当前周期 - 10日最低值到当前周期
20日最低值到当前周期 - 20日最低值到当前周期
30日最低值到当前周期 - 30日最低值到当前周期
60日最低值到当前周期 - 60日最低值到当前周期
120日最低值到当前周期 - 120日最低值到当前周期
【Alpha系数】
5日Alpha - 5日Alpha
10日Alpha - 10日Alpha
20日Alpha - 20日Alpha
30日Alpha - 30日Alpha
60日Alpha - 60日Alpha
120日Alpha - 120日Alpha
【Beta系数】
5日Beta - 5日Beta
10日Beta - 10日Beta
20日Beta - 20日Beta
30日Beta - 30日Beta
60日Beta - 60日Beta
120日Beta - 120日Beta
【夏普比率】
5日夏普比率 - 5日夏普比率
10日夏普比率 - 10日夏普比率
20日夏普比率 - 20日夏普比率
30日夏普比率 - 30日夏普比率
60日夏普比率 - 60日夏普比率
120日夏普比率 - 120日夏普比率
【年化波动率】
5日年化波动率 - 5日年化波动率
10日年化波动率 - 10日年化波动率
20日年化波动率 - 20日年化波动率
30日年化波动率 - 30日年化波动率
60日年化波动率 - 60日年化波动率
120日年化波动率 - 120日年化波动率
【最大回撤】
5日最大回撤 - 5日最大回撤
10日最大回撤 - 10日最大回撤
20日最大回撤 - 20日最大回撤
30日最大回撤 - 30日最大回撤
60日最大回撤 - 60日最大回撤
120日最大回撤 - 120日最大回撤
【上涨/下跌捕获率】
5日上涨捕获率 - 5日上涨捕获率
10日上涨捕获率 - 10日上涨捕获率
20日上涨捕获率 - 20日上涨捕获率
30日上涨捕获率 - 30日上涨捕获率
60日上涨捕获率 - 60日上涨捕获率
120日上涨捕获率 - 120日上涨捕获率
5日下跌捕获率 - 5日下跌捕获率
10日下跌捕获率 - 10日下跌捕获率
20日下跌捕获率 - 20日下跌捕获率
30日下跌捕获率 - 30日下跌捕获率
60日下跌捕获率 - 60日下跌捕获率
120日下跌捕获率 - 120日下跌捕获率
【庄家/主力指标】
ZJTJ_无庄控盘 - ZJTJ庄家抬轿无庄控盘
ZJTJ_开始控盘 - ZJTJ庄家抬轿开始控盘
ZJTJ_有庄控盘 - ZJTJ庄家抬轿有庄控盘
ZJTJ_主力出货 - ZJTJ庄家抬轿主力出货
CYW - CYW主力控盘
ZLJC_JCS - ZLJC主力进出JCS
ZLJC_JCM - ZLJC主力进出JCM
ZLJC_JCL - ZLJC主力进出JCL
ZLMM_MMS - ZLMM主力买卖MMS
ZLMM_MMM - ZLMM主力买卖MMM
ZLMM_MML - ZLMM主力买卖MML
LHXJ_主力弃盘 - LHXJ猎狐先觉主力弃盘
LHXJ_主力控盘 - LHXJ猎狐先觉主力控盘
LYJH_机构做空能量线 - LYJH猎鹰歼狐机构做空能量线
LYJH_机构做多能量线 - LYJH猎鹰歼狐机构做多能量线
【智能交易信号】
BDZX_AK - BDZX波段之星AK
BDZX_AD1 - BDZX波段之星AD1
BDZX_AJ - BDZX波段之星AJ
BDZX_买进 - BDZX波段之星买进信号
BDZX_卖出 - BDZX波段之星卖出信号
CYHT_SK - CYHT财运亨通SK
CYHT_SD - CYHT财运亨通SD
CYHT_卖出 - CYHT财运亨通卖出信号
CYHT_买进 - CYHT财运亨通买进信号
BSQJ_B买 - BSQJ买卖区间B买信号
BSQJ_持仓 - BSQJ买卖区间持仓信号
BSQJ_S卖 - BSQJ买卖区间S卖信号
BSQJ_空仓 - BSQJ买卖区间空仓信号
JFZX_多头力量 - JFZX飓风智能中线多头力量
JFZX_空头力量 - JFZX飓风智能中线空头力量
XJDX_J - XJDX超级短线J
XJDX_D - XJDX超级短线D
XJDX_K - XJDX超级短线K
【其他特色指标】
CYS - CYS市场盈亏
CYR_CYR - CYR市场强弱CYR
CYR_MACYR - CYR市场强弱MACYR
CYE_CYEL - CYE市场趋势CYEL
CYE_CYES - CYE市场趋势CYES
CYS - CYS市场盈亏
RAD_RADER1 - RAD威力雷达RADER1
RAD_RADERMA - RAD威力雷达RADERMA
SG_XDT_QR - SG_XDT心电图QR
SG_XDT_MQR1 - SG_XDT心电图MQR1
SG_XDT_MQR2 - SG_XDT心电图MQR2
SG_NDB_DK - SG_NDB脑电波DK
SG_NDB_MDK1 - SG_NDB脑电波MDK1
SG_NDB_MDK2 - SG_NDB脑电波MDK2
SG_SMX_ZY1 - SG_SMX生命线ZY1
SG_SMX_ZY2 - SG_SMX生命线ZY2
SG_SMX_ZY3 - SG_SMX生命线ZY3
SG_LB_量比 - SG_LB量比
SG_LB_MA5 - SG_LB量比MA5
SG_LB_MA10 - SG_LB量比MA10
SG_PF - SG_PF强势股评分
SLZT_白龙 - SLZT神龙在天白龙
SLZT_黄龙 - SLZT神龙在天黄龙
SLZT_紫龙 - SLZT神龙在天紫龙
SLZT_青龙 - SLZT神龙在天青龙
SLZT_红龙 - SLZT神龙在天红龙
SLZT_蓝龙 - SLZT神龙在天蓝龙
ADVOL_ADVOL - ADVOL龙系离散量ADVOL
ADVOL_MA1 - ADVOL龙系离散量MA1
ADVOL_MA2 - ADVOL龙系离散量MA2
JAX_J - JAX济安线J
JAX_A - JAX济安线A
JAX_X - JAX济安线X
LON_LON - LON龙系长线LON
LON_LONMA - LON龙系长线LONMA
LON_LONT - LON龙系长线LONT
SHT_SHT - SHT龙系短线SHT
SHT_SHTMA - SHT龙系短线SHTMA
CDP_STD_CDP - CDP_STD逆势操作CDP
CDP_STD_AH - CDP_STD逆势操作AH
CDP_STD_NH - CDP_STD逆势操作NH
CDP_STD_NL - CDP_STD逆势操作NL
CDP_STD_AL - CDP_STD逆势操作AL
"""
print("\n" + "=" * 60)
print("📈 获取因子数据")
print("=" * 60)
# 获取基础因子数据
result = client.get_stock_factor_data(
stock='513100.SH', # 纳指ETF
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,high,low,volume,amount'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
# 获取涨跌幅因子数据
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='159915.SZ', # 创业板ETF
start_date='20220101',
end_date='20241231',
columns='date,证券代码,5日涨跌幅,10日涨跌幅,20日涨跌幅,60日涨跌幅'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
# 获取技术指标因子数据
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,MACD_DIF,MACD_DEA,MACD_MACD,KDJ_K,KDJ_D,KDJ_J,RSI1,RSI2,RSI3,BOLL_BOLL,BOLL_UB,BOLL_LB'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（技术指标）")
print(df.head())
# 获取均线系统因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,5日均线,10日均线,20日均线,30日均线,60日均线,120日均线'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（均线系统）")
print(df.head())
# 获取Alpha因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='128137.SZ',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,Alpha001,Alpha002,Alpha003,Alpha004,Alpha005'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（Alpha因子）")
print(df.head())
# 获取动量因子
print("\n" + "-" * 40)
result = client.get_stock_factor_data(
stock='513100.SH',
start_date='20240101',
end_date='20500101',
columns='date,证券代码,close,3日回归动量,5日回归动量,10日回归动量,20日回归动量,30日回归动量,60日回归动量'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据（动量因子）")
print(df.head())
8. 获取财务数据 获取资产负债表
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例4：获取资产负债表（全部字段）
# ============================================================
"""
参数说明：
table: str = '资产负债表' - 财务表类型
date: str = '2026-06-30' - 报告日期，格式YYYY-MM-DD
columns: str = 'secu_code,end_date,total_assets' - 选择字段，逗号分隔
财务表类型：
'资产负债表' - 资产负债表
'利润表' - 利润表
'现金流量表' - 现金流量表
'估值数据' - 估值数据
'成长能力' - 成长能力指标
'盈利能力' - 盈利能力指标
'每股指标' - 每股指标
'营运能力' - 营运能力指标
'偿债能力' - 偿债能力指标
【资产负债表 - balance_statement 全部字段】
secu_code - 股票代码
secu_abbr - 股票简称
company_type - 公司类型
end_date - 截止日期
publ_date - 公告日期
settlement_provi - 结算备付金
client_provi - 客户备付金
deposit_in_interbank - 存放同业款项
r_metal - 贵金属
lend_capital - 拆出资金
derivative_assets - 衍生金融资产
bought_sellback_assets - 买入返售金融资产
loan_and_advance - 发放贷款和垫款
insurance_receivables - 应收保费
receivable_subrogation_fee - 应收代位追偿款
reinsurance_receivables - 应收分保账款
receivable_unearned_r - 应收分保未到期责任准备金
receivable_claims_r - 应收分保未决赔款准备金
receivable_life_r - 应收分保寿险责任准备金
receivable_lt_health_r - 应收分保长期健康险责任准备金
insurer_impawn_loan - 保户质押贷款
fixed_deposit - 定期存款
refundable_capital_deposit - 存出资本保证金
refundable_deposit - 存出保证金
independence_account_assets - 独立账户资产
other_assets - 其他资产
borrowing_from_centralbank - 向中央银行借款
deposit_of_interbank - 同业及其他金融机构存放款项
borrowing_capital - 拆入资金
derivative_liability - 衍生金融负债
sold_buyback_secu_proceeds - 卖出回购金融资产款
deposit - 吸收存款
proxy_secu_proceeds - 代理买卖证券款
sub_issue_secu_proceeds - 代理承销证券款
deposits_received - 存入保证金
advance_insurance - 预收保费
commission_payable - 应付手续费及佣金
reinsurance_payables - 应付分保账款
compensation_payable - 应付赔付款
policy_dividend_payable - 应付保单红利
insurer_deposit_investment - 保户储金及投资款
unearned_premium_reserve - 未到期责任准备金
outstanding_claim_reserve - 未决赔款准备金
life_insurance_reserve - 寿险责任准备金
lt_health_insurance_lr - 长期健康险责任准备金
independence_liability - 独立账户负债
other_liability - 其他负债
cash_equivalents - 货币资金
client_deposit - 客户资金存款
trading_assets - 交易性金融资产
bill_receivable - 应收票据
dividend_receivable - 应收股利
interest_receivable - 应收利息
account_receivable - 应收账款
other_receivable - 其他应收款
advance_payment - 预付款项
inventories - 存货
non_current_asset_in_one_year - 一年内到期的非流动资产
other_current_assets - 其他流动资产
total_current_assets - 流动资产合计
shortterm_loan - 短期借款
impawned_loan - 质押借款
trading_liability - 交易性金融负债
notes_payable - 应付票据
accounts_payable - 应付账款
advance_receipts - 预收款项
salaries_payable - 应付职工薪酬
dividend_payable - 应付股利
taxs_payable - 应交税费
interest_payable - 应付利息
other_payable - 其他应付款
non_current_liability_in_one_year - 一年内到期的非流动负债
other_current_liability - 其他流动负债
total_current_liability - 流动负债合计
hold_for_sale_assets - 可供出售金融资产
hold_to_maturity_investments - 持有至到期投资
investment_property - 投资性房地产
longterm_equity_invest - 长期股权投资
longterm_receivable_account - 长期应收款
fixed_assets - 固定资产
construction_materials - 工程物资
constru_in_process - 在建工程
fixed_assets_liquidation - 固定资产清理
biological_assets - 生产性生物资产
oil_gas_assets - 油气资产
intangible_assets - 无形资产
seat_costs - 交易席位费
development_expenditure - 开发支出
good_will - 商誉
long_deferred_expense - 长期待摊费用
deferred_tax_assets - 递延所得税资产
other_non_current_assets - 其他非流动资产
total_non_current_assets - 非流动资产合计
longterm_loan - 长期借款
bonds_payable - 应付债券
longterm_account_payable - 长期应付款
long_salaries_pay - 长期应付职工薪酬
specific_account_payable - 专项应付款
estimate_liability - 预计负债
deferred_tax_liability - 递延所得税负债
long_defer_income - 长期递延收益
other_non_current_liability - 其他非流动负债
total_non_current_liability - 非流动负债合计
paidin_capital - 实收资本（或股本）
other_equityinstruments - 其他权益工具
capital_reserve_fund - 资本公积
surplus_reserve_fund - 盈余公积
retained_profit - 未分配利润
treasury_stock - 减：库存股
other_composite_income - 其他综合收益
ordinary_risk_reserve_fund - 一般风险准备
foreign_currency_report_conv_diff - 外币报表折算差额
specific_reserves - 专项储备
se_without_mi - 归属母公司股东权益合计
minority_interests - 少数股东权益
total_shareholder_equity - 所有者权益合计
total_liability_and_equity - 负债和权益总计
total_assets - 资产总计
total_liability - 负债总计
contract_liability - 合同负债
total_fixed_asset - 固定资产合计
t_constru_in_process - 在建工程合计
"""
print("\n" + "=" * 60)
print("💰 获取资产负债表")
print("=" * 60)
result = client.get_stock_finance_data(
table='资产负债表',
date='2024-06-30',
columns='secu_code,secu_abbr,end_date,total_assets,total_liability,total_shareholder_equity'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
9. 获取利润表
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例5：获取利润表（全部字段）
# ============================================================
"""
【利润表 - income_statement 全部字段】
secu_code - 股票代码
secu_abbr - 股票简称
company_type - 公司类型
end_date - 截止日期
publ_date - 公告日期
basic_eps - 基本每股收益
diluted_eps - 稀释每股收益
net_profit - 净利润
np_parent_company_owners - 归属于母公司所有者的净利润
minority_profit - 少数股东损益
total_operating_cost - 营业总成本
operating_payout - 营业支出
refunded_premiums - 退保金
compensation_expense - 赔付支出
amortization_expense - 减:摊回赔付支出
premium_reserve - 提取保险责任准备金
amortization_premium_reserve - 减:摊回保险责任准备金
policy_dividend_payout - 保单红利支出
reinsurance_cost - 分保费用
amortization_reinsurance_cost - 减:摊回分保费用
insurance_commission_expense - 保险手续费及佣金支出
other_operating_cost - 其他营业成本
operating_cost - 营业成本
operating_tax_surcharges - 营业税金及附加
operating_expense - 销售费用
administration_expense - 管理费用
financial_expense - 财务费用
asset_impairment_loss - 资产减值损失
operating_profit - 营业利润
non_operating_income - 加：营业收入
non_operating_expense - 减：营业外支出
non_current_assetss_deal_loss - 其中：非流动资产处置净损失
total_operating_revenue - 营业总收入
operating_revenue - 营业收入
net_interest_income - 利息净收入
interest_income - 其中：利息收入
interest_expense - 其中:利息支出
net_commission_income - 手续费及佣金净收入
commission_income - 其中：手续费及佣金收入
commission_expense - 其中：手续费及佣金支出
net_proxy_secu_income - 其中：代理买卖证券业务净收入
net_subissue_secu_income - 其中：证券承销业务净收入
net_trust_income - 其中:受托客户资产管理业务净收入
premiums_earned - 已赚保费
premiums_income - 保险业务收入
reinsurance_income - 其中：分保费收入
reinsurance - 减：分出保费
unearned_premium_reserve - 提取未到期责任准备金
other_operating_revenue - 其他营业收入
other_net_revenue - 非营业性收入
fair_value_change_income - 公允价值变动净收益
invest_income - 投资净收益
invest_income_associates - 其中:对联营合营企业的投资收益
exchange_income - 汇兑收益
total_profit - 利润总额
income_tax_cost - 减：所得税费用
total_composite_income - 综合收益总额
ci_parent_company_owners - 归属于母公司所有者的综合收益总额
ci_minority_owners - 归属于少数股东的综合收益总额
r_and_d - 研发费用
"""
print("\n" + "=" * 60)
print("💰 获取利润表")
print("=" * 60)
result = client.get_stock_finance_data(
table='利润表',
date='2024-06-30',
columns='secu_code,secu_abbr,total_operating_revenue,operating_cost,net_profit,basic_eps'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
10. 现金流量表
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例6：获取现金流量表（全部字段）
# ============================================================
"""
【现金流量表 - cashflow_statement 全部字段】
secu_code - 股票代码
secu_abbr - 股票简称
company_type - 公司类型
end_date - 截止日期
publ_date - 公告日期
goods_sale_service_render_cash - 销售商品、提供劳务收到的现金
tax_levy_refund - 收到的税费返还
net_deposit_increase - 客户存款和同业存放款项净增加额
net_borrowing_from_central_bank - 向中央银行借款净增加额
net_borrowing_from_finance_co - 向其他金融机构拆入资金净增加额
interest_and_commission_cashin - 收取利息、手续费及佣金的现金
net_deal_trading_assets - 处置交易性金融资产净增加额
net_buyback - 回购业务资金净增加额
net_original_insurance_cash - 收到原保险合同保费取得的现金
net_reinsurance_cash - 收到再保业务现金净额
net_insurer_deposit_investment - 保户储金及投资款净增加额
other_cashin_related_operate - 收到其他与经营活动有关的现金
subtotal_operate_cash_inflow - 经营活动现金流入小计
goods_and_services_cash_paid - 购买商品、接受劳务支付的现金
staff_behalf_paid - 支付给职工以及为职工支付的现金
all_taxes_paid - 支付的各项税费
net_loan_and_advance_increase - 客户贷款及垫款净增加额
net_deposit_in_cb_and_ib - 存放中央银行和同业款项净增加额
net_lend_capital - 拆出资金净增加额
commission_cash_paid - 支付手续费及佣金的现金
original_compensation_paid - 支付原保险合同赔付款项的现金
net_cash_for_reinsurance - 支付再保业务现金净额
policy_dividend_cash_paid - 支付保单红利的现金
other_operate_cash_paid - 支付其他与经营活动有关的现金
subtotal_operate_cash_outflow - 经营活动现金流出小计
net_operate_cash_flow - 经营活动产生的现金流量净额
invest_withdrawal_cash - 收回投资收到的现金
invest_proceeds - 取得投资收益收到的现金
fix_intan_other_asset_dispo_cash - 处置固定资产、无形资产和其他长期资产收回的现金净额
net_cash_deal_sub_company - 处置子公司及其他营业单位收到的现金净额
other_cash_from_invest_act - 收到其他与投资活动有关的现金
subtotal_invest_cash_inflow - 投资活动现金流入小计
fix_intan_other_asset_acqui_cash - 购建固定资产、无形资产和其他长期资产支付的现金
invest_cash_paid - 投资支付的现金
net_cash_from_sub_company - 取得子公司及其他营业单位支付的现金净额
impawned_loan_net_increase - 质押贷款净增加额
other_cash_to_invest_act - 支付其他与投资活动有关的现金
subtotal_invest_cash_outflow - 投资活动现金流出小计
net_invest_cash_flow - 投资活动产生的现金流量净额
cash_from_invest - 吸收投资收到的现金
cash_from_bonds_issue - 发行债券收到的现金
cash_from_borrowing - 取得借款收到的现金
other_finance_act_cash - 收到其他与筹资活动有关的现金
subtotal_finance_cash_inflow - 筹资活动现金流入小计
borrowing_repayment - 偿还债务支付的现金
dividend_interest_payment - 分配股利、利润或偿付利息支付的现金
other_finance_act_payment - 支付其他与筹资活动有关的现金
subtotal_finance_cash_outflow - 筹资活动现金流出小计
net_finance_cash_flow - 筹资活动产生的现金流量净额
exchan_rate_change_effect - 汇率变动对现金及现金等价物的影响
cash_equivalent_increase - 现金及现金等价物净增加额
begin_period_cash - 加：期初现金及现金等价物余额
end_period_cash_equivalent - 期末现金及现金等价物余额
net_profit - 净利润
minority_profit - 加:少数股东损益
assets_depreciation_reserves - 加:资产减值准备
fixed_asset_depreciation - 固定资产折旧
intangible_asset_amortization - 收无形资产摊销
deferred_expense_amort - 长期待摊费用摊销
deferred_expense_decreased - 待摊费用减少(减:增加)
accrued_expense_added - 预提费用增加(减:减少)
fix_intanther_asset_dispo_loss - 处置固定资产、无形资产和其他长期资产的损失
fixed_asset_scrap_loss - 固定资产报废损失
loss_from_fair_value_changes - 公允价值变动损失
financial_expense - 财务费用
invest_loss - 投资损失
defered_tax_asset_decrease - 递延所得税资产减少
defered_tax_liability_increase - 递延所得税负债增加
inventory_decrease - 存货的减少
operate_receivable_decrease - 经营性应收项目的减少
operate_payable_increase - 经营性应付项目的增加
others - 其他
net_operate_cash_flow_notes - 经营活动产生的现金流量净额
debt_to_captical - 债务转为资本
cbs_expiring_within_one_year - 一年内到期的可转换公司债券
fixed_assets_finance_leases - 融资租入固定资产
cash_at_end_of_year - 现金的期末余额
cash_at_beginning_of_year - 减:现金的期初余额
cash_equivalents_at_end_of_year - 加:现金等价物的期末余额
cash_equivalents_at_beginning - 减:现金等价物的期初余额
net_incr_in_cash_and_equivalents - 现金及现金等价物净增加额
"""
print("\n" + "=" * 60)
print("💰 获取现金流量表")
print("=" * 60)
result = client.get_stock_finance_data(
table='现金流量表',
date='2024-06-30',
columns='secu_code,secu_abbr,net_operate_cash_flow,net_invest_cash_flow,net_finance_cash_flow'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
11. 获取估值数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例7：获取估值数据（全部字段）
# ============================================================
"""
【估值数据 - valuation 全部字段】
trading_day - 交易日期（固定返回）
total_value - A股总市值(元)（固定返回）
float_value - A股流通市值(元)（自选返回）
naps - 每股净资产/(元/股)（自选返回）
pcf - 市现率（自选返回）
secu_abbr - 证券简称（自选返回）
secu_code - 证券代码（固定返回）
ps - 市销率PS（自选返回）
ps_ttm - 市销率PS(TTM)（自选返回）
pe_ttm - 市盈率PE(TTM)（自选返回）
a_shares - A股股本（自选返回）
a_floats - 可流通A股（自选返回）
pe_dynamic - 动态市盈率（自选返回）
pe_static - 静态市盈率（自选返回）
b_floats - 可流通B股（自选返回）
b_shares - B股股本（自选返回）
h_shares - H股股本（自选返回）
total_shares - 总股本（自选返回）
turnover_rate - 换手率（自选返回）
dividend_ratio - 滚动股息率（自选返回）
pb - 市净率（自选返回）
roe - 净资产收益率（自选返回）
"""
print("\n" + "=" * 60)
print("💰 获取估值数据")
print("=" * 60)
result = client.get_stock_finance_data(
table='估值数据',
date='2024-06-30',
columns='secu_code,secu_abbr,pe_ttm,pb,total_value,roe,turnover_rate'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
12. 获取成长能力数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例8：获取成长能力数据（全部字段）
# ============================================================
"""
【成长能力 - growth_ability 全部字段】
secu_code - 股票代码（固定返回）
secu_abbr - 股票简称（固定返回）
publ_date - 公告日期（固定返回）
end_date - 截止日期（固定返回）
basic_eps_yoy - 基本每股收益同比增长（%）
diluted_eps_yoy - 稀释每股收益同比增长（%）
operating_revenue_grow_rate - 营业收入同比增长（%）
np_parent_company_yoy - 归属母公司股东的净利润同比增长（%）
net_operate_cash_flow_yoy - 经营活动产生的现金流量净额同比增长（%）
oper_profit_grow_rate - 营业利润同比增长（%）
total_profit_grow_rate - 利润总额同比增长（%）
eps_grow_rate_ytd - 每股净资产相对年初增长率（%）
se_without_mi_grow_rate_ytd - 归属母公司股东的权益相对年初增长率（%）
ta_grow_rate_ytd - 资产总计相对年初增长率（%)
np_parent_company_cut_yoy - 归属母公司股东的净利润(扣除)同比增长（%）
avg_np_yoy_past_five_year - 过去五年同期归属母公司净利润平均增幅（%）
oper_cash_ps_grow_rate - 每股经营活动产生的现金流量净额同比增长（%）
naor_yoy - 净资产收益率(摊薄)同比增（%）
net_asset_grow_rate - 净资产同比增长（%）
total_asset_grow_rate - 总资产同比增长（%）
sustainable_grow_rate - 可持续增长率（%）
net_profit_grow_rate - 净利润同比增长（%）
"""
print("\n" + "=" * 60)
print("📈 获取成长能力数据")
print("=" * 60)
result = client.get_stock_finance_data(
table='成长能力',
date='2024-06-30',
columns='secu_code,secu_abbr,operating_revenue_grow_rate,np_parent_company_yoy,oper_profit_grow_rate'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
13. 盈利能力
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例9：获取盈利能力数据（全部字段）
# ============================================================
"""
【盈利能力 - profit_ability 全部字段】
secu_code - 股票代码（固定返回）
secu_abbr - 股票简称（固定返回）
publ_date - 公告日期（固定返回）
end_date - 截止日期（固定返回）
roe_avg - 净资产收益率%平均计算值（%）
roe_weighted - 净资产收益率%加权公布值（%）
roe - 净资产收益率%摊薄公布值（%）
roe_cut - 净资产收益率%扣除摊薄（%）
roe_cut_weighted - 净资产收益率%扣除加权（%）
roe_ttm - 净资产收益率_TTM（%）
roa_ebit - 总资产报酬率（%）
roa_ebit_ttm - 总资产报酬率_TTM（%）
roa - 总资产净利率（%）
roa_ttm - 总资产净利率_TTM（%）
roic - 投入资本回报率（%）
net_profit_ratio - 销售净利率（%）
net_profit_ratio_ttm - 销售净利率_TTM（%）
gross_income_ratio - 销售毛利率（%）
gross_income_ratio_ttm - 销售毛利率_TTM（%）
sales_cost_ratio - 销售成本率（%）
period_costs_rate - 销售期间费用率（%）
period_costs_rate_ttm - 销售期间的费用率_TTM（%）
np_to_tor - 净利润／营业总收入（%）
np_to_tor_ttm - 净利润／营业总收入_TTM（%）
operating_profit_to_tor - 营业利润／营业总收入（%）
operating_profit_to_tor_ttm - 营业利润／营业总收入_TTM（%）
ebit_to_tor - 息税前利润／营业总收入（%）
ebit_to_tor_ttm - 息税前利润／营业总收入_TTM（%）
t_operating_cost_to_tor - 营业总成本／营业总收入（%）
t_operating_cost_to_tor_ttm - 营业总成本／营业总收入_TTM（%）
operating_expense_rate - 销售费用／营业总收入（%）
operating_expense_rate_ttm - 销售费用／营业总收入_TTM（%）
admini_expense_rate - 管理费用／营业总收入（%）
admini_expense_rate_ttm - 管理费用／营业总收入_TTM（%）
financial_expense_rate - 财务费用／营业总收入（%）
financial_expense_rate_ttm - 财务费用／营业总收入_TTM（%）
asset_impa_loss_to_tor - 资产减值损失／营业总收入（%）
asset_impa_loss_to_tor_ttm - 资产减值损失／营业总收入_TTM（%）
net_profit - 归属母公司净利润（元）
net_profit_cut - 扣除非经常性损益后的净利润（元）
ebit - 息税前利润（元）
ebitda - 息税折旧摊销前利润（元）
operating_profit_ratio - 营业利润率（%）
total_profit_cost_ratio - 成本费用利润率
"""
print("\n" + "=" * 60)
print("📈 获取盈利能力数据")
print("=" * 60)
result = client.get_stock_finance_data(
table='盈利能力',
date='2024-06-30',
columns='secu_code,secu_abbr,roe,gross_income_ratio,net_profit_ratio'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
14. 每股指标
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例10：获取每股指标数据（全部字段）
# ============================================================
"""
【每股指标 - eps 全部字段】
secu_code - 股票代码（固定返回）
secu_abbr - 股票简称（固定返回）
publ_date - 公告日期（固定返回）
end_date - 截止日期（固定返回）
basic_eps - 基本每股收益（元/股）
diluted_eps - 稀释每股收益（元/股）
eps - 每股收益_期末股本摊薄（元/股）
eps_ttm - 每股收益_TTM（元/股）
naps - 每股净资产（元/股）
total_operating_revenue_ps - 每股营业总收入（元/股）
main_income_ps - 每股营业收入（元/股）
operating_revenue_ps_ttm - 每股营业收入_TTM（元/股）
oper_profit_ps - 每股营业利润（元/股）
ebitps - 每股息税前利润（元/股）
capital_surplus_fund_ps - 每股资本公积金（元/股）
surplus_reserve_fund_ps - 每股盈余公积（元/股）
accumulation_fund_ps - 每股公积金（元/股）
undivided_profit - 每股未分配利润（元/股）
retained_earnings_ps - 每股留存收益（元/股）
net_operate_cash_flow_ps - 每股经营活动产生的现金流量净额（元/股）
net_operate_cash_flow_ps_ttm - 每股经营活动产生的现金流量净额_TTM（元/股）
cash_flow_ps - 每股现金流量净额（元/股）
cash_flow_ps_ttm - 每股现金流量净额_TTM（元/股）
enterprise_fcf_ps - 每股企业自由现金流量（元/股）
shareholder_fcf_ps - 每股股东自由现金流量（元/股）
"""
print("\n" + "=" * 60)
print("📈 获取每股指标数据")
print("=" * 60)
result = client.get_stock_finance_data(
table='每股指标',
date='2024-06-30',
columns='secu_code,secu_abbr,basic_eps,diluted_eps,naps'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
15. 获取营运能力数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例11：获取营运能力数据（全部字段）
# ============================================================
"""
【营运能力 - operating_ability 全部字段】
secu_code - 股票代码（固定返回）
secu_abbr - 股票简称（固定返回）
publ_date - 公告日期（固定返回）
end_date - 截止日期（固定返回）
oper_cycle - 营业周期（天/次）
inventory_turnover_rate - 存货周转率（次）
inventory_turnover_days - 存货周转天数（天/次）
accounts_receivables_turnover_rate - 应收账款周转率（次）
accounts_receivables_turnover_days - 应收账款周转天数（天/次）
accounts_payables_turnover_rate - 应付账款周转率（次）
accounts_payables_turnover_days - 应付账款周转天数（天/次）
current_assets_turnover_rate - 流动资产周转率（次）
fixed_asset_turnover_rate - 固定资产周转率（次）
equity_turnover_rate - 股东权益周转率（次）
total_asset_turnover_rate - 总资产周转率（次）
"""
print("\n" + "=" * 60)
print("📈 获取营运能力数据")
print("=" * 60)
result = client.get_stock_finance_data(
table='营运能力',
date='2024-06-30',
columns='secu_code,secu_abbr,inventory_turnover_rate,accounts_receivables_turnover_rate,total_asset_turnover_rate'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
16. 获取偿债能力数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例12：获取偿债能力数据（全部字段）
# ============================================================
"""
【偿债能力 - debt_paying_ability 全部字段】
secu_code - 股票代码（固定返回）
secu_abbr - 股票简称（固定返回）
publ_date - 公告日期（固定返回）
end_date - 截止日期（固定返回）
current_ratio - 流动比率
quick_ratio - 速动比率
super_quick_ratio - 超速动比率
debt_equity_ratio - 产权比率（%）
sewmi_to_total_liability - 归属母公司股东的权益／负债合计（%）
sewmi_to_interest_bear_debt - 归属母公司股东的权益／带息债务（%）
debt_tangible_equity_ratio - 有形净值债务率（%）
tangible_a_to_interest_bear_debt - 有形净值／带息债务（%）
tangible_a_to_net_debt - 有形净值／净债务（%）
ebitda_to_t_liability - 息税折旧摊销前利润／负债合计
nocf_to_t_liability - 经营活动产生现金流量净额/负债合计
nocf_to_interest_bear_debt - 经营活动产生现金流量净额/带息债务
nocf_to_current_liability - 经营活动产生现金流量净额/流动负债
nocf_to_net_debt - 经营活动产生现金流量净额/净债务
interest_cover - 利息保障倍数（倍）
long_debt_to_working_capital - 长期负债与营运资金比率
opercashinto_current_debt - 现金流动负债比
"""
print("\n" + "=" * 60)
print("📈 获取偿债能力数据")
print("=" * 60)
result = client.get_stock_finance_data(
table='偿债能力',
date='2024-06-30',
columns='secu_code,secu_abbr,current_ratio,quick_ratio,debt_equity_ratio'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
17. 读取模拟交易统计数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例13：读取模拟交易统计数据
# ============================================================
"""
参数说明：
st_type: str = '动量策略' - 策略类型
可选值：'定投策略'、'动量策略'、'资产配置策略'、
'资产配置平衡策略'、'网格策略'、'海龟策略'、
'综合动量策略'、'条件因子策略'、'排序多因子策略'
st_name: str = '小果动量模拟策略' - 策略名称
"""
print("\n" + "=" * 60)
print("📊 读取模拟交易统计数据")
print("=" * 60)
result = client.get_moni_trader_data(
st_type='动量策略',
st_name='小果动量模拟策略'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
18. 读取社区交易统计数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例14：读取社区交易统计数据
# ============================================================
"""
参数说明：
st_type: str = '动量策略' - 策略类型
可选值：'定投策略'、'动量策略'、'资产配置策略'、
'资产配置平衡策略'、'网格策略'、'海龟策略'、
'综合动量策略'、'条件因子策略'、'排序多因子策略'
st_name: str = '小果动量模拟策略' - 策略名称
"""
print("\n" + "=" * 60)
print("📊 读取社区交易统计数据")
print("=" * 60)
result = client.get_moni_trader_data_sq(
st_type='动量策略',
st_name='小果动量模拟策略'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条数据")
print(df.head())
三、回测接口
1. 定投回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例15：定投回测
# ============================================================
"""
参数说明：
start_date: str = '20260701' - 回测开始日期，格式YYYYMMDD
end_date: str = '20500101' - 回测结束日期，格式YYYYMMDD
stock_list: str = '513100.SH,513500.SH' - 股票列表，逗号分隔
index_stock: str = '000300.SH' - 基准指数代码
cash: float = 100000 - 初始资金
dt_interval: int = 20 - 定投间隔（交易日）
dt_type: str = '金额' - 定投类型：'金额'、'份额'、'百分比'
dt_value: float = 1000 - 定投金额/份额/百分比值
sell_zdf: float = 0.03 - 止盈涨幅阈值（如0.03表示3%）
buy_zdf: float = -0.03 - 补仓跌幅阈值（如-0.03表示-3%）
trade_value: float = 1000 - 每次交易金额
comm: float = 0.0001 - 佣金费率（如0.0001表示万分之一）
"""
print("\n" + "=" * 60)
print("📊 定投回测")
print("=" * 60)
result = client.xg_dt_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
dt_interval=20,
dt_type='金额',
dt_value=1000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("定投回测结果：")
print(result)
2. 动量回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例16：动量回测
# ============================================================
"""
参数说明：
start_date: str = '20260101' - 回测开始日期
end_date: str = '20500101' - 回测结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
comm: float = 0.0001 - 佣金费率
mom_type: str = '百分比' - 动量类型：'百分比'或'金额'
mom_value: float = 1 - 动量值（百分比或金额）
mom_daily: int = 25 - 动量计算周期（交易日）
min_mom: float = 0 - 最小动量阈值，低于此值不买入
max_mom: float = 5 - 最大动量阈值，高于此值不买入
buy_rank: int = 1 - 买入排名，1表示买排名第1的股票
sell_zdf: float = 0.03 - 止盈涨幅
sell_amount: float = 1000 - 卖出金额
"""
print("\n" + "=" * 60)
print("📊 动量回测")
print("=" * 60)
result = client.xg_mom_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
mom_type='百分比',
mom_value=1,
mom_daily=25,
min_mom=0,
max_mom=5,
buy_rank=1,
sell_zdf=0.03,
sell_amount=1000
)
print("动量回测结果：")
print(result)
3. 资产配置回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例17：资产配置回测
# ============================================================
"""
参数说明：
start_date: str = '20260101' - 回测开始日期
end_date: str = '20500101' - 回测结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
dt_type: str = '百分比' - 配置类型：'百分比'、'金额'
weight_list: str = '0.4,0.4,0.2' - 权重配置，与股票列表一一对应
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
sell_zdf: float = 0.03 - 止盈涨幅
buy_zdf: float = -0.03 - 补仓跌幅
trade_value: float = 1000 - 交易金额
comm: float = 0.0001 - 佣金费率
"""
print("\n" + "=" * 60)
print("📊 资产配置回测")
print("=" * 60)
result = client.xg_pz_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
dt_type='百分比',
weight_list='0.4,0.4,0.2',
index_stock='000300.SH',
cash=100000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("资产配置回测结果：")
print(result)
4. 资产配置平衡回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例18：资产配置平衡回测
# ============================================================
"""
参数说明：
start_date: str = '20260101' - 回测开始日期
end_date: str = '20500101' - 回测结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
dt_type: str = '百分比' - 配置类型
weight_list: str = '0.35,0.35,0.3' - 目标权重
deviation_list: str = '0.1,0.1,0.05' - 偏离容忍度，与股票一一对应
interval: int = 20 - 再平衡间隔（交易日）
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
sell_zdf: float = 0.03 - 止盈涨幅
buy_zdf: float = -0.03 - 补仓跌幅
trade_value: float = 1000 - 交易金额
comm: float = 0.0001 - 佣金费率
"""
print("\n" + "=" * 60)
print("📊 资产配置平衡回测")
print("=" * 60)
result = client.xg_zcph_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
dt_type='百分比',
weight_list='0.35,0.35,0.3',
deviation_list='0.1,0.1,0.05',
interval=20,
index_stock='000300.SH',
cash=100000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("资产配置平衡回测结果：")
print(result)
5. 网格策略回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例19：网格策略回测
# ============================================================
"""
参数说明：
start_date: str = '20250701' - 回测开始日期
end_date: str = '20500101' - 回测结束日期
stock_list: str = '513100.SH,513500.SH' - 股票列表
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
gd_interval: int = 1 - 网格间隔
gd_bc_type_list: str = '百分比,百分比' - 网格类型
gd_buy_bc_list: str = '0.03,0.02' - 买入阈值
gd_sell_bc_list: str = '-0.02,-0.015' - 卖出阈值
gd_atr_ratio_list: str = '2.0,2.0' - ATR比例
gd_type_list: str = '金额,金额' - 交易类型
gd_value_list: str = '1000,1500' - 交易金额
init_position_ratio_list: str = '0.1,0.15' - 初始仓位
sell_zdf: float = 0.03 - 止盈涨幅
buy_zdf: float = -0.03 - 补仓跌幅
trade_value: float = 1000 - 交易金额
comm: float = 0.0001 - 佣金费率
"""
print("\n" + "=" * 60)
print("📊 网格策略回测")
print("=" * 60)
result = client.xg_gd_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
gd_interval=1,
gd_bc_type_list='百分比,百分比',
gd_buy_bc_list='0.03,0.02',
gd_sell_bc_list='-0.02,-0.015',
gd_atr_ratio_list='2.0,2.0',
gd_type_list='金额,金额',
gd_value_list='1000,1500',
init_position_ratio_list='0.1,0.15',
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("网格策略回测结果：")
print(result)
6. 海龟策略回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例20：海龟策略回测
# ============================================================
"""
参数说明：
start_date: str = '20240101' - 回测开始日期
end_date: str = '20500101' - 回测结束日期
stock_list: str = '513100.SH,513500.SH' - 股票列表
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
comm: float = 0.0001 - 佣金费率
max_workers: int = 4 - 最大进程数
entry_period: int = 20 - 入场周期
exit_period: int = 10 - 离场周期
n_period: int = 20 - N值计算周期
risk_per_trade: float = 0.01 - 单笔风险
risk_per_unit: float = 0.02 - 单位风险
max_units: int = 4 - 最大单位
add_unit_threshold: float = 0.5 - 加仓阈值
sell_zdf: float = 0.03 - 止盈涨幅
buy_zdf: float = -0.03 - 补仓跌幅
trade_value: float = 1000 - 交易金额
"""
print("\n" + "=" * 60)
print("📊 海龟策略回测")
print("=" * 60)
result = client.xg_hg_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
entry_period=20,
exit_period=10,
n_period=20,
risk_per_trade=0.01,
risk_per_unit=0.02,
max_units=4,
add_unit_threshold=0.5,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000
)
print("海龟策略回测结果：")
print(result)
7. 综合动量回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例21：综合动量回测
# ============================================================
"""
参数说明：
start_date: str = '20250101' - 回测开始日期
end_date: str = '20500101' - 回测结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
comm: float = 0.0001 - 佣金费率
max_workers: int = 4 - 最大进程数
enable_index_timing: bool = False - 启用指数择时
index_mean_line: int = 20 - 指数均线周期
index_not_trader: str = '513100.SH,518880.SH' - 不参与择时的标的
index_condition_type: str = '大于均线' - 指数条件类型
index_offset: float = 0.0 - 指数偏移
mom_type: str = '百分比' - 动量类型
mom_value: float = 0.1 - 动量值
mom_models: str = '动量1' - 动量模型
mom_daily: int = 25 - 动量计算天数
period: int = 20 - 周期
short_ma: int = 3 - 短期均线
long_ma: int = 20 - 长期均线
enable_mom_filter: bool = False - 启用动量过滤
max_value: float = 5 - 最大值
mini_value: float = 0 - 最小值
max_rank: int = 1 - 最大排名
min_rank: int = 2 - 最小排名
enable_buy_condition: bool = False - 启用买入条件
enable_sell_condition: bool = False - 启用卖出条件
buy_condition_type: str = '涨幅' - 买入条件类型
buy_period: int = 20 - 买入周期
buy_period_ratio: float = 0.1 - 买入周期比例
buy_offset: float = 0.0 - 买入偏移
sell_condition_type: str = '跌幅' - 卖出条件类型
sell_period: int = 20 - 卖出周期
sell_period_ratio: float = -0.1 - 卖出周期比例
sell_offset: float = 0.0 - 卖出偏移
sell_zdf: float = 0.03 - 止盈涨幅
sell_amount: float = 1000 - 卖出金额
interval: int = 1 - 间隔
"""
print("\n" + "=" * 60)
print("📊 综合动量回测")
print("=" * 60)
result = client.xg_more_mom_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
enable_index_timing=False,
index_mean_line=20,
index_not_trader='513100.SH,518880.SH',
index_condition_type='大于均线',
index_offset=0.0,
mom_type='百分比',
mom_value=0.1,
mom_models='动量1',
mom_daily=25,
period=20,
short_ma=3,
long_ma=20,
enable_mom_filter=False,
max_value=5,
mini_value=0,
max_rank=1,
min_rank=2,
enable_buy_condition=False,
enable_sell_condition=False,
buy_condition_type='涨幅',
buy_period=20,
buy_period_ratio=0.1,
buy_offset=0.0,
sell_condition_type='跌幅',
sell_period=20,
sell_period_ratio=-0.1,
sell_offset=0.0,
sell_zdf=0.03,
sell_amount=1000,
interval=1
)
print("综合动量回测结果：")
print(result)
8. 条件因子回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例22：条件因子回测
# ============================================================
"""
参数说明：
start_date: str = '20250101' - 回测开始日期
end_date: str = '20261201' - 回测结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
comm: float = 0.0001 - 佣金费率
min_commission: float = 0 - 最低佣金
trader_type: str = '百分比' - 交易类型
trader_value: float = 0.5 - 交易值
hold_stock_limit: int = 2 - 持股上限
is_open_user_factor: bool = True - 启用自定义因子
user_factor_list: str = 'close,high,low,open,amount,volume,zdf' - 因子列表
user_factor_cacal: str = '{"因子名": "计算公式"}' - 因子计算
buy_condi_factor: str = '{"因子名": {"选择类型": "and", "选择方向": "大于", "值": 0}}' - 买入条件
rank_factor: str = '{"因子名": "降序"}' - 排序因子
sell_condi_factor: str = '{"因子名": {"选择类型": "or", "选择方向": "等于", "值": false}}' - 卖出条件
sell_type: str = '金额' - 卖出类型
sell_zdf: float = 0.03 - 止盈涨幅
sell_value: float = 1000 - 卖出金额
max_workers: int = 4 - 最大进程数
interval: int = 1 - 间隔
min_hold_days: int = 1 - 最少持有天数
risk_free_rate: float = 0.02 - 无风险利率
slippage: float = 0 - 滑点
enable_limit_up_down_filter: bool = True - 启用涨跌停过滤
max_single_position_ratio: float = 1.0 - 最大单仓位比例
"""
print("\n" + "=" * 60)
print("📊 条件因子回测")
print("=" * 60)
result = client.xg_condi_factor_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
min_commission=0,
trader_type='百分比',
trader_value=0.5,
hold_stock_limit=2,
is_open_user_factor=True,
user_factor_list='close,high,low,open,amount,volume,zdf',
user_factor_cacal='{"收盘价大于5日均线": "IF(df[\'close\']>MA(df[\'close\'],5),True,False)", "均线评分": "IF(MA(df[\'close\'],3)>MA(df[\'close\'],5),25,0)+IF(MA(df[\'close\'],5)>MA(df[\'close\'],10),25,0)+IF(MA(df[\'close\'],10)>MA(df[\'close\'],20),25,0)+IF(MA(df[\'close\'],20)>MA(df[\'close\'],30),25,0)"}',
buy_condi_factor='{"收盘价大于5日均线": {"选择类型": "and", "选择方向": "等于", "值": true}, "连续上涨天数": {"选择类型": "and", "选择方向": "大于", "值": 2}}',
rank_factor='{"均线评分": "降序"}',
sell_condi_factor='{"收盘价大于5日均线": {"选择类型": "or", "选择方向": "等于", "值": false}, "连续下跌天数": {"选择类型": "or", "选择方向": "大于", "值": 2}}',
sell_type='金额',
sell_zdf=0.03,
sell_value=1000,
max_workers=4,
interval=1,
min_hold_days=1,
risk_free_rate=0.02,
slippage=0,
enable_limit_up_down_filter=True,
max_single_position_ratio=1.0
)
print("条件因子回测结果：")
print(result)
9. 排序多因子回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例23：排序多因子回测
# ============================================================
"""
参数说明：
start_date: str = '20250101' - 回测开始日期
end_date: str = '20261201' - 回测结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
comm: float = 0.0001 - 佣金费率
min_commission: float = 0 - 最低佣金
trader_type: str = '百分比' - 交易类型
trader_value: float = 0.5 - 交易值
hold_stock_limit: int = 2 - 持股上限
is_open_user_factor: bool = True - 启用自定义因子
user_factor_list: str = 'close,high,low,open,amount,volume,zdf' - 因子列表
user_factor_cacal: str = '{"因子名": "计算公式"}' - 因子计算
is_open_buy_condi: bool = True - 启用买入条件
buy_condi_factor: str = '{"因子名": {"选择类型": "and", "选择方向": "大于", "值": 0}}' - 买入条件
rank_factor: str = '{"因子名": {"相关性": "正相关", "权重": 1}}' - 排序因子
total_factor_rank: str = '降序' - 总因子排序
sell_type: str = '金额' - 卖出类型
sell_zdf: float = 0.03 - 止盈涨幅
sell_value: float = 1000 - 卖出金额
max_workers: int = 4 - 最大进程数
interval: int = 1 - 间隔
min_hold_days: int = 1 - 最少持有天数
risk_free_rate: float = 0.02 - 无风险利率
slippage: float = 0 - 滑点
enable_limit_up_down_filter: bool = True - 启用涨跌停过滤
max_single_position_ratio: float = 1.0 - 最大单仓位比例
"""
print("\n" + "=" * 60)
print("📊 排序多因子回测")
print("=" * 60)
result = client.xg_rank_factor_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
min_commission=0,
trader_type='百分比',
trader_value=0.5,
hold_stock_limit=2,
is_open_user_factor=True,
user_factor_list='close,high,low,open,amount,volume,zdf',
user_factor_cacal='{"收盘价大于5日均线": "IF(df[\'close\']>MA(df[\'close\'],5),0,1)", "均线评分": "IF(MA(df[\'close\'],3)>MA(df[\'close\'],5),25,0)+IF(MA(df[\'close\'],5)>MA(df[\'close\'],10),25,0)+IF(MA(df[\'close\'],10)>MA(df[\'close\'],20),25,0)+IF(MA(df[\'close\'],20)>MA(df[\'close\'],30),25,0)"}',
is_open_buy_condi=True,
buy_condi_factor='{"25日回归动量": {"选择类型": "and", "选择方向": "大于", "值": 0}, "25日回归动量": {"选择类型": "and", "选择方向": "小于", "值": 5}}',
rank_factor='{"25日回归动量": {"相关性": "正相关", "权重": 1}}',
total_factor_rank='降序',
sell_type='金额',
sell_zdf=0.03,
sell_value=1000,
max_workers=4,
interval=1,
min_hold_days=1,
risk_free_rate=0.02,
slippage=0,
enable_limit_up_down_filter=True,
max_single_position_ratio=1.0
)
print("排序多因子回测结果：")
print(result)
10均值方差最优资产组合回测
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例42：均值方差最优资产组合回测
# ============================================================
"""
参数说明：
start_date: str = '20260101' - 回测开始日期
end_date: str = '20500101' - 回测结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
index_stock: str = '000300.SH' - 基准指数
cash: float = 100000 - 初始资金
comm: float = 0.0001 - 佣金费率
max_workers: int = 4 - 最大进程数
lookback_days: int = 60 - 计算协方差矩阵使用的历史数据天数
max_weight: float = 0.6 - 最大单只权重
min_weight: float = 0.05 - 最小单只权重
lambda_risk: float = 2.0 - 风险厌恶系数
interval: int = 5 - 调仓间隔（交易日）
"""
print("\n" + "=" * 60)
print("📊 均值方差最优资产组合回测")
print("=" * 60)
result = client.xg_mean_var_backtrader(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
lookback_days=60,
max_weight=0.6,
min_weight=0.05,
lambda_risk=2.0,
interval=5
)
print("均值方差最优资产组合回测结果：")
print(result)
四、个人策略模拟交易
1. 定投策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例24：定投策略模拟交易
# ============================================================
"""
参数说明：与 xg_dt_backtrader 完全相同
新增参数：
st_name: str = '小果测试' - 策略名称
open_show: str = '是' - 是否显示
"""
result = client.xg_dt_backtrader_moni(
st_name='我的定投策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
dt_interval=20,
dt_type='金额',
dt_value=1000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("定投策略模拟交易结果：")
print(result)
2. 动量策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例25：动量策略模拟交易
# ============================================================
"""
参数说明：与 xg_mom_backtrader 完全相同
"""
result = client.xg_mom_backtrader_moni(
st_name='我的动量策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
mom_type='百分比',
mom_value=1,
mom_daily=25,
min_mom=0,
max_mom=5,
buy_rank=1,
sell_zdf=0.03,
sell_amount=1000
)
print("动量策略模拟交易结果：")
print(result)
3. 资产配置策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例26：资产配置策略模拟交易
# ============================================================
"""
参数说明：与 xg_pz_backtrader 完全相同
"""
result = client.xg_pz_backtrader_moni(
st_name='我的资产配置策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
dt_type='百分比',
weight_list='0.4,0.4,0.2',
index_stock='000300.SH',
cash=100000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("资产配置策略模拟交易结果：")
print(result)
4. 资产配置平衡策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例27：资产配置平衡策略模拟交易
# ============================================================
"""
参数说明：与 xg_zcph_backtrader 完全相同
"""
result = client.xg_zcph_backtrader_moni(
st_name='我的资产配置平衡策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
dt_type='百分比',
weight_list='0.35,0.35,0.3',
deviation_list='0.1,0.1,0.05',
interval=20,
index_stock='000300.SH',
cash=100000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("资产配置平衡策略模拟交易结果：")
print(result)
5. 网格策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例28：网格策略模拟交易
# ============================================================
"""
参数说明：与 xg_gd_backtrader 完全相同
"""
result = client.xg_gd_backtrader_moni(
st_name='我的网格策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
gd_interval=1,
gd_bc_type_list='百分比,百分比',
gd_buy_bc_list='0.03,0.02',
gd_sell_bc_list='-0.02,-0.015',
gd_atr_ratio_list='2.0,2.0',
gd_type_list='金额,金额',
gd_value_list='1000,1500',
init_position_ratio_list='0.1,0.15',
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("网格策略模拟交易结果：")
print(result)
6. 海龟策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例29：海龟策略模拟交易
# ============================================================
"""
参数说明：与 xg_hg_backtrader 完全相同
"""
result = client.xg_hg_backtrader_moni(
st_name='我的海龟策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
entry_period=20,
exit_period=10,
n_period=20,
risk_per_trade=0.01,
risk_per_unit=0.02,
max_units=4,
add_unit_threshold=0.5,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000
)
print("海龟策略模拟交易结果：")
print(result)
7. 综合动量策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例30：综合动量策略模拟交易
# ============================================================
"""
参数说明：与 xg_more_mom_backtrader 完全相同
"""
result = client.xg_more_mom_backtrader_moni(
st_name='我的综合动量策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
enable_index_timing=False,
index_mean_line=20,
index_not_trader='513100.SH,518880.SH',
index_condition_type='大于均线',
index_offset=0.0,
mom_type='百分比',
mom_value=0.1,
mom_models='动量1',
mom_daily=25,
period=20,
short_ma=3,
long_ma=20,
enable_mom_filter=False,
max_value=5,
mini_value=0,
max_rank=1,
min_rank=2,
enable_buy_condition=False,
enable_sell_condition=False,
buy_condition_type='涨幅',
buy_period=20,
buy_period_ratio=0.1,
buy_offset=0.0,
sell_condition_type='跌幅',
sell_period=20,
sell_period_ratio=-0.1,
sell_offset=0.0,
sell_zdf=0.03,
sell_amount=1000,
interval=1
)
print("综合动量策略模拟交易结果：")
print(result)
8. 条件因子策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例31：条件因子策略模拟交易
# ============================================================
"""
参数说明：与 xg_condi_factor_backtrader 完全相同
"""
result = client.xg_condi_factor_backtrader_moni(
st_name='我的条件因子策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
min_commission=0,
trader_type='百分比',
trader_value=0.5,
hold_stock_limit=2,
is_open_user_factor=True,
user_factor_list='close,high,low,open,amount,volume,zdf',
user_factor_cacal='{"收盘价大于5日均线": "IF(df[\'close\']>MA(df[\'close\'],5),True,False)", "均线评分": "IF(MA(df[\'close\'],3)>MA(df[\'close\'],5),25,0)+IF(MA(df[\'close\'],5)>MA(df[\'close\'],10),25,0)+IF(MA(df[\'close\'],10)>MA(df[\'close\'],20),25,0)+IF(MA(df[\'close\'],20)>MA(df[\'close\'],30),25,0)"}',
buy_condi_factor='{"收盘价大于5日均线": {"选择类型": "and", "选择方向": "等于", "值": true}, "连续上涨天数": {"选择类型": "and", "选择方向": "大于", "值": 2}}',
rank_factor='{"均线评分": "降序"}',
sell_condi_factor='{"收盘价大于5日均线": {"选择类型": "or", "选择方向": "等于", "值": false}, "连续下跌天数": {"选择类型": "or", "选择方向": "大于", "值": 2}}',
sell_type='金额',
sell_zdf=0.03,
sell_value=1000,
max_workers=4,
interval=1,
min_hold_days=1,
risk_free_rate=0.02,
slippage=0,
enable_limit_up_down_filter=True,
max_single_position_ratio=1.0
)
print("条件因子策略模拟交易结果：")
print(result)
9. 排序多因子策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例32：排序多因子策略模拟交易
# ============================================================
"""
参数说明：与 xg_rank_factor_backtrader 完全相同
"""
result = client.xg_rank_factor_backtrader_moni(
st_name='我的排序多因子策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
min_commission=0,
trader_type='百分比',
trader_value=0.5,
hold_stock_limit=2,
is_open_user_factor=True,
user_factor_list='close,high,low,open,amount,volume,zdf',
user_factor_cacal='{"收盘价大于5日均线": "IF(df[\'close\']>MA(df[\'close\'],5),0,1)", "均线评分": "IF(MA(df[\'close\'],3)>MA(df[\'close\'],5),25,0)+IF(MA(df[\'close\'],5)>MA(df[\'close\'],10),25,0)+IF(MA(df[\'close\'],10)>MA(df[\'close\'],20),25,0)+IF(MA(df[\'close\'],20)>MA(df[\'close\'],30),25,0)"}',
is_open_buy_condi=True,
buy_condi_factor='{"25日回归动量": {"选择类型": "and", "选择方向": "大于", "值": 0}, "25日回归动量": {"选择类型": "and", "选择方向": "小于", "值": 5}}',
rank_factor='{"25日回归动量": {"相关性": "正相关", "权重": 1}}',
total_factor_rank='降序',
sell_type='金额',
sell_zdf=0.03,
sell_value=1000,
max_workers=4,
interval=1,
min_hold_days=1,
risk_free_rate=0.02,
slippage=0,
enable_limit_up_down_filter=True,
max_single_position_ratio=1.0
)
print("排序多因子策略模拟交易结果：")
print(result)
10均值方差策略模拟交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例43：均值方差策略模拟交易
# ============================================================
"""
参数说明：与 xg_mean_var_backtrader 完全相同
新增参数：
st_name: str = '小果均值方差策略' - 策略名称
open_show: str = '是' - 是否公开策略
"""
result = client.xg_mean_var_backtrader_moni(
st_name='我的均值方差策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
lookback_days=60,
max_weight=0.6,
min_weight=0.05,
lambda_risk=2.0,
interval=5
)
print("均值方差策略模拟交易结果：")
print(result)
五、社区模拟策略接口
1. 定投策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例33：定投策略社区交易
# ============================================================
"""
参数说明：与 xg_dt_backtrader_moni 完全相同
"""
result = client.xg_dt_backtrader_moni_sq(
st_name='社区定投策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
dt_interval=20,
dt_type='金额',
dt_value=1000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("定投策略社区交易结果：")
print(result)
2. 动量策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例34：动量策略社区交易
# ============================================================
"""
参数说明：与 xg_mom_backtrader_moni 完全相同
"""
result = client.xg_mom_backtrader_moni_sq(
st_name='社区动量策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
mom_type='百分比',
mom_value=1,
mom_daily=25,
min_mom=0,
max_mom=5,
buy_rank=1,
sell_zdf=0.03,
sell_amount=1000
)
print("动量策略社区交易结果：")
print(result)
3. 资产配置策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例35：资产配置策略社区交易
# ============================================================
"""
参数说明：与 xg_pz_backtrader_moni 完全相同
"""
result = client.xg_pz_backtrader_moni_sq(
st_name='社区资产配置策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
dt_type='百分比',
weight_list='0.4,0.4,0.2',
index_stock='000300.SH',
cash=100000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("资产配置策略社区交易结果：")
print(result)
4. 资产配置平衡策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例36：资产配置平衡策略社区交易
# ============================================================
"""
参数说明：与 xg_zcph_backtrader_moni 完全相同
"""
result = client.xg_zcph_backtrader_moni_sq(
st_name='社区资产配置平衡策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
dt_type='百分比',
weight_list='0.35,0.35,0.3',
deviation_list='0.1,0.1,0.05',
interval=20,
index_stock='000300.SH',
cash=100000,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("资产配置平衡策略社区交易结果：")
print(result)
5. 网格策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例37：网格策略社区交易
# ============================================================
"""
参数说明：与 xg_gd_backtrader_moni 完全相同
"""
result = client.xg_gd_backtrader_moni_sq(
st_name='社区网格策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
gd_interval=1,
gd_bc_type_list='百分比,百分比',
gd_buy_bc_list='0.03,0.02',
gd_sell_bc_list='-0.02,-0.015',
gd_atr_ratio_list='2.0,2.0',
gd_type_list='金额,金额',
gd_value_list='1000,1500',
init_position_ratio_list='0.1,0.15',
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000,
comm=0.0001
)
print("网格策略社区交易结果：")
print(result)
6. 海龟策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例38：海龟策略社区交易
# ============================================================
"""
参数说明：与 xg_hg_backtrader_moni 完全相同
"""
result = client.xg_hg_backtrader_moni_sq(
st_name='社区海龟策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='513100.SH,513500.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
entry_period=20,
exit_period=10,
n_period=20,
risk_per_trade=0.01,
risk_per_unit=0.02,
max_units=4,
add_unit_threshold=0.5,
sell_zdf=0.03,
buy_zdf=-0.03,
trade_value=1000
)
print("海龟策略社区交易结果：")
print(result)
7. 综合动量策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例39：综合动量策略社区交易
# ============================================================
"""
参数说明：与 xg_more_mom_backtrader_moni 完全相同
"""
result = client.xg_more_mom_backtrader_moni_sq(
st_name='社区综合动量策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
enable_index_timing=False,
index_mean_line=20,
index_not_trader='513100.SH,518880.SH',
index_condition_type='大于均线',
index_offset=0.0,
mom_type='百分比',
mom_value=0.1,
mom_models='动量1',
mom_daily=25,
period=20,
short_ma=3,
long_ma=20,
enable_mom_filter=False,
max_value=5,
mini_value=0,
max_rank=1,
min_rank=2,
enable_buy_condition=False,
enable_sell_condition=False,
buy_condition_type='涨幅',
buy_period=20,
buy_period_ratio=0.1,
buy_offset=0.0,
sell_condition_type='跌幅',
sell_period=20,
sell_period_ratio=-0.1,
sell_offset=0.0,
sell_zdf=0.03,
sell_amount=1000,
interval=1
)
print("综合动量策略社区交易结果：")
print(result)
8. 条件因子策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例40：条件因子策略社区交易
# ============================================================
"""
参数说明：与 xg_condi_factor_backtrader_moni 完全相同
"""
result = client.xg_condi_factor_backtrader_moni_sq(
st_name='社区条件因子策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
min_commission=0,
trader_type='百分比',
trader_value=0.5,
hold_stock_limit=2,
is_open_user_factor=True,
user_factor_list='close,high,low,open,amount,volume,zdf',
user_factor_cacal='{"收盘价大于5日均线": "IF(df[\'close\']>MA(df[\'close\'],5),True,False)", "均线评分": "IF(MA(df[\'close\'],3)>MA(df[\'close\'],5),25,0)+IF(MA(df[\'close\'],5)>MA(df[\'close\'],10),25,0)+IF(MA(df[\'close\'],10)>MA(df[\'close\'],20),25,0)+IF(MA(df[\'close\'],20)>MA(df[\'close\'],30),25,0)"}',
buy_condi_factor='{"收盘价大于5日均线": {"选择类型": "and", "选择方向": "等于", "值": true}, "连续上涨天数": {"选择类型": "and", "选择方向": "大于", "值": 2}}',
rank_factor='{"均线评分": "降序"}',
sell_condi_factor='{"收盘价大于5日均线": {"选择类型": "or", "选择方向": "等于", "值": false}, "连续下跌天数": {"选择类型": "or", "选择方向": "大于", "值": 2}}',
sell_type='金额',
sell_zdf=0.03,
sell_value=1000,
max_workers=4,
interval=1,
min_hold_days=1,
risk_free_rate=0.02,
slippage=0,
enable_limit_up_down_filter=True,
max_single_position_ratio=1.0
)
print("条件因子策略社区交易结果：")
print(result)
9. 排序多因子策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例41：排序多因子策略社区交易
# ============================================================
"""
参数说明：与 xg_rank_factor_backtrader_moni 完全相同
"""
result = client.xg_rank_factor_backtrader_moni_sq(
st_name='社区排序多因子策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
min_commission=0,
trader_type='百分比',
trader_value=0.5,
hold_stock_limit=2,
is_open_user_factor=True,
user_factor_list='close,high,low,open,amount,volume,zdf',
user_factor_cacal='{"收盘价大于5日均线": "IF(df[\'close\']>MA(df[\'close\'],5),0,1)", "均线评分": "IF(MA(df[\'close\'],3)>MA(df[\'close\'],5),25,0)+IF(MA(df[\'close\'],5)>MA(df[\'close\'],10),25,0)+IF(MA(df[\'close\'],10)>MA(df[\'close\'],20),25,0)+IF(MA(df[\'close\'],20)>MA(df[\'close\'],30),25,0)"}',
is_open_buy_condi=True,
buy_condi_factor='{"25日回归动量": {"选择类型": "and", "选择方向": "大于", "值": 0}, "25日回归动量": {"选择类型": "and", "选择方向": "小于", "值": 5}}',
rank_factor='{"25日回归动量": {"相关性": "正相关", "权重": 1}}',
total_factor_rank='降序',
sell_type='金额',
sell_zdf=0.03,
sell_value=1000,
max_workers=4,
interval=1,
min_hold_days=1,
risk_free_rate=0.02,
slippage=0,
enable_limit_up_down_filter=True,
max_single_position_ratio=1.0
)
print("排序多因子策略社区交易结果：")
print(result)
10均值方差策略社区交易
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例44：均值方差策略社区交易
# ============================================================
"""
参数说明：与 xg_mean_var_backtrader_moni 完全相同
"""
result = client.xg_mean_var_backtrader_moni_sq(
st_name='社区均值方差策略',
open_show='是',
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
index_stock='000300.SH',
cash=100000,
comm=0.0001,
max_workers=4,
lookback_days=60,
max_weight=0.6,
min_weight=0.05,
lambda_risk=2.0,
interval=5
)
print("均值方差策略社区交易结果：")
print(result)
六、多标的量化分析接口
1相关性矩阵
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例45：计算多标的收益率相关性矩阵
# ============================================================
"""
参数说明：
start_date: str = '20260101' - 开始日期
end_date: str = '20500101' - 结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
max_workers: int = 4 - 最大进程数
method: str = 'pearson' - 相关性计算方法：pearson/spearman/kendall
risk_free_rate: float = 0.03 - 无风险利率
返回数据：
correlation_matrix - 相关性矩阵
correlation_matrix_index - 矩阵索引（股票代码列表）
covariance_matrix - 协方差矩阵
stock_list - 股票列表
method - 使用的计算方法
"""
print("\n" + "=" * 60)
print("📊 多标的收益率相关性矩阵")
print("=" * 60)
result = client.xg_stock_cov_correlation(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
max_workers=4,
method='pearson',
risk_free_rate=0.03
)
print(result)
2 协方差矩阵
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例46：计算多标的收益率协方差矩阵
# ============================================================
"""
参数说明：
start_date: str = '20260101' - 开始日期
end_date: str = '20500101' - 结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
max_workers: int = 4 - 最大进程数
method: str = 'pearson' - 相关性计算方法
risk_free_rate: float = 0.03 - 无风险利率
annualized: bool = True - 是否年化协方差矩阵
返回数据：
covariance_matrix - 协方差矩阵
covariance_matrix_index - 矩阵索引
standard_deviations - 标准差（年化）
stock_list - 股票列表
annualized - 是否年化
"""
print("\n" + "=" * 60)
print("📊 多标的收益率协方差矩阵")
print("=" * 60)
result = client.xg_stock_cov_covariance(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
max_workers=4,
method='pearson',
risk_free_rate=0.03,
annualized=True
)
print(result)
3投资组合优化
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例47：多标的投资组合优化
# ============================================================
"""
参数说明：
start_date: str = '20260101' - 开始日期
end_date: str = '20500101' - 结束日期
stock_list: str = '159915.SZ,513100.SH,518880.SH' - 股票列表
max_workers: int = 4 - 最大进程数
method: str = 'pearson' - 相关性计算方法
risk_free_rate: float = 0.03 - 无风险利率
target_return: float = None - 目标收益率（年化），可选
返回组合：
min_variance - 最小方差组合
max_sharpe - 最大夏普比率组合
risk_parity - 风险平价组合
equal_weight - 等权重组合（基准）
target_return_portfolio - 目标收益组合（如果指定target_return）
每个组合包含：
weights - 各标的权重
expected_return - 预期收益率（年化）
volatility - 波动率（年化）
sharpe_ratio - 夏普比率
"""
print("\n" + "=" * 60)
print("📊 多标的投资组合优化")
print("=" * 60)
# 不指定目标收益率
result = client.xg_stock_cov_portfolio(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
max_workers=4,
method='pearson',
risk_free_rate=0.03,
target_return=None
)
print("投资组合优化结果：")
print(result)
# 指定目标收益率
print("\n" + "-" * 40)
print("指定目标收益率 15%")
result = client.xg_stock_cov_portfolio(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,513100.SH,518880.SH',
max_workers=4,
method='pearson',
risk_free_rate=0.03,
target_return=0.15
)
print(result)
4股票组合分析接口
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例48：小果股票分析系统 - 组合收益分析
# ============================================================
"""
参数说明：
start_date: str = '20240101' - 开始日期
end_date: str = '20261231' - 结束日期
stock_list: str = '159915.SZ,518880.SH,510300.SH' - 股票列表
stock_weight: str = '0.4,0.3,0.3' - 股票权重（自动归一化）
index_stock: str = '000300.SH' - 基准指数
max_workers: int = 4 - 最大进程数
risk_free_rate: float = 0.03 - 无风险利率
返回数据结构：
summary - 基本摘要信息（日期范围、股票数量等）
performance_metrics - 完整绩效指标（50+项）
annual_performance - 年度绩效
rolling_metrics - 滚动指标（60日窗口）
period_returns - 周期收益（日/周/月/年）
equity_curve - 权益曲线（每日净值）
weight_info - 个股权重信息
raw_data - 原始日度数据
绩效指标包括：
total_return - 总收益率
annual_return - 年化收益率
annual_std - 年化波动率
sharpe_ratio - 夏普比率
max_drawdown - 最大回撤
max_drawdown_duration - 最大回撤持续天数
win_rate - 胜率
positive_ratio - 正收益比例
beta - Beta系数
alpha - Alpha系数
information_ratio - 信息比率
tracking_error - 跟踪误差
calmar_ratio - 卡玛比率
sortino_ratio - 索提诺比率
"""
print("\n" + "=" * 60)
print("📊 小果股票分析系统 - 组合收益分析")
print("=" * 60)
result = client.xg_stock_analysis(
start_date='20240101',
end_date='20241231',
stock_list='159915.SZ,518880.SH,510300.SH',
stock_weight='0.4,0.3,0.3',
index_stock='000300.SH',
max_workers=4,
risk_free_rate=0.03
)
print("股票组合分析结果：")
print(f"状态: {result.get('status')}")
print(f"消息: {result.get('message')}")
# 提取绩效指标
metrics = result.get('performance_metrics', {})
print(f"\n📈 绩效指标:")
print(f" 总收益率: {metrics.get('total_return', 0)*100:.2f}%")
print(f" 年化收益率: {metrics.get('annual_return', 0)*100:.2f}%")
print(f" 年化波动率: {metrics.get('annual_std', 0)*100:.2f}%")
print(f" 夏普比率: {metrics.get('sharpe_ratio', 0):.4f}")
print(f" 最大回撤: {metrics.get('max_drawdown', 0)*100:.2f}%")
print(f" 胜率: {metrics.get('positive_ratio', 0)*100:.2f}%")
if 'beta' in metrics:
print(f" Beta: {metrics.get('beta', 0):.4f}")
if 'alpha' in metrics:
print(f" Alpha: {metrics.get('alpha', 0)*100:.2f}%")
七股票分钟数据
获取5分钟数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例57：获取5分钟数据（mini）
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = '5' - 频率（5/15/30/60）
adjustflag: str = '2' - 复权类型（1-不复权 2-前复权 3-后复权）
"""
print("\n" + "=" * 60)
print("📊 获取5分钟数据（mini）")
print("=" * 60)
result = client.get_mini_data_5(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='5',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条5分钟数据（mini）")
print(df.head())
2获取5分钟数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例57：获取5分钟数据（mini）
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = '5' - 频率（5/15/30/60）
adjustflag: str = '2' - 复权类型（1-不复权 2-前复权 3-后复权）
"""
print("\n" + "=" * 60)
print("📊 获取5分钟数据（mini）")
print("=" * 60)
result = client.get_mini_data_5(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='5',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条5分钟数据（mini）")
print(df.head())
3获取15分钟数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例58：获取15分钟数据（mini）
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = '15' - 频率
adjustflag: str = '2' - 复权类型
"""
print("\n" + "=" * 60)
print("📊 获取15分钟数据（mini）")
print("=" * 60)
result = client.get_mini_data_15(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='15',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条15分钟数据（mini）")
print(df.head()
4 获取30分钟数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例59：获取30分钟数据（mini）
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = '30' - 频率
adjustflag: str = '2' - 复权类型
"""
print("\n" + "=" * 60)
print("📊 获取30分钟数据（mini）")
print("=" * 60)
result = client.get_mini_data_30(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='30',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条30分钟数据（mini）")
print(df.head())
5获取60分钟数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例60：获取60分钟数据（mini）
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = '60' - 频率
adjustflag: str = '2' - 复权类型
"""
print("\n" + "=" * 60)
print("📊 获取60分钟数据（mini）")
print("=" * 60)
result = client.get_mini_data_60(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='60',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条60分钟数据（mini）")
print(df.head())
6 获取日线数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例61：获取日线数据
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = 'd' - 频率（d/w/m）
adjustflag: str = '2' - 复权类型（1-不复权 2-前复权 3-后复权）
返回字段：
date, open, high, low, close, volume, amount
"""
print("\n" + "=" * 60)
print("📊 获取日线数据")
print("=" * 60)
result = client.query_history_k_data_plus_d(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='d',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条日线数据")
print(df.head())
7 获取周线数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例62：获取周线数据
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = 'w' - 频率
adjustflag: str = '2' - 复权类型
"""
print("\n" + "=" * 60)
print("📊 获取周线数据")
print("=" * 60)
result = client.query_history_k_data_plus_w(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='w',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条周线数据")
print(df.head())
8获取月线数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例63：获取月线数据
# ============================================================
"""
参数说明：
stock: str = 'sh.600031' - 股票代码（mini格式）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = 'm' - 频率
adjustflag: str = '2' - 复权类型
"""
print("\n" + "=" * 60)
print("📊 获取月线数据")
print("=" * 60)
result = client.query_history_k_data_plus_m(
stock='sh.600031',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='m',
adjustflag='2'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条月线数据")
print(df.head())
八指数K线数据接口
1获取指数日线数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例64：获取指数日线数据
# ============================================================
"""
参数说明：
stock: str = 'sh.000001' - 指数代码（sh.000001 上证指数，sz.399001 深证成指）
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = 'd' - 频率（d/w/m）
"""
print("\n" + "=" * 60)
print("📊 获取指数日线数据")
print("=" * 60)
result = client.query_history_k_data_plus_index_d(
stock='sh.000001',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='d'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条指数日线数据")
print(df.head())
2获取指数周线数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例65：获取指数周线数据
# ============================================================
"""
参数说明：
stock: str = 'sh.000001' - 指数代码
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = 'w' - 频率
"""
print("\n" + "=" * 60)
print("📊 获取指数周线数据")
print("=" * 60)
result = client.query_history_k_data_plus_index_w(
stock='sh.000001',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='w'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条指数周线数据")
print(df.head())
3获取指数月线数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例66：获取指数月线数据
# ============================================================
"""
参数说明：
stock: str = 'sh.000001' - 指数代码
start_date: str = '2026-04-01' - 开始日期
end_date: str = '2050-12-31' - 结束日期
frequency: str = 'm' - 频率
"""
print("\n" + "=" * 60)
print("📊 获取指数月线数据")
print("=" * 60)
result = client.query_history_k_data_plus_index_m(
stock='sh.000001',
start_date='2026-04-01',
end_date='2050-12-31',
frequency='m'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条指数月线数据")
print(df.head())
九 财务数据
1获取盈利能力数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例1：获取盈利能力数据
# ============================================================
"""
参数说明：
code: str = 'sh.600031' - 股票代码，格式：sh.600031 或 sz.000001
year: str = '2025' - 年份
quarter: str = '1' - 季度（1/2/3/4）
【盈利能力 - query_profit_data 返回字段】
code - 证券代码
pubDate - 公司发布财报的日期
statDate - 财报统计的季度的最后一天，如2017-03-31, 2017-06-30
roeAvg - 净资产收益率(平均)(%)，归属母公司股东净利润/[(期初归属母公司股东的权益+期末归属母公司股东的权益)/2]*100%
npMargin - 销售净利率(%)，净利润/营业收入*100%
gpMargin - 销售毛利率(%)，毛利/营业收入100%=(营业收入-营业成本)/营业收入100%
netProfit - 净利润(元)
epsTTM - 每股收益，归属母公司股东的净利润TTM/最新总股本
MBRevenue - 主营营业收入(元)
totalShare - 总股本
liqaShare - 流通股本
"""
print("\n" + "=" * 60)
print("📊 获取盈利能力数据")
print("=" * 60)
result = client.query_profit_data(
code='sh.600031',
year='2025',
quarter='1'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条盈利能力数据")
print(df.head())
# 打印关键指标
if not df.empty:
print("\n📈 关键盈利能力指标：")
print(f" 净资产收益率(平均): {df['roeAvg'].iloc[0] if 'roeAvg' in df.columns else 'N/A'}")
print(f" 销售净利率: {df['npMargin'].iloc[0] if 'npMargin' in df.columns else 'N/A'}")
print(f" 销售毛利率: {df['gpMargin'].iloc[0] if 'gpMargin' in df.columns else 'N/A'}")
print(f" 净利润: {df['netProfit'].iloc[0] if 'netProfit' in df.columns else 'N/A'}")
2获取营运能力数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例2：获取营运能力数据
# ============================================================
"""
参数说明：
code: str = 'sh.600031' - 股票代码
year: str = '2025' - 年份
quarter: str = '1' - 季度（1/2/3/4）
【营运能力 - query_operation_data 返回字段】
code - 证券代码
pubDate - 公司发布财报的日期
statDate - 财报统计的季度的最后一天
NRTurnRatio - 应收账款周转率(次)，营业收入/[(期初应收票据及应收账款净额+期末应收票据及应收账款净额)/2]
NRTurnDays - 应收账款周转天数(天)，季报天数/应收账款周转率(一季报：90天，中报：180天，三季报：270天，年报：360天)
INVTurnRatio - 存货周转率(次)，营业成本/[(期初存货净额+期末存货净额)/2]
INVTurnDays - 存货周转天数(天)，季报天数/存货周转率(一季报：90天，中报：180天，三季报：270天，年报：360天)
CATurnRatio - 流动资产周转率(次)，营业总收入/[(期初流动资产+期末流动资产)/2]
AssetTurnRatio - 总资产周转率，营业总收入/[(期初资产总额+期末资产总额)/2]
"""
print("\n" + "=" * 60)
print("📊 获取营运能力数据")
print("=" * 60)
result = client.query_operation_data(
code='sh.600031',
year='2025',
quarter='1'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条营运能力数据")
print(df.head())
if not df.empty:
print("\n📈 关键营运能力指标：")
print(f" 应收账款周转率: {df['NRTurnRatio'].iloc[0] if 'NRTurnRatio' in df.columns else 'N/A'}")
print(f" 存货周转率: {df['INVTurnRatio'].iloc[0] if 'INVTurnRatio' in df.columns else 'N/A'}")
print(f" 总资产周转率: {df['AssetTurnRatio'].iloc[0] if 'AssetTurnRatio' in df.columns else 'N/A'}")
3获取成长能力数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例3：获取成长能力数据
# ============================================================
"""
参数说明：
code: str = 'sh.600031' - 股票代码
year: str = '2025' - 年份
quarter: str = '1' - 季度（1/2/3/4）
【成长能力 - query_growth_data 返回字段】
code - 证券代码
pubDate - 公司发布财报的日期
statDate - 财报统计的季度的最后一天
YOYEquity - 净资产同比增长率，(本期净资产-上年同期净资产)/上年同期净资产的绝对值*100%
YOYAsset - 总资产同比增长率，(本期总资产-上年同期总资产)/上年同期总资产的绝对值*100%
YOYNI - 净利润同比增长率，(本期净利润-上年同期净利润)/上年同期净利润的绝对值*100%
YOYEPSBasic - 基本每股收益同比增长率，(本期基本每股收益-上年同期基本每股收益)/上年同期基本每股收益的绝对值*100%
YOYPNI - 归属母公司股东净利润同比增长率，(本期归属母公司股东净利润-上年同期归属母公司股东净利润)/上年同期归属母公司股东净利润的绝对值*100%
"""
print("\n" + "=" * 60)
print("📊 获取成长能力数据")
print("=" * 60)
result = client.query_growth_data(
code='sh.600031',
year='2025',
quarter='1'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条成长能力数据")
print(df.head())
if not df.empty:
print("\n📈 关键成长能力指标：")
print(f" 净资产同比增长率: {df['YOYEquity'].iloc[0] if 'YOYEquity' in df.columns else 'N/A'}")
print(f" 总资产同比增长率: {df['YOYAsset'].iloc[0] if 'YOYAsset' in df.columns else 'N/A'}")
print(f" 净利润同比增长率: {df['YOYNI'].iloc[0] if 'YOYNI' in df.columns else 'N/A'}")
print(f" 归属母公司股东净利润同比增长率: {df['YOYPNI'].iloc[0] if 'YOYPNI' in df.columns else 'N/A'}")
4 获取偿债能力数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例4：获取偿债能力数据
# ============================================================
"""
参数说明：
code: str = 'sh.600031' - 股票代码
year: str = '2025' - 年份
quarter: str = '1' - 季度（1/2/3/4）
【偿债能力 - query_balance_data 返回字段】
code - 证券代码
pubDate - 公司发布财报的日期
statDate - 财报统计的季度的最后一天
currentRatio - 流动比率，流动资产/流动负债
quickRatio - 速动比率，(流动资产-存货净额)/流动负债
cashRatio - 现金比率，(货币资金+交易性金融资产)/流动负债
YOYLiability - 总负债同比增长率，(本期总负债-上年同期总负债)/上年同期中负债的绝对值*100%
liabilityToAsset - 资产负债率，负债总额/资产总额
assetToEquity - 权益乘数，资产总额/股东权益总额=1/(1-资产负债率)
"""
print("\n" + "=" * 60)
print("📊 获取偿债能力数据")
print("=" * 60)
result = client.query_balance_data(
code='sh.600031',
year='2025',
quarter='1'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条偿债能力数据")
print(df.head())
if not df.empty:
print("\n📈 关键偿债能力指标：")
print(f" 流动比率: {df['currentRatio'].iloc[0] if 'currentRatio' in df.columns else 'N/A'}")
print(f" 速动比率: {df['quickRatio'].iloc[0] if 'quickRatio' in df.columns else 'N/A'}")
print(f" 资产负债率: {df['liabilityToAsset'].iloc[0] if 'liabilityToAsset' in df.columns else 'N/A'}")
5获取现金流量数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例5：获取现金流量数据
# ============================================================
"""
参数说明：
code: str = 'sh.600031' - 股票代码
year: str = '2025' - 年份
quarter: str = '1' - 季度（1/2/3/4）
【现金流量 - query_cash_flow_data 返回字段】
code - 证券代码
pubDate - 公司发布财报的日期
statDate - 财报统计的季度的最后一天
CAToAsset - 流动资产除以总资产
NCAToAsset - 非流动资产除以总资产
tangibleAssetToAsset - 有形资产除以总资产
ebitToInterest - 已获利息倍数，息税前利润/利息费用
CFOToOR - 经营活动产生的现金流量净额除以营业收入
CFOToNP - 经营性现金净流量除以净利润
CFOToGr - 经营性现金净流量除以营业总收入
"""
print("\n" + "=" * 60)
print("📊 获取现金流量数据")
print("=" * 60)
result = client.query_cash_flow_data(
code='sh.600031',
year='2025',
quarter='1'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条现金流量数据")
print(df.head())
if not df.empty:
print("\n📈 关键现金流量指标：")
print(f" 已获利息倍数: {df['ebitToInterest'].iloc[0] if 'ebitToInterest' in df.columns else 'N/A'}")
print(f" 经营性现金净流量/营业收入: {df['CFOToOR'].iloc[0] if 'CFOToOR' in df.columns else 'N/A'}")
6 获取杜邦指数数据
from xg_quant_backtrader_data.xg_quant_backtrader_data import xg_quant_backtrader_data
# 初始化客户端
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
# ============================================================
# 完整示例6：获取杜邦指数数据
# ============================================================
"""
参数说明：
code: str = 'sh.600031' - 股票代码
year: str = '2025' - 年份
quarter: str = '1' - 季度（1/2/3/4）
【杜邦指数 - query_dupont_data 返回字段】
code - 证券代码
pubDate - 公司发布财报的日期
statDate - 财报统计的季度的最后一天
dupontROE - 净资产收益率，归属母公司股东净利润/[(期初归属母公司股东的权益+期末归属母公司股东的权益)/2]*100%
dupontAssetStoEquity - 权益乘数，反映企业财务杠杆效应强弱和财务风险，平均总资产/平均归属于母公司的股东权益
dupontAssetTurn - 总资产周转率，反映企业资产管理效率的指标，营业总收入/[(期初资产总额+期末资产总额)/2]
dupontPnitoni - 归属母公司股东的净利润/净利润，反映母公司控股子公司百分比
dupontNitogr - 净利润/营业总收入，反映企业销售获利率
dupontTaxBurden - 净利润/利润总额，反映企业税负水平，该比值高则税负较低
dupontIntburden - 利润总额/息税前利润，反映企业利息负担，该比值高则税负较低
dupontEbittogr - 息税前利润/营业总收入，反映企业经营利润率
"""
print("\n" + "=" * 60)
print("📊 获取杜邦指数数据")
print("=" * 60)
result = client.query_dupont_data(
code='sh.600031',
year='2025',
quarter='1'
)
df = client._to_dataframe(result)
print(f"✅ 获取到 {len(df)} 条杜邦指数数据")
print(df.head())
if not df.empty:
print("\n📈 关键杜邦指数指标：")
print(f" 净资产收益率(ROE): {df['dupontROE'].iloc[0] if 'dupontROE' in df.columns else 'N/A'}")
print(f" 权益乘数: {df['dupontAssetStoEquity'].iloc[0] if 'dupontAssetStoEquity' in df.columns else 'N/A'}")
print(f" 总资产周转率: {df['dupontAssetTurn'].iloc[0] if 'dupontAssetTurn' in df.columns else 'N/A'}")
print(f" 销售净利率: {df['dupontNitogr'].iloc[0] if 'dupontNitogr' in df.columns else 'N/A'}")
十全部代码
'''
作者:小果
微信:xg_quant
'''
import requests
import json
import pandas as pd
import numpy as np
from typing import Optional, Dict, Any, List, Union
from datetime import datetime
import urllib.parse
class xg_quant_backtrader_data:
"""
小果量化回测系统数据api
小果量化数据 - API对接框架
"""
def __init__(
self,
url: str = "数据库服务器",
port: int = 数据库端口, # 修复：port应该是int类型
user: str = "小果",
password: str = "小果",
auth_code: str = "小果"
):
"""
初始化小果量化数据客户端
Args:
url: 服务器地址
port: 服务器端口
user: 用户名称
password: 用户密码
auth_code: 授权码
"""
self.url = url
self.port = port
self.user = user
self.password = password
self.auth_code = auth_code
self.base_url = f"http://{url}:{port}"
self.session = requests.Session()
self.timeout = 120
# 设置默认请求头
self.session.headers.update({
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
'Accept': 'application/json',
'Accept-Charset': 'utf-8'
})
def _get_params(self, **kwargs) -> Dict[str, Any]:
"""构建请求参数，自动添加用户认证信息"""
params = {
'user': self.user,
'password': self.password,
'auth_code': self.auth_code,
}
params.update(kwargs)
return params
def _request(
self,
endpoint: str,
params: Dict[str, Any],
method: str = 'GET',
timeout: Optional[int] = None,
verbose: bool = True
) -> Dict[str, Any]:
"""
发送HTTP请求
Args:
endpoint: API端点路径
params: 请求参数
method: 请求方法
timeout: 超时时间
verbose: 是否打印详细信息
Returns:
响应数据字典
"""
if timeout is None:
timeout = self.timeout
url = f"{self.base_url}{endpoint}"
# 清理参数中的None值
clean_params = {k: v for k, v in params.items() if v is not None}
try:
if method.upper() == 'GET':
response = self.session.get(url, params=clean_params, timeout=timeout)
else:
response = self.session.post(url, params=clean_params, timeout=timeout)
if verbose:
print(f"📤 请求URL: {response.url[:100]}...")
print(f"📤 状态码: {response.status_code}")
response.raise_for_status()
# 尝试解析JSON
try:
result = response.json()
if verbose and result.get('status') == 'failed':
print(f"❌ 接口返回失败: {result.get('message', result.get('error', '未知错误'))}")
if 'info' in result:
print(f"📄 详细信息: {result.get('info')}")
return result
except json.JSONDecodeError as e:
print(f"❌ JSON解析失败: {e}")
print(f"📄 响应内容: {response.text[:500]}")
return {"status": "failed", "error": "Invalid JSON response", "raw": response.text[:500]}
except requests.exceptions.RequestException as e:
print(f"❌ 请求失败: {e}")
# 尝试获取更多错误信息
if hasattr(e, 'response') and e.response is not None:
try:
error_detail = e.response.json()
print(f"📄 错误详情: {error_detail}")
return {"status": "failed", "error": str(e), "detail": error_detail}
except:
print(f"📄 响应内容: {e.response.text[:500]}")
return {"status": "failed", "error": str(e), "raw": e.response.text[:500]}
return {"status": "failed", "error": str(e)}
def _to_dataframe(self, data: Dict[str, Any]) -> pd.DataFrame:
"""
将API返回的数据转换为DataFrame
处理NaN和Infinity值
"""
if data.get('status') == 'failed':
print(f"⚠️ 数据获取失败: {data.get('message', data.get('error', '未知错误'))}")
return pd.DataFrame()
if 'data' in data and data['data']:
df = pd.DataFrame(data['data'])
# 清理数据：将NaN、Infinity替换为None
df = df.replace([np.inf, -np.inf], np.nan)
df = df.where(pd.notnull(df), None)
return df
return pd.DataFrame()
def _to_dataframe_with_info(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""将API返回的数据转换为包含元信息的DataFrame"""
if data.get('status') == 'failed':
return {
'status': 'failed',
'data': pd.DataFrame(),
'info': data.get('message', data.get('error', '未知错误'))
}
df = pd.DataFrame(data.get('data', []))
# 清理数据
df = df.replace([np.inf, -np.inf], np.nan)
df = df.where(pd.notnull(df), None)
result = {
'status': data.get('status', 'success'),
'data': df,
'total': data.get('total', len(df)),
'message': data.get('message', ''),
'available_columns': data.get('available_columns', []),
'selected_columns': data.get('selected_columns', []),
}
for key in ['stock', 'start_date', 'end_date', 'table', 'report_date']:
if key in data:
result[key] = data[key]
return result
# ============================================================
# 一、回测接口（类方法名不带数字，但请求路径带 _1）
# ============================================================
def xg_dt_backtrader(
self,
start_date: str = '20260701',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
dt_interval: int = 20,
dt_type: str = '金额',
dt_value: float = 1000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""定投回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, dt_interval=dt_interval,
dt_type=dt_type, dt_value=dt_value, sell_zdf=sell_zdf,
buy_zdf=buy_zdf, trade_value=trade_value, comm=comm
)
return self._request('/xg_dt_backtrader_1', params)
def xg_mom_backtrader(
self,
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
mom_type: str = '百分比',
mom_value: float = 1,
mom_daily: int = 25,
min_mom: float = 0,
max_mom: float = 5,
buy_rank: int = 1,
sell_zdf: float = 0.03,
sell_amount: float = 1000
) -> Dict[str, Any]:
"""动量回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
mom_type=mom_type, mom_value=mom_value, mom_daily=mom_daily,
min_mom=min_mom, max_mom=max_mom, buy_rank=buy_rank,
sell_zdf=sell_zdf, sell_amount=sell_amount
)
return self._request('/xg_mom_backtrader_1', params)
def xg_pz_backtrader(
self,
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
dt_type: str = '百分比',
weight_list: str = '0.4,0.4,0.2',
index_stock: str = '000300.SH',
cash: float = 100000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""资产配置回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
dt_type=dt_type, weight_list=weight_list, index_stock=index_stock,
cash=cash, sell_zdf=sell_zdf, buy_zdf=buy_zdf,
trade_value=trade_value, comm=comm
)
return self._request('/xg_pz_backtrader_1', params)
def xg_zcph_backtrader(
self,
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
dt_type: str = '百分比',
weight_list: str = '0.35,0.35,0.3',
deviation_list: str = '0.1,0.1,0.05',
interval: int = 20,
index_stock: str = '000300.SH',
cash: float = 100000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""资产配置平衡策略回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
dt_type=dt_type, weight_list=weight_list, deviation_list=deviation_list,
interval=interval, index_stock=index_stock, cash=cash,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value, comm=comm
)
return self._request('/xg_zcph_backtrader_1', params)
def xg_gd_backtrader(
self,
start_date: str = '20250701',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
gd_interval: int = 1,
gd_bc_type_list: str = '百分比,百分比',
gd_buy_bc_list: str = '0.03,0.02',
gd_sell_bc_list: str = '-0.02,-0.015',
gd_atr_ratio_list: str = '2.0,2.0',
gd_atr_period_list: str = '14,14',
gd_type_list: str = '金额,金额',
gd_value_list: str = '1000,1500',
init_position_ratio_list: str = '0.1,0.15',
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001,
max_workers: int = 4
) -> Dict[str, Any]:
"""网格策略回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, gd_interval=gd_interval,
gd_bc_type_list=gd_bc_type_list, gd_buy_bc_list=gd_buy_bc_list,
gd_sell_bc_list=gd_sell_bc_list, gd_atr_ratio_list=gd_atr_ratio_list,
gd_atr_period_list=gd_atr_period_list, gd_type_list=gd_type_list,
gd_value_list=gd_value_list, init_position_ratio_list=init_position_ratio_list,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value,
comm=comm, max_workers=max_workers
)
return self._request('/xg_gd_backtrader_1', params)
def xg_hg_backtrader(
self,
start_date: str = '20240101',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
entry_period: int = 20,
exit_period: int = 10,
n_period: int = 20,
risk_per_trade: float = 0.01,
risk_per_unit: float = 0.02,
max_units: int = 4,
add_unit_threshold: float = 0.5,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000
) -> Dict[str, Any]:
"""海龟策略回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, entry_period=entry_period,
exit_period=exit_period, n_period=n_period,
risk_per_trade=risk_per_trade, risk_per_unit=risk_per_unit,
max_units=max_units, add_unit_threshold=add_unit_threshold,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value
)
return self._request('/xg_hg_backtrader_1', params)
def xg_more_mom_backtrader(
self,
start_date: str = '20250101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
enable_index_timing: bool = False,
index_mean_line: int = 20,
index_not_trader: str = '513100.SH,518880.SH',
index_condition_type: str = '大于均线',
index_offset: float = 0.0,
mom_type: str = '百分比',
mom_value: float = 0.1,
mom_models: str = '动量1',
mom_daily: int = 25,
period: int = 20,
short_ma: int = 3,
long_ma: int = 20,
enable_mom_filter: bool = False,
max_value: float = 5,
mini_value: float = 0,
max_rank: int = 1,
min_rank: int = 2,
enable_buy_condition: bool = False,
enable_sell_condition: bool = False,
buy_condition_type: str = '涨幅',
buy_period: int = 20,
buy_period_ratio: float = 0.1,
buy_offset: float = 0.0,
sell_condition_type: str = '跌幅',
sell_period: int = 20,
sell_period_ratio: float = -0.1,
sell_offset: float = 0.0,
sell_zdf: float = 0.03,
sell_amount: float = 1000,
interval: int = 1
) -> Dict[str, Any]:
"""综合动量回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, enable_index_timing=enable_index_timing,
index_mean_line=index_mean_line, index_not_trader=index_not_trader,
index_condition_type=index_condition_type, index_offset=index_offset,
mom_type=mom_type, mom_value=mom_value, mom_models=mom_models,
mom_daily=mom_daily, period=period, short_ma=short_ma,
long_ma=long_ma, enable_mom_filter=enable_mom_filter,
max_value=max_value, mini_value=mini_value,
max_rank=max_rank, min_rank=min_rank,
enable_buy_condition=enable_buy_condition,
enable_sell_condition=enable_sell_condition,
buy_condition_type=buy_condition_type, buy_period=buy_period,
buy_period_ratio=buy_period_ratio, buy_offset=buy_offset,
sell_condition_type=sell_condition_type, sell_period=sell_period,
sell_period_ratio=sell_period_ratio, sell_offset=sell_offset,
sell_zdf=sell_zdf, sell_amount=sell_amount, interval=interval
)
return self._request('/xg_more_mom_backtrader_1', params)
def xg_condi_factor_backtrader(
self,
start_date: str = '20250101',
end_date: str = '20261201',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
min_commission: float = 0,
trader_type: str = '百分比',
trader_value: float = 0.5,
hold_stock_limit: int = 2,
is_open_user_factor: bool = True,
user_factor_list: str = 'close,high,low,open,amount,volume,zdf',
user_factor_cacal: str = '''{
"收盘价大于5日均线": "IF(df['close']>MA(df['close'],5),True,False)",
"均线评分": "IF(MA(df['close'],3)>MA(df['close'],5),25,0)+IF(MA(df['close'],5)>MA(df['close'],10),25,0)+IF(MA(df['close'],10)>MA(df['close'],20),25,0)+IF(MA(df['close'],20)>MA(df['close'],30),25,0)"
}''',
buy_condi_factor: str = '''{
"收盘价大于5日均线": {"选择类型": "and", "选择方向": "等于", "值": true},
"连续上涨天数": {"选择类型": "and", "选择方向": "大于", "值": 2}
}''',
rank_factor: str = '''{
"均线评分": "降序"
}''',
sell_condi_factor: str = '''{
"收盘价大于5日均线": {"选择类型": "or", "选择方向": "等于", "值": false},
"连续下跌天数": {"选择类型": "or", "选择方向": "大于", "值": 2}
}''',
sell_type: str = '金额',
sell_zdf: float = 0.03,
sell_value: float = 1000,
max_workers: int = 4,
interval: int = 1,
min_hold_days: int = 1,
risk_free_rate: float = 0.02,
slippage: float = 0,
enable_limit_up_down_filter: bool = True,
max_single_position_ratio: float = 1.0
) -> Dict[str, Any]:
"""条件因子回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
min_commission=min_commission, trader_type=trader_type,
trader_value=trader_value, hold_stock_limit=hold_stock_limit,
is_open_user_factor=is_open_user_factor,
user_factor_list=user_factor_list,
user_factor_cacal=user_factor_cacal,
buy_condi_factor=buy_condi_factor,
rank_factor=rank_factor,
sell_condi_factor=sell_condi_factor,
sell_type=sell_type, sell_zdf=sell_zdf, sell_value=sell_value,
max_workers=max_workers, interval=interval,
min_hold_days=min_hold_days, risk_free_rate=risk_free_rate,
slippage=slippage,
enable_limit_up_down_filter=enable_limit_up_down_filter,
max_single_position_ratio=max_single_position_ratio
)
return self._request('/xg_condi_factor_backtrader_1', params)
def xg_rank_factor_backtrader(
self,
start_date: str = '20250101',
end_date: str = '20261201',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
min_commission: float = 0,
trader_type: str = '百分比',
trader_value: float = 0.5,
hold_stock_limit: int = 2,
is_open_user_factor: bool = True,
user_factor_list: str = 'close,high,low,open,amount,volume,zdf',
user_factor_cacal: str = '''{
"收盘价大于5日均线": "IF(df['close']>MA(df['close'],5),0,1)",
"均线评分": "IF(MA(df['close'],3)>MA(df['close'],5),25,0)+IF(MA(df['close'],5)>MA(df['close'],10),25,0)+IF(MA(df['close'],10)>MA(df['close'],20),25,0)+IF(MA(df['close'],20)>MA(df['close'],30),25,0)"
}''',
is_open_buy_condi: bool = True,
buy_condi_factor: str = '''{
"25日回归动量": {"选择类型": "and", "选择方向": "大于", "值": 0},
"25日回归动量": {"选择类型": "and", "选择方向": "小于", "值": 5}
}''',
rank_factor: str = '''{
"25日回归动量": {"相关性": "正相关", "权重": 1}
}''',
total_factor_rank: str = '降序',
sell_type: str = '金额',
sell_zdf: float = 0.03,
sell_value: float = 1000,
max_workers: int = 4,
interval: int = 1,
min_hold_days: int = 1,
risk_free_rate: float = 0.02,
slippage: float = 0,
enable_limit_up_down_filter: bool = True,
max_single_position_ratio: float = 1.0
) -> Dict[str, Any]:
"""排序多因子回测接口"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
min_commission=min_commission, trader_type=trader_type,
trader_value=trader_value, hold_stock_limit=hold_stock_limit,
is_open_user_factor=is_open_user_factor,
user_factor_list=user_factor_list,
user_factor_cacal=user_factor_cacal,
is_open_buy_condi=is_open_buy_condi,
buy_condi_factor=buy_condi_factor,
rank_factor=rank_factor,
total_factor_rank=total_factor_rank,
sell_type=sell_type, sell_zdf=sell_zdf, sell_value=sell_value,
max_workers=max_workers, interval=interval,
min_hold_days=min_hold_days, risk_free_rate=risk_free_rate,
slippage=slippage,
enable_limit_up_down_filter=enable_limit_up_down_filter,
max_single_position_ratio=max_single_position_ratio
)
return self._request('/xg_rank_factor_backtrader_1', params)
# ============================================================
# 二、策略模拟交易接口（moni，不带 _1）
# ============================================================
def xg_dt_backtrader_moni(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260701',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
dt_interval: int = 20,
dt_type: str = '金额',
dt_value: float = 1000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""定投策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, dt_interval=dt_interval,
dt_type=dt_type, dt_value=dt_value, sell_zdf=sell_zdf,
buy_zdf=buy_zdf, trade_value=trade_value, comm=comm
)
return self._request('/xg_dt_backtrader_moni', params)
def xg_mom_backtrader_moni(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
mom_type: str = '百分比',
mom_value: float = 1,
mom_daily: int = 25,
min_mom: float = 0,
max_mom: float = 5,
buy_rank: int = 1,
sell_zdf: float = 0.03,
sell_amount: float = 1000
) -> Dict[str, Any]:
"""动量策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
mom_type=mom_type, mom_value=mom_value, mom_daily=mom_daily,
min_mom=min_mom, max_mom=max_mom, buy_rank=buy_rank,
sell_zdf=sell_zdf, sell_amount=sell_amount
)
return self._request('/xg_mom_backtrader_moni', params)
def xg_pz_backtrader_moni(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
dt_type: str = '百分比',
weight_list: str = '0.4,0.4,0.2',
index_stock: str = '000300.SH',
cash: float = 100000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""资产配置策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
dt_type=dt_type, weight_list=weight_list, index_stock=index_stock,
cash=cash, sell_zdf=sell_zdf, buy_zdf=buy_zdf,
trade_value=trade_value, comm=comm
)
return self._request('/xg_pz_backtrader_moni', params)
def xg_zcph_backtrader_moni(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
dt_type: str = '百分比',
weight_list: str = '0.35,0.35,0.3',
deviation_list: str = '0.1,0.1,0.05',
interval: int = 20,
index_stock: str = '000300.SH',
cash: float = 100000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""资产配置平衡策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
dt_type=dt_type, weight_list=weight_list, deviation_list=deviation_list,
interval=interval, index_stock=index_stock, cash=cash,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value, comm=comm
)
return self._request('/xg_zcph_backtrader_moni', params)
def xg_gd_backtrader_moni(
self,
st_name: str = '小果网格测试策略',
open_show: str = '是',
start_date: str = '20250701',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
gd_interval: int = 1,
gd_bc_type_list: str = '百分比,百分比',
gd_buy_bc_list: str = '0.03,0.02',
gd_sell_bc_list: str = '-0.02,-0.015',
gd_atr_ratio_list: str = '2.0,2.0',
gd_atr_period_list: str = '14,14',
gd_type_list: str = '金额,金额',
gd_value_list: str = '1000,1500',
init_position_ratio_list: str = '0.1,0.15',
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001,
max_workers: int = 4
) -> Dict[str, Any]:
"""网格策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, gd_interval=gd_interval,
gd_bc_type_list=gd_bc_type_list, gd_buy_bc_list=gd_buy_bc_list,
gd_sell_bc_list=gd_sell_bc_list, gd_atr_ratio_list=gd_atr_ratio_list,
gd_atr_period_list=gd_atr_period_list, gd_type_list=gd_type_list,
gd_value_list=gd_value_list,
init_position_ratio_list=init_position_ratio_list,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value,
comm=comm, max_workers=max_workers
)
return self._request('/xg_gd_backtrader_moni', params)
def xg_hg_backtrader_moni(
self,
st_name: str = '小果海龟测试策略',
open_show: str = '是',
start_date: str = '20240101',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
entry_period: int = 20,
exit_period: int = 10,
n_period: int = 20,
risk_per_trade: float = 0.01,
risk_per_unit: float = 0.02,
max_units: int = 4,
add_unit_threshold: float = 0.5,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000
) -> Dict[str, Any]:
"""海龟策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, entry_period=entry_period,
exit_period=exit_period, n_period=n_period,
risk_per_trade=risk_per_trade, risk_per_unit=risk_per_unit,
max_units=max_units, add_unit_threshold=add_unit_threshold,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value
)
return self._request('/xg_hg_backtrader_moni', params)
def xg_more_mom_backtrader_moni(
self,
st_name: str = '小果综合动量测试策略',
open_show: str = '是',
start_date: str = '20250101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
enable_index_timing: bool = False,
index_mean_line: int = 20,
index_not_trader: str = '513100.SH,518880.SH',
index_condition_type: str = '大于均线',
index_offset: float = 0.0,
mom_type: str = '百分比',
mom_value: float = 0.1,
mom_models: str = '动量1',
mom_daily: int = 25,
period: int = 20,
short_ma: int = 3,
long_ma: int = 20,
enable_mom_filter: bool = False,
max_value: float = 5,
mini_value: float = 0,
max_rank: int = 1,
min_rank: int = 2,
enable_buy_condition: bool = False,
enable_sell_condition: bool = False,
buy_condition_type: str = '涨幅',
buy_period: int = 20,
buy_period_ratio: float = 0.1,
buy_offset: float = 0.0,
sell_condition_type: str = '跌幅',
sell_period: int = 20,
sell_period_ratio: float = -0.1,
sell_offset: float = 0.0,
sell_zdf: float = 0.03,
sell_amount: float = 1000,
interval: int = 1
) -> Dict[str, Any]:
"""综合动量策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, enable_index_timing=enable_index_timing,
index_mean_line=index_mean_line, index_not_trader=index_not_trader,
index_condition_type=index_condition_type, index_offset=index_offset,
mom_type=mom_type, mom_value=mom_value, mom_models=mom_models,
mom_daily=mom_daily, period=period, short_ma=short_ma,
long_ma=long_ma, enable_mom_filter=enable_mom_filter,
max_value=max_value, mini_value=mini_value,
max_rank=max_rank, min_rank=min_rank,
enable_buy_condition=enable_buy_condition,
enable_sell_condition=enable_sell_condition,
buy_condition_type=buy_condition_type, buy_period=buy_period,
buy_period_ratio=buy_period_ratio, buy_offset=buy_offset,
sell_condition_type=sell_condition_type, sell_period=sell_period,
sell_period_ratio=sell_period_ratio, sell_offset=sell_offset,
sell_zdf=sell_zdf, sell_amount=sell_amount, interval=interval
)
return self._request('/xg_more_mom_backtrader_moni', params)
def xg_condi_factor_backtrader_moni(
self,
st_name: str = '小果条件因子测试策略',
open_show: str = '是',
start_date: str = '20250101',
end_date: str = '20261201',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
min_commission: float = 0,
trader_type: str = '百分比',
trader_value: float = 0.5,
hold_stock_limit: int = 2,
is_open_user_factor: bool = True,
user_factor_list: str = 'close,high,low,open,amount,volume,zdf',
user_factor_cacal: str = '''{
"收盘价大于5日均线": "IF(df['close']>MA(df['close'],5),True,False)",
"均线评分": "IF(MA(df['close'],3)>MA(df['close'],5),25,0)+IF(MA(df['close'],5)>MA(df['close'],10),25,0)+IF(MA(df['close'],10)>MA(df['close'],20),25,0)+IF(MA(df['close'],20)>MA(df['close'],30),25,0)"
}''',
buy_condi_factor: str = '''{
"收盘价大于5日均线": {"选择类型": "and", "选择方向": "等于", "值": true},
"连续上涨天数": {"选择类型": "and", "选择方向": "大于", "值": 2}
}''',
rank_factor: str = '''{
"均线评分": "降序"
}''',
sell_condi_factor: str = '''{
"收盘价大于5日均线": {"选择类型": "or", "选择方向": "等于", "值": false},
"连续下跌天数": {"选择类型": "or", "选择方向": "大于", "值": 2}
}''',
sell_type: str = '金额',
sell_zdf: float = 0.03,
sell_value: float = 1000,
max_workers: int = 4,
interval: int = 1,
min_hold_days: int = 1,
risk_free_rate: float = 0.02,
slippage: float = 0,
enable_limit_up_down_filter: bool = True,
max_single_position_ratio: float = 1.0
) -> Dict[str, Any]:
"""条件多因子策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
min_commission=min_commission, trader_type=trader_type,
trader_value=trader_value, hold_stock_limit=hold_stock_limit,
is_open_user_factor=is_open_user_factor,
user_factor_list=user_factor_list,
user_factor_cacal=user_factor_cacal,
buy_condi_factor=buy_condi_factor,
rank_factor=rank_factor,
sell_condi_factor=sell_condi_factor,
sell_type=sell_type, sell_zdf=sell_zdf, sell_value=sell_value,
max_workers=max_workers, interval=interval,
min_hold_days=min_hold_days, risk_free_rate=risk_free_rate,
slippage=slippage,
enable_limit_up_down_filter=enable_limit_up_down_filter,
max_single_position_ratio=max_single_position_ratio
)
return self._request('/xg_condi_factor_backtrader_moni', params)
def xg_rank_factor_backtrader_moni(
self,
st_name: str = '小果排序多因子模拟策略',
open_show: str = '是',
start_date: str = '20250101',
end_date: str = '20261201',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
min_commission: float = 0,
trader_type: str = '百分比',
trader_value: float = 0.5,
hold_stock_limit: int = 2,
is_open_user_factor: bool = True,
user_factor_list: str = 'close,high,low,open,amount,volume,zdf',
user_factor_cacal: str = '''{
"收盘价大于5日均线": "IF(df['close']>MA(df['close'],5),0,1)",
"均线评分": "IF(MA(df['close'],3)>MA(df['close'],5),25,0)+IF(MA(df['close'],5)>MA(df['close'],10),25,0)+IF(MA(df['close'],10)>MA(df['close'],20),25,0)+IF(MA(df['close'],20)>MA(df['close'],30),25,0)"
}''',
is_open_buy_condi: bool = True,
buy_condi_factor: str = '''{
"25日回归动量": {"选择类型": "and", "选择方向": "大于", "值": 0},
"25日回归动量": {"选择类型": "and", "选择方向": "小于", "值": 5}
}''',
rank_factor: str = '''{
"25日回归动量": {"相关性": "正相关", "权重": 1}
}''',
total_factor_rank: str = '降序',
sell_type: str = '金额',
sell_zdf: float = 0.03,
sell_value: float = 1000,
max_workers: int = 4,
interval: int = 1,
min_hold_days: int = 1,
risk_free_rate: float = 0.02,
slippage: float = 0,
enable_limit_up_down_filter: bool = True,
max_single_position_ratio: float = 1.0
) -> Dict[str, Any]:
"""排序多因子策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
min_commission=min_commission, trader_type=trader_type,
trader_value=trader_value, hold_stock_limit=hold_stock_limit,
is_open_user_factor=is_open_user_factor,
user_factor_list=user_factor_list,
user_factor_cacal=user_factor_cacal,
is_open_buy_condi=is_open_buy_condi,
buy_condi_factor=buy_condi_factor,
rank_factor=rank_factor,
total_factor_rank=total_factor_rank,
sell_type=sell_type, sell_zdf=sell_zdf, sell_value=sell_value,
max_workers=max_workers, interval=interval,
min_hold_days=min_hold_days, risk_free_rate=risk_free_rate,
slippage=slippage,
enable_limit_up_down_filter=enable_limit_up_down_filter,
max_single_position_ratio=max_single_position_ratio
)
return self._request('/xg_rank_factor_backtrader_moni', params)
# ============================================================
# 三、社区策略接口（moni_sq，不带 _1）
# ============================================================
def xg_dt_backtrader_moni_sq(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260701',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
dt_interval: int = 20,
dt_type: str = '金额',
dt_value: float = 1000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""定投策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, dt_interval=dt_interval,
dt_type=dt_type, dt_value=dt_value, sell_zdf=sell_zdf,
buy_zdf=buy_zdf, trade_value=trade_value, comm=comm
)
return self._request('/xg_dt_backtrader_moni_sq', params)
def xg_mom_backtrader_moni_sq(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
mom_type: str = '百分比',
mom_value: float = 1,
mom_daily: int = 25,
min_mom: float = 0,
max_mom: float = 5,
buy_rank: int = 1,
sell_zdf: float = 0.03,
sell_amount: float = 1000
) -> Dict[str, Any]:
"""动量策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
mom_type=mom_type, mom_value=mom_value, mom_daily=mom_daily,
min_mom=min_mom, max_mom=max_mom, buy_rank=buy_rank,
sell_zdf=sell_zdf, sell_amount=sell_amount
)
return self._request('/xg_mom_backtrader_moni_sq', params)
def xg_pz_backtrader_moni_sq(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
dt_type: str = '百分比',
weight_list: str = '0.4,0.4,0.2',
index_stock: str = '000300.SH',
cash: float = 100000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""资产配置策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
dt_type=dt_type, weight_list=weight_list, index_stock=index_stock,
cash=cash, sell_zdf=sell_zdf, buy_zdf=buy_zdf,
trade_value=trade_value, comm=comm
)
return self._request('/xg_pz_backtrader_moni_sq', params)
def xg_zcph_backtrader_moni_sq(
self,
st_name: str = '小果测试',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
dt_type: str = '百分比',
weight_list: str = '0.35,0.35,0.3',
deviation_list: str = '0.1,0.1,0.05',
interval: int = 20,
index_stock: str = '000300.SH',
cash: float = 100000,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001
) -> Dict[str, Any]:
"""资产配置平衡策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
dt_type=dt_type, weight_list=weight_list, deviation_list=deviation_list,
interval=interval, index_stock=index_stock, cash=cash,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value, comm=comm
)
return self._request('/xg_zcph_backtrader_moni_sq', params)
def xg_gd_backtrader_moni_sq(
self,
st_name: str = '小果网格测试策略',
open_show: str = '是',
start_date: str = '20250701',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
gd_interval: int = 1,
gd_bc_type_list: str = '百分比,百分比',
gd_buy_bc_list: str = '0.03,0.02',
gd_sell_bc_list: str = '-0.02,-0.015',
gd_atr_ratio_list: str = '2.0,2.0',
gd_atr_period_list: str = '14,14',
gd_type_list: str = '金额,金额',
gd_value_list: str = '1000,1500',
init_position_ratio_list: str = '0.1,0.15',
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000,
comm: float = 0.0001,
max_workers: int = 4
) -> Dict[str, Any]:
"""网格策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, gd_interval=gd_interval,
gd_bc_type_list=gd_bc_type_list, gd_buy_bc_list=gd_buy_bc_list,
gd_sell_bc_list=gd_sell_bc_list, gd_atr_ratio_list=gd_atr_ratio_list,
gd_atr_period_list=gd_atr_period_list, gd_type_list=gd_type_list,
gd_value_list=gd_value_list,
init_position_ratio_list=init_position_ratio_list,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value,
comm=comm, max_workers=max_workers
)
return self._request('/xg_gd_backtrader_moni_sq', params)
def xg_hg_backtrader_moni_sq(
self,
st_name: str = '小果海龟测试策略',
open_show: str = '是',
start_date: str = '20240101',
end_date: str = '20500101',
stock_list: str = '513100.SH,513500.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
entry_period: int = 20,
exit_period: int = 10,
n_period: int = 20,
risk_per_trade: float = 0.01,
risk_per_unit: float = 0.02,
max_units: int = 4,
add_unit_threshold: float = 0.5,
sell_zdf: float = 0.03,
buy_zdf: float = -0.03,
trade_value: float = 1000
) -> Dict[str, Any]:
"""海龟策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, entry_period=entry_period,
exit_period=exit_period, n_period=n_period,
risk_per_trade=risk_per_trade, risk_per_unit=risk_per_unit,
max_units=max_units, add_unit_threshold=add_unit_threshold,
sell_zdf=sell_zdf, buy_zdf=buy_zdf, trade_value=trade_value
)
return self._request('/xg_hg_backtrader_moni_sq', params)
def xg_more_mom_backtrader_moni_sq(
self,
st_name: str = '小果综合动量测试策略',
open_show: str = '是',
start_date: str = '20250101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
enable_index_timing: bool = False,
index_mean_line: int = 20,
index_not_trader: str = '513100.SH,518880.SH',
index_condition_type: str = '大于均线',
index_offset: float = 0.0,
mom_type: str = '百分比',
mom_value: float = 0.1,
mom_models: str = '动量1',
mom_daily: int = 25,
period: int = 20,
short_ma: int = 3,
long_ma: int = 20,
enable_mom_filter: bool = False,
max_value: float = 5,
mini_value: float = 0,
max_rank: int = 1,
min_rank: int = 2,
enable_buy_condition: bool = False,
enable_sell_condition: bool = False,
buy_condition_type: str = '涨幅',
buy_period: int = 20,
buy_period_ratio: float = 0.1,
buy_offset: float = 0.0,
sell_condition_type: str = '跌幅',
sell_period: int = 20,
sell_period_ratio: float = -0.1,
sell_offset: float = 0.0,
sell_zdf: float = 0.03,
sell_amount: float = 1000,
interval: int = 1
) -> Dict[str, Any]:
"""综合动量策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, enable_index_timing=enable_index_timing,
index_mean_line=index_mean_line, index_not_trader=index_not_trader,
index_condition_type=index_condition_type, index_offset=index_offset,
mom_type=mom_type, mom_value=mom_value, mom_models=mom_models,
mom_daily=mom_daily, period=period, short_ma=short_ma,
long_ma=long_ma, enable_mom_filter=enable_mom_filter,
max_value=max_value, mini_value=mini_value,
max_rank=max_rank, min_rank=min_rank,
enable_buy_condition=enable_buy_condition,
enable_sell_condition=enable_sell_condition,
buy_condition_type=buy_condition_type, buy_period=buy_period,
buy_period_ratio=buy_period_ratio, buy_offset=buy_offset,
sell_condition_type=sell_condition_type, sell_period=sell_period,
sell_period_ratio=sell_period_ratio, sell_offset=sell_offset,
sell_zdf=sell_zdf, sell_amount=sell_amount, interval=interval
)
return self._request('/xg_more_mom_backtrader_moni_sq', params)
def xg_condi_factor_backtrader_moni_sq(
self,
st_name: str = '小果条件因子测试策略',
open_show: str = '是',
start_date: str = '20250101',
end_date: str = '20261201',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
min_commission: float = 0,
trader_type: str = '百分比',
trader_value: float = 0.5,
hold_stock_limit: int = 2,
is_open_user_factor: bool = True,
user_factor_list: str = 'close,high,low,open,amount,volume,zdf',
user_factor_cacal: str = '''{
"收盘价大于5日均线": "IF(df['close']>MA(df['close'],5),True,False)",
"均线评分": "IF(MA(df['close'],3)>MA(df['close'],5),25,0)+IF(MA(df['close'],5)>MA(df['close'],10),25,0)+IF(MA(df['close'],10)>MA(df['close'],20),25,0)+IF(MA(df['close'],20)>MA(df['close'],30),25,0)"
}''',
buy_condi_factor: str = '''{
"收盘价大于5日均线": {"选择类型": "and", "选择方向": "等于", "值": true},
"连续上涨天数": {"选择类型": "and", "选择方向": "大于", "值": 2}
}''',
rank_factor: str = '''{
"均线评分": "降序"
}''',
sell_condi_factor: str = '''{
"收盘价大于5日均线": {"选择类型": "or", "选择方向": "等于", "值": false},
"连续下跌天数": {"选择类型": "or", "选择方向": "大于", "值": 2}
}''',
sell_type: str = '金额',
sell_zdf: float = 0.03,
sell_value: float = 1000,
max_workers: int = 4,
interval: int = 1,
min_hold_days: int = 1,
risk_free_rate: float = 0.02,
slippage: float = 0,
enable_limit_up_down_filter: bool = True,
max_single_position_ratio: float = 1.0
) -> Dict[str, Any]:
"""条件多因子策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
min_commission=min_commission, trader_type=trader_type,
trader_value=trader_value, hold_stock_limit=hold_stock_limit,
is_open_user_factor=is_open_user_factor,
user_factor_list=user_factor_list,
user_factor_cacal=user_factor_cacal,
buy_condi_factor=buy_condi_factor,
rank_factor=rank_factor,
sell_condi_factor=sell_condi_factor,
sell_type=sell_type, sell_zdf=sell_zdf, sell_value=sell_value,
max_workers=max_workers, interval=interval,
min_hold_days=min_hold_days, risk_free_rate=risk_free_rate,
slippage=slippage,
enable_limit_up_down_filter=enable_limit_up_down_filter,
max_single_position_ratio=max_single_position_ratio
)
return self._request('/xg_condi_factor_backtrader_moni_sq', params)
def xg_rank_factor_backtrader_moni_sq(
self,
st_name: str = '小果排序多因子社区策略',
open_show: str = '是',
start_date: str = '20250101',
end_date: str = '20261201',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
min_commission: float = 0,
trader_type: str = '百分比',
trader_value: float = 0.5,
hold_stock_limit: int = 2,
is_open_user_factor: bool = True,
user_factor_list: str = 'close,high,low,open,amount,volume,zdf',
user_factor_cacal: str = '''{
"收盘价大于5日均线": "IF(df['close']>MA(df['close'],5),0,1)",
"均线评分": "IF(MA(df['close'],3)>MA(df['close'],5),25,0)+IF(MA(df['close'],5)>MA(df['close'],10),25,0)+IF(MA(df['close'],10)>MA(df['close'],20),25,0)+IF(MA(df['close'],20)>MA(df['close'],30),25,0)"
}''',
is_open_buy_condi: bool = True,
buy_condi_factor: str = '''{
"25日回归动量": {"选择类型": "and", "选择方向": "大于", "值": 0},
"25日回归动量": {"选择类型": "and", "选择方向": "小于", "值": 5}
}''',
rank_factor: str = '''{
"25日回归动量": {"相关性": "正相关", "权重": 1}
}''',
total_factor_rank: str = '降序',
sell_type: str = '金额',
sell_zdf: float = 0.03,
sell_value: float = 1000,
max_workers: int = 4,
interval: int = 1,
min_hold_days: int = 1,
risk_free_rate: float = 0.02,
slippage: float = 0,
enable_limit_up_down_filter: bool = True,
max_single_position_ratio: float = 1.0
) -> Dict[str, Any]:
"""排序多因子策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
min_commission=min_commission, trader_type=trader_type,
trader_value=trader_value, hold_stock_limit=hold_stock_limit,
is_open_user_factor=is_open_user_factor,
user_factor_list=user_factor_list,
user_factor_cacal=user_factor_cacal,
is_open_buy_condi=is_open_buy_condi,
buy_condi_factor=buy_condi_factor,
rank_factor=rank_factor,
total_factor_rank=total_factor_rank,
sell_type=sell_type, sell_zdf=sell_zdf, sell_value=sell_value,
max_workers=max_workers, interval=interval,
min_hold_days=min_hold_days, risk_free_rate=risk_free_rate,
slippage=slippage,
enable_limit_up_down_filter=enable_limit_up_down_filter,
max_single_position_ratio=max_single_position_ratio
)
return self._request('/xg_rank_factor_backtrader_moni_sq', params)
# ============================================================
# 四、数据读取接口
# ============================================================
def get_moni_trader_data(
self,
user: str = '小果',
st_type: str = '动量策略',
st_name: str = '小果动量模拟策略'
) -> Dict[str, Any]:
"""读取模拟交易的统计数据"""
params = self._get_params(user=user,st_type=st_type, st_name=st_name)
return self._request('/get_moni_trader_data', params)
def get_moni_trader_data_sq(
self,
user: str = '小果',
st_type: str = '动量策略',
st_name: str = '小果动量模拟策略'
) -> Dict[str, Any]:
"""读取社区交易的统计数据"""
params = self._get_params(user=user,st_type=st_type, st_name=st_name)
return self._request('/get_moni_trader_data_sq', params)
def get_stock_hist_data(
self,
stock: str = '513100.SH',
start_date: str = '20200101',
end_date: str = '20261231'
) -> Dict[str, Any]:
"""读取标的历史行情数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date
)
return self._request('/get_stock_hist_data', params)
def get_stock_factor_data(
self,
stock: str = '513100.SH',
start_date: str = '20200101',
end_date: str = '20261231',
columns: str = 'date,close,open,high,low,volume,amount'
) -> Dict[str, Any]:
"""读取标的因子数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
columns=columns
)
return self._request('/get_stock_factor_data', params)
def get_stock_finance_data(
self,
table: str = '资产负债表',
date: str = '2026-06-30',
columns: str = 'secu_code,end_date,total_assets'
) -> Dict[str, Any]:
"""读取股票财务数据"""
params = self._get_params(
table=table,
date=date,
columns=columns
)
return self._request('/get_stock_finance_data', params)
# ============================================================
# 四、策略删除接口（单个）
# ============================================================
def del_moni_trader_data(
self,
user: str = '小果',
st_type: str = '定投策略',
st_name: str = '小果定投模拟策略公开',
open_show: str = '是'
) -> Dict[str, Any]:
"""删除模拟策略数据"""
params = self._get_params(
user=user,
st_type=st_type,
st_name=st_name,
open_show=open_show
)
return self._request('/del_moni_trader_data', params)
def del_moni_trader_data_sq(
self,
user: str = '小果',
st_type: str = '定投策略',
st_name: str = '小果定投模拟策略公开',
open_show: str = '是'
) -> Dict[str, Any]:
"""删除社区策略数据"""
params = self._get_params(
user=user,
st_type=st_type,
st_name=st_name,
open_show=open_show
)
return self._request('/del_moni_trader_data_sq', params)
# ============================================================
# 五、批量策略管理接口
# ============================================================
def del_all_moni_trader_data(
self,
user: str = '小果',
confirm: str = '是'
) -> Dict[str, Any]:
"""删除全部模拟策略数据"""
params = self._get_params(
user=user,
confirm=confirm
)
return self._request('/del_all_moni_trader_data', params)
def del_all_moni_trader_data_sq(
self,
user: str = '小果',
confirm: str = '是'
) -> Dict[str, Any]:
"""删除全部社区策略数据"""
params = self._get_params(
user=user,
confirm=confirm
)
return self._request('/del_all_moni_trader_data_sq', params)
def get_all_moni_trader_data(
self,
user: str = '小果'
) -> Dict[str, Any]:
"""读取个人模拟全部策略"""
params = self._get_params(user=user)
return self._request('/get_all_moni_trader_data', params)
def get_all_moni_trader_data_sq(
self,
user: str = '小果'
) -> Dict[str, Any]:
"""读取个人社区全部策略"""
params = self._get_params(user=user)
return self._request('/get_all_moni_trader_data_sq', params)
# ============================================================
# 六、策略执行接口
# ============================================================
def xg_condi_factor_backtrader_run(
self,
st_name: str = '小果条件因子测试策略',
force_rerun: bool = False,
save_data: bool = True
) -> Dict[str, Any]:
"""条件多因子策略回测执行接口"""
params = self._get_params(
st_name=st_name,
force_rerun=force_rerun,
save_data=save_data
)
return self._request('/xg_condi_factor_backtrader_run', params)
############################新添加模型**************************
# ============================================================
# 七、均值方差策略接口
# ============================================================
def xg_mean_var_backtrader(
self,
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
lookback_days: int = 60,
max_weight: float = 0.6,
min_weight: float = 0.05,
lambda_risk: float = 2.0,
interval: int = 5
) -> Dict[str, Any]:
"""均值方差最优资产组合权重再平衡策略回测"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, lookback_days=lookback_days,
max_weight=max_weight, min_weight=min_weight,
lambda_risk=lambda_risk, interval=interval
)
return self._request('/xg_mean_var_backtrader_1', params)
def xg_mean_var_backtrader_moni(
self,
st_name: str = '小果均值方差策略',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
lookback_days: int = 60,
max_weight: float = 0.6,
min_weight: float = 0.05,
lambda_risk: float = 2.0,
interval: int = 5
) -> Dict[str, Any]:
"""均值方差最优资产组合策略模拟交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, lookback_days=lookback_days,
max_weight=max_weight, min_weight=min_weight,
lambda_risk=lambda_risk, interval=interval
)
return self._request('/xg_mean_var_backtrader_moni', params)
def xg_mean_var_backtrader_moni_sq(
self,
st_name: str = '小果均值方差社区策略',
open_show: str = '是',
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
index_stock: str = '000300.SH',
cash: float = 100000,
comm: float = 0.0001,
max_workers: int = 4,
lookback_days: int = 60,
max_weight: float = 0.6,
min_weight: float = 0.05,
lambda_risk: float = 2.0,
interval: int = 5
) -> Dict[str, Any]:
"""均值方差最优资产组合策略社区交易接口"""
params = self._get_params(
st_name=st_name, open_show=open_show,
start_date=start_date, end_date=end_date, stock_list=stock_list,
index_stock=index_stock, cash=cash, comm=comm,
max_workers=max_workers, lookback_days=lookback_days,
max_weight=max_weight, min_weight=min_weight,
lambda_risk=lambda_risk, interval=interval
)
return self._request('/xg_mean_var_backtrader_moni_sq', params)
# ============================================================
# 八、多标的量化分析接口
# ============================================================
def xg_stock_cov_correlation(
self,
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
max_workers: int = 4,
method: str = 'pearson',
risk_free_rate: float = 0.03
) -> Dict[str, Any]:
"""多标的收益率相关性矩阵"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
max_workers=max_workers, method=method,
risk_free_rate=risk_free_rate
)
return self._request('/xg_stock_cov_correlation', params)
def xg_stock_cov_covariance(
self,
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
max_workers: int = 4,
method: str = 'pearson',
risk_free_rate: float = 0.03,
annualized: bool = True
) -> Dict[str, Any]:
"""多标的收益率协方差矩阵"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
max_workers=max_workers, method=method,
risk_free_rate=risk_free_rate, annualized=annualized
)
return self._request('/xg_stock_cov_covariance', params)
def xg_stock_cov_portfolio(
self,
start_date: str = '20260101',
end_date: str = '20500101',
stock_list: str = '159915.SZ,513100.SH,518880.SH',
max_workers: int = 4,
method: str = 'pearson',
risk_free_rate: float = 0.03,
target_return: Optional[float] = None
) -> Dict[str, Any]:
"""多标的投资组合优化"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
max_workers=max_workers, method=method,
risk_free_rate=risk_free_rate, target_return=target_return
)
return self._request('/xg_stock_cov_portfolio', params)
# ============================================================
# 九、股票组合分析接口
# ============================================================
def xg_stock_analysis(
self,
start_date: str = '20240101',
end_date: str = '20261231',
stock_list: str = '159915.SZ,518880.SH,510300.SH',
stock_weight: str = '0.4,0.3,0.3',
index_stock: str = '000300.SH',
max_workers: int = 4,
risk_free_rate: float = 0.03
) -> Dict[str, Any]:
"""小果股票分析系统 - 组合收益分析"""
params = self._get_params(
start_date=start_date, end_date=end_date, stock_list=stock_list,
stock_weight=stock_weight, index_stock=index_stock,
max_workers=max_workers, risk_free_rate=risk_free_rate
)
return self._request('/xg_stock_analysis', params)
# ============================================================
# 十、用户认证接口
# ============================================================
def get_user_info(
self,
user: str = '小果'
) -> Dict[str, Any]:
"""获取用户信息"""
params = self._get_params(user=user)
return self._request('/get_user_info', params)
def check_password_is_av_user(
self,
user: str = '小果'
) -> Dict[str, Any]:
"""检查授权码有效性"""
params = self._get_params(user=user)
return self._request('/check_password_is_av_user', params)
# ============================================================
# 十一、数据查询接口（AKShare/数据库API）
# ============================================================
def get_wencai_data(
self,
query: str = '今日涨停'
) -> Dict[str, Any]:
"""获取问财数据"""
params = self._get_params(query=query)
return self._request('/get_wencai_data', params)
def get_user_def_data(
self,
name: str = 'df',
func: str = '''
import akshare as ak
df = ak.stock_info_a_code_name()
print(df)
'''
) -> Dict[str, Any]:
"""获取自定义数据"""
params = self._get_params(name=name, func=func)
return self._request('/get_user_def_data', params)
def get_user_base_data(
self,
file_path: str = '/xg_data/全市场股票/',
file_name: str = '全市场股票'
) -> Dict[str, Any]:
"""获取数据库的数据"""
params = self._get_params(file_path=file_path, file_name=file_name)
return self._request('/get_user_base_data', params)
# ============================================================
# 十二、Tick/分钟数据接口
# ============================================================
def get_mini_data_5(
self,
stock: str = 'sh.600031',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = '5',
adjustflag: str = '2'
) -> Dict[str, Any]:
"""读取5分钟数据（mini）"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag
)
return self._request('/get_mini_data_5', params)
def get_mini_data_15(
self,
stock: str = 'sh.600031',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = '15',
adjustflag: str = '2'
) -> Dict[str, Any]:
"""读取15分钟数据（mini）"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag
)
return self._request('/get_mini_data_15', params)
def get_mini_data_30(
self,
stock: str = 'sh.600031',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = '30',
adjustflag: str = '2'
) -> Dict[str, Any]:
"""读取30分钟数据（mini）"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag
)
return self._request('/get_mini_data_30', params)
def get_mini_data_60(
self,
stock: str = 'sh.600031',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = '60',
adjustflag: str = '2'
) -> Dict[str, Any]:
"""读取60分钟数据（mini）"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag
)
return self._request('/get_mini_data_60', params)
# ============================================================
# 十三、K线数据接口
# ============================================================
def query_history_k_data_plus_d(
self,
stock: str = 'sh.600031',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = 'd',
adjustflag: str = '2'
) -> Dict[str, Any]:
"""日线数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag
)
return self._request('/query_history_k_data_plus_d', params)
def query_history_k_data_plus_w(
self,
stock: str = 'sh.600031',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = 'w',
adjustflag: str = '2'
) -> Dict[str, Any]:
"""周线数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag
)
return self._request('/query_history_k_data_plus_w', params)
def query_history_k_data_plus_m(
self,
stock: str = 'sh.600031',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = 'm',
adjustflag: str = '2'
) -> Dict[str, Any]:
"""月线数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag
)
return self._request('/query_history_k_data_plus_m', params)
# ============================================================
# 十四、指数K线数据接口
# ============================================================
def query_history_k_data_plus_index_d(
self,
stock: str = 'sh.000001',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = 'd'
) -> Dict[str, Any]:
"""指数日线数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency
)
return self._request('/query_history_k_data_plus_index_d', params)
def query_history_k_data_plus_index_w(
self,
stock: str = 'sh.000001',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = 'w'
) -> Dict[str, Any]:
"""指数周线数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency
)
return self._request('/query_history_k_data_plus_index_w', params)
def query_history_k_data_plus_index_m(
self,
stock: str = 'sh.000001',
start_date: str = '2026-04-01',
end_date: str = '2050-12-31',
frequency: str = 'm'
) -> Dict[str, Any]:
"""指数月线数据"""
params = self._get_params(
stock=stock,
start_date=start_date,
end_date=end_date,
frequency=frequency
)
return self._request('/query_history_k_data_plus_index_m', params)
# ============================================================
# 十五、财务数据接口
# ============================================================
def query_profit_data(
self,
code: str = 'sh.600031',
year: str = '2025',
quarter: str = '1'
) -> Dict[str, Any]:
"""盈利能力"""
params = self._get_params(
code=code,
year=year,
quarter=quarter
)
return self._request('/query_profit_data', params)
def query_operation_data(
self,
code: str = 'sh.600031',
year: str = '2025',
quarter: str = '1'
) -> Dict[str, Any]:
"""营运能力"""
params = self._get_params(
code=code,
year=year,
quarter=quarter
)
return self._request('/query_operation_data', params)
def query_growth_data(
self,
code: str = 'sh.600031',
year: str = '2026',
quarter: str = '1'
) -> Dict[str, Any]:
"""季频成长能力"""
params = self._get_params(
code=code,
year=year,
quarter=quarter
)
return self._request('/query_growth_data', params)
def query_balance_data(
self,
code: str = 'sh.600031',
year: str = '2026',
quarter: str = '1'
) -> Dict[str, Any]:
"""季频偿债能力"""
params = self._get_params(
code=code,
year=year,
quarter=quarter
)
return self._request('/query_balance_data', params)
def query_cash_flow_data(
self,
code: str = 'sh.600031',
year: str = '2026',
quarter: str = '1'
) -> Dict[str, Any]:
"""季频现金流量"""
params = self._get_params(
code=code,
year=year,
quarter=quarter
)
return self._request('/query_cash_flow_data', params)
def query_dupont_data(
self,
code: str = 'sh.600031',
year: str = '2026',
quarter: str = '1'
) -> Dict[str, Any]:
"""季频杜邦指数"""
params = self._get_params(
code=code,
year=year,
quarter=quarter
)
return self._request('/query_dupont_data', params)
# ============================================================
# 五、系统接口
# ============================================================
def root(self) -> Dict[str, Any]:
"""根路径"""
return self._request('/', {})
def health(self) -> Dict[str, Any]:
"""健康检查"""
return self._request('/health', {})
# ============================================================
# 测试代码
# ============================================================
if __name__ == "__main__":
print("=" * 60)
print("🚀 小果量化数据API测试")
print("=" * 60)
# 初始化客户端（使用您提供的服务器地址）
client = xg_quant_backtrader_data(
url="数据库服务器",
port=数据库端口,
user="自己名称",
password="自己密码",
auth_code="自己的token"
)
print("\n" + "=" * 60)
print("📋 一、系统接口测试")
print("=" * 60)
#因子数据
df=client.get_stock_factor_data(columns='date,证券代码,5日涨跌幅')
df=client._to_dataframe(df)
print(df)
#股票数据
df=client.get_stock_hist_data()
df=client._to_dataframe(df)
print(df)
#财务数据
df=client.get_stock_finance_data()
df=client._to_dataframe(df)
print(df)
全部的因子
{ "5日涨跌幅": "cacal_zdf(n=5)", "10日涨跌幅": "cacal_zdf(n=10)", "20日涨跌幅": "cacal_zdf(n=20)", "30日涨跌幅": "cacal_zdf(n=30)", "60日涨跌幅": "cacal_zdf(n=60)", "120日涨跌幅": "cacal_zdf(n=120)", "六脉神剑":"six_pulse_excalibur_hist()", "小波段交易":"small_fruit_band_trading_1()", "大波段交易":"small_fruit_band_trading_2()", "波段超级买卖":"band_supe_buy_sell()", "价格距离5日均线涨跌幅":"cacal_price_line_zdf(n=5)", "价格距离10日均线涨跌幅":"cacal_price_line_zdf(n=10)", "价格距离20日均线涨跌幅":"cacal_price_line_zdf(n=20)", "价格距离30日均线涨跌幅":"cacal_price_line_zdf(n=30)", "价格距离60日均线涨跌幅":"cacal_price_line_zdf(n=60)", "价格距离120日均线涨跌幅":"cacal_price_line_zdf(n=120)", "5日均线距离10日均线涨跌幅":"cacal_line_line_zdf(n1=5,n2=10)", "10日均线距离20日均线涨跌幅":"cacal_line_line_zdf(n1=10,n2=20)", "20日均线距离30日均线涨跌幅":"cacal_line_line_zdf(n1=20,n2=30)", "30日均线距离60日均线涨跌幅":"cacal_line_line_zdf(n1=30,n2=60)", "60日均线距离120日均线涨跌幅":"cacal_line_line_zdf(n1=60,n2=120)", "5日偏度":"cacal_skew(n=5)", "10日偏度":"cacal_skew(n=10)", "20日偏度":"cacal_skew(n=20)", "30日偏度":"cacal_skew(n=30)", "60日偏度":"cacal_skew(n=60)", "120日偏度":"cacal_skew(n=120)", "5日峰度":"cacal_kurt(n=5)", "10日峰度":"cacal_kurt(n=10)", "20日峰度":"cacal_kurt(n=20)", "30日峰度":"cacal_kurt(n=30)", "60日峰度":"cacal_kurt(n=60)", "120日峰度":"cacal_kurt(n=120)", "KDJ_KD金叉": "KDJ_KD金叉()", "KDJ_KD死叉": "KDJ_KD死叉()", "RSI_金叉": "RSI_金叉()", "RSI_死叉": "RSI_死叉()", "WR_金叉": "WR_金叉()", "MACD_金叉": "MACD_金叉()", "MACD_死叉": "MACD_死叉()", "PSY_金叉": "PSY_金叉()", "PSY_死叉": "PSY_死叉()", "5日均线": "SMA(period=5)", "10日均线": "SMA(period=10)", "20日均线": "SMA(period=20)", "30日均线": "SMA(period=30)", "60日均线": "SMA(period=60)", "120日均线": "SMA(period=120)", "5日10日金叉": "CROSS_UP(n1=5,n2=10)", "10日20日金叉": "CROSS_UP(n1=10,n2=20)", "20日30日金叉": "CROSS_UP(n1=20,n2=30)", "30日60日金叉": "CROSS_UP(n1=30,n2=60)", "60日120日金叉": "CROSS_UP(n1=60,n2=120)", "5日10日死叉": "CROSS_DOWN(n1=10,n2=5)", "10日20日死叉": "CROSS_DOWN(n1=20,n2=10)", "20日30日死叉": "CROSS_DOWN(n1=30,n2=20)", "30日60日死叉": "CROSS_DOWN(n1=60,n2=30)", "60日120日死叉": "CROSS_DOWN(n1=120,n2=60)", "连续上涨天数": "BARSLASTCOUNT_UP()", "连续下跌天数": "BARSLASTCOUNT_DOWN()", "价格在5均线上": "PRICE_MA_LINE_ANAL(n=5)", "价格在10均线上": "PRICE_MA_LINE_ANAL(n=10)", "价格在20均线上": "PRICE_MA_LINE_ANAL(n=20)", "价格在30均线上": "PRICE_MA_LINE_ANAL(n=30)", "价格在60均线上": "PRICE_MA_LINE_ANAL(n=60)", "价格在120均线上": "PRICE_MA_LINE_ANAL(n=120)", "5均线在10均线上": "MA_LINE_ANAL(n1=5,n2=10)", "10均线在20均线上": "MA_LINE_ANAL(n1=10,n2=20)", "20均线在30均线上": "MA_LINE_ANAL(n1=20,n2=30)", "30均线在60均线上": "MA_LINE_ANAL(n1=30,n2=60)", "60均线在120均线上": "MA_LINE_ANAL(n1=60,n2=120)", "5日Alpha": "roll_alpha(n=5)", "10日Alpha": "roll_alpha(n=10)", "20日Alpha": "roll_alpha(n=20)", "30日Alpha": "roll_alpha(n=30)", "60日Alpha": "roll_alpha(n=60)", "120日Alpha": "roll_alpha(n=120)", "5日Beta": "roll_beta(n=5)", "10日Beta": "roll_beta(n=10)", "20日Beta": "roll_beta(n=20)", "30日Beta": "roll_beta(n=30)", "60日Beta": "roll_beta(n=60)", "120日Beta": "roll_beta(n=120)", "5日夏普比率": "roll_sharpe_ratio(n=5)", "10日夏普比率": "roll_sharpe_ratio(n=10)", "20日夏普比率": "roll_sharpe_ratio(n=20)", "30日夏普比率": "roll_sharpe_ratio(n=30)", "60日夏普比率": "roll_sharpe_ratio(n=60)", "120日夏普比率": "roll_sharpe_ratio(n=120)", "5日年化波动率": "roll_annual_volatility(n=5)", "10日年化波动率": "roll_annual_volatility(n=10)", "20日年化波动率": "roll_annual_volatility(n=20)", "30日年化波动率": "roll_annual_volatility(n=30)", "60日年化波动率": "roll_annual_volatility(n=60)", "120日年化波动率": "roll_annual_volatility(n=120)", "5日最大回撤": "roll_max_drawdown(n=5)", "10日最大回撤": "roll_max_drawdown(n=10)", "20日最大回撤": "roll_max_drawdown(n=20)", "30日最大回撤": "roll_max_drawdown(n=30)", "60日最大回撤": "roll_max_drawdown(n=60)", "120日最大回撤": "roll_max_drawdown(n=120)", "5日上涨捕获率": "roll_up_capture(n=5)", "10日上涨捕获率": "roll_up_capture(n=10)", "20日上涨捕获率": "roll_up_capture(n=20)", "30日上涨捕获率": "roll_up_capture(n=30)", "60日上涨捕获率": "roll_up_capture(n=60)", "120日上涨捕获率": "roll_up_capture(n=120)", "5日下跌捕获率": "roll_down_capture(n=5)", "10日下跌捕获率": "roll_down_capture(n=10)", "20日下跌捕获率": "roll_down_capture(n=20)", "30日下跌捕获率": "roll_down_capture(n=30)", "60日下跌捕获率": "roll_down_capture(n=60)", "120日下跌捕获率": "roll_down_capture(n=120)", "3日回归动量": "calculate_momentum_score(n=3)", "5日回归动量": "calculate_momentum_score(n=5)", "7日回归动量": "calculate_momentum_score(n=7)", "9日回归动量": "calculate_momentum_score(n=9)", "12日回归动量": "calculate_momentum_score(n=12)", "15日回归动量": "calculate_momentum_score(n=15)", "18日回归动量": "calculate_momentum_score(n=18)", "20日回归动量": "calculate_momentum_score(n=20)", "23日回归动量": "calculate_momentum_score(n=23)", "25日回归动量": "calculate_momentum_score(n=25)", "28日回归动量": "calculate_momentum_score(n=28)", "30日回归动量": "calculate_momentum_score(n=30)", "35日回归动量": "calculate_momentum_score(n=35)", "40日回归动量": "calculate_momentum_score(n=40)", "45日回归动量": "calculate_momentum_score(n=45)", "50日回归动量": "calculate_momentum_score(n=50)", "60日回归动量": "calculate_momentum_score(n=60)", "5日最高值到当前周期": "HHVBARS(n=5)", "10日最高值到当前周期": "HHVBARS(n=10)", "20日最高值到当前周期": "HHVBARS(n=20)", "30日最高值到当前周期": "HHVBARS(n=30)", "60日最高值到当前周期": "HHVBARS(n=60)", "120日最高值到当前周期": "HHVBARS(n=120)", "5日最低值到当前周期": "LLVBARS(n=5)", "10日最低值到当前周期": "LLVBARS(n=10)", "20日最低值到当前周期": "LLVBARS(n=20)", "30日最低值到当前周期": "LLVBARS(n=30)", "60日最低值到当前周期": "LLVBARS(n=60)", "120日最低值到当前周期": "LLVBARS(n=120)", "5日回归斜率": "SLOPE(n=5)", "10日回归斜率": "SLOPE(n=10)", "20日回归斜率": "SLOPE(n=20)", "30日回归斜率": "SLOPE(n=30)", "60日回归斜率": "SLOPE(n=60)", "120日回归斜率": "SLOPE(n=120)", "5日标准差": "STD(n=5)", "10日标准差": "STD(n=10)", "20日标准差": "STD(n=20)", "30日标准差": "STD(n=30)", "60日标准差": "STD(n=60)", "120日标准差": "STD(n=120)", "CCI商品路径指标": "CCI()", "MFI最近流量指标": "MFI()", "MTM动量线_MTM值": "MTM_MTM()", "MTM动量线_MTMMA值": "MTM_MTMMA()", "RSI相对强弱_RSI1": "RSI1()", "RSI相对强弱_RSI2": "RSI2()", "RSI相对强弱_RSI3": "RSI3()", "KDJ指标_K值": "KDJ_K()", "KDJ指标_D值": "KDJ_D()", "KDJ指标_J值": "KDJ_J()", "SKDJ慢速随机_K值": "SKDJ_K()", "SKDJ慢速随机_D值": "SKDJ_D()", "UDL引力线_UDL值": "UDL_UDL()", "UDL引力线_MAUDL值": "UDL_MAUDL()", "WR威廉指标_WR1": "WR1()", "WR威廉指标_WR2": "WR2()", "LWR指标_LWR1": "LWR1()", "LWR指标_LWR2": "LWR2()", "MARSI相对强弱平均线_RSI1": "MARSI1()", "MARSI相对强弱平均线_RSI2": "MARSI2()", "BIAS乖离率_BIAS1": "BIAS1()", "BIAS乖离率_BIAS2": "BIAS2()", "BIAS乖离率_BIAS3": "BIAS3()", "BIAS_QL乖离率传统版_BIAS值": "BIAS_QL_BIAS()", "BIAS_QL乖离率传统版_BIASMA值": "BIAS_QL_BIASMA()", "BIAS36三六乖离_BIAS36": "BIAS36_BIAS36()", "BIAS36三六乖离_BIAS612": "BIAS36_BIAS612()", "BIAS36三六乖离_MABIAS": "BIAS36_MABIAS()", "ACCER幅度涨速": "ACCER()", "ASI振动升降指标_ASI": "ASI_ASI()", "ASI振动升降指标_ASIT": "ASI_ASIT()", "CHO佳庆指标_CHO": "CHO_CHO()", "CHO佳庆指标_MACHO": "CHO_MACHO()", "DMA_XT平均差_DIF": "DMA_XT_DIF()", "DMA_XT平均差_DIFMA": "DMA_XT_DIFMA()", "DMI趋向指标_PDI": "DMI_PDI()", "DMI趋向指标_MDI": "DMI_MDI()", "DMI趋向指标_ADX": "DMI_ADX()", "DMI趋向指标_ADXR": "DMI_ADXR()", "DPO区间震荡线_DPO": "DPO_DPO()", "DPO区间震荡线_MADPO": "DPO_MADPO()", "EMV简易波动指标_EMV": "EMV_EMV()", "EMV简易波动指标_MAEMV": "EMV_MAEMV()", "MACD平滑异同平均线_DIF": "MACD_DIF()", "MACD平滑异同平均线_DEA": "MACD_DEA()", "MACD平滑异同平均线_MACD": "MACD_MACD()", "VMACD量平滑异同平均线_DIF": "VMACD_DIF()", "VMACD量平滑异同平均线_DEA": "VMACD_DEA()", "VMACD量平滑异同平均线_MACD": "VMACD_MACD()", "SMACD单线平滑异同平均线_DEA": "SMACD_DEA()", "SMACD单线平滑异同平均线_MACD": "SMACD_MACD()", "QACD快速异同平均线_DIF": "QACD_DIF()", "QACD快速异同平均线_MACD": "QACD_MACD()", "QACD快速异同平均线_DDIF": "QACD_DDIF()", "TRIX三重指数平均线_TRIX": "TRIX_TRIX()", "TRIX三重指数平均线_MATRIX": "TRIX_MATRIX()", "UOS终极指标_UOS": "UOS_UOS()", "UOS终极指标_MAUOS": "UOS_MAUOS()", "VTP量价曲线_VPT": "VTP_VPT()", "VTP量价曲线_MAVP": "VTP_MAVP()", "WVAD威廉变异离散量_WVAD": "WVAD_WVAD()", "WVAD威廉变异离散量_MAWVAD": "WVAD_MAWVAD()", "JS加数线_JS": "JS_JS()", "JS加数线_MAJS1": "JS_MAJS1()", "JS加数线_MAJS2": "JS_MAJS2()", "JS加数线_MAJS3": "JS_MAJS3()", "CYE市场趋势_CYEL": "CYE_CYEL()", "CYE市场趋势_CYES": "CYE_CYES()", "GDX轨道线_轨道": "GDX_轨道()", "GDX轨道线_压力线": "GDX_压力线()", "GDX轨道线_支撑线": "GDX_支撑线()", "JLHB绝路航标_B": "JLHB_B()", "JLHB绝路航标_VAR2": "JLHB_VAR2()", "JLHB绝路航标_绝路航标": "JLHB_绝路航标()", "BRAR情绪指标_BR": "BRAR_BR()", "BRAR情绪指标_AR": "BRAR_AR()", "CR带状能量线_CR": "CR_CR()", "CR带状能量线_MA1": "CR_MA1()", "CR带状能量线_MA2": "CR_MA2()", "CR带状能量线_MA3": "CR_MA3()", "CR带状能量线_MA4": "CR_MA4()", "MASS梅斯线_MASS": "MASS_MASS()", "MASS梅斯线_MAMASS": "MASS_MAMASS()", "PSY心理线_PSY": "PSY_PSY()", "PSY心理线_PSYMA": "PSY_PSYMA()", "VR成交量变异率_VR": "VR_VR()", "VR成交量变异率_MAVR": "VR_MAVR()", "WAD威廉多空力度线_WAD": "WAD_WAD()", "WAD威廉多空力度线_MAWAD": "WAD_MAWAD()", "PCNT幅度比_PCNT": "PCNT_PCNT()", "PCNT幅度比_MAPCNT": "PCNT_MAPCNT()", "CYR市场强弱_CYR": "CYR_CYR()", "CYR市场强弱_MACYR": "CYR_MACYR()", "AMO成交金额_AMOW": "AMO_AMOW()", "AMO成交金额_AMO1": "AMO_AMO1()", "AMO成交金额_AMO2": "AMO_AMO2()", "OBV累积能量线_OBV": "OBV_OBV()", "OBV累积能量线_MAOBV": "OBV_MAOBV()", "VOL成交量_MAVOL1": "VOL_XT_MAVOL1()", "VOL成交量_MAVOL2": "VOL_XT_MAVOL2()", "VRSI相对强弱量_RSI1": "VRSI1()", "VRSI相对强弱量_RSI2": "VRSI2()", "VRSI相对强弱量_RSI3": "VRSI3()", "HSL换手线_HSL": "HSL_HSL()", "HSL换手线_MAHSL": "HSL_MAHSL()", "MA均线_MA1": "MA_XT_MA1()", "MA均线_MA2": "MA_XT_MA2()", "MA均线_MA3": "MA_XT_MA3()", "MA均线_MA4": "MA_XT_MA4()", "ACD升降线_ACD": "ACD_ACD()", "ACD升降线_MAACD": "ACD_MAACD()", "BBI多空均线": "BBI()", "EXPMA指数平均线_EXP1": "EXPMA_EXP1()", "EXPMA指数平均线_EXP2": "EXPMA_EXP2()", "HMA高价平均线_HMA1": "HMA_HMA1()", "HMA高价平均线_HMA2": "HMA_HMA2()", "HMA高价平均线_HMA3": "HMA_HMA3()", "HMA高价平均线_HMA4": "HMA_HMA4()", "HMA高价平均线_HMA5": "HMA_HMA5()", "LMA低价平均线_LMA1": "LMA_LMA1()", "LMA低价平均线_LMA2": "LMA_LMA2()", "LMA低价平均线_LMA3": "LMA_LMA3()", "LMA低价平均线_LMA4": "LMA_LMA4()", "LMA低价平均线_LMA5": "LMA_LMA5()", "VMA变异平均线_VMA1": "VMA_VMA1()", "VMA变异平均线_VMA2": "VMA_VMA2()", "VMA变异平均线_VMA3": "VMA_VMA3()", "VMA变异平均线_VMA4": "VMA_VMA4()", "VMA变异平均线_VMA5": "VMA_VMA5()", "AMV成本均线_AMV1": "AMV_AMV1()", "AMV成本均线_AMV2": "AMV_AMV2()", "AMV成本均线_AMV3": "AMV_AMV3()", "AMV成本均线_AMV4": "AMV_AMV4()", "BBIBOLL多空布林线_BBIBOLL": "BBIBOLL_BBIBOLL()", "BBIBOLL多空布林线_UPR": "BBIBOLL_UPR()", "BBIBOLL多空布林线_DWN": "BBIBOLL_DWN()", "ALLIGAT鳄鱼线_上唇": "ALLIGAT_上唇()", "ALLIGAT鳄鱼线_牙齿": "ALLIGAT_牙齿()", "ALLIGAT鳄鱼线_下颚": "ALLIGAT_下颚()", "GMMA顾比均线_MA3": "GMMA_MA3()", "GMMA顾比均线_MA5": "GMMA_MA5()", "GMMA顾比均线_MA8": "GMMA_MA8()", "GMMA顾比均线_MA10": "GMMA_MA10()", "GMMA顾比均线_MA12": "GMMA_MA12()", "GMMA顾比均线_MA15": "GMMA_MA15()", "GMMA顾比均线_MA30": "GMMA_MA30()", "GMMA顾比均线_MA35": "GMMA_MA35()", "GMMA顾比均线_MA40": "GMMA_MA40()", "GMMA顾比均线_MA45": "GMMA_MA45()", "GMMA顾比均线_MA50": "GMMA_MA50()", "GMMA顾比均线_MA60": "GMMA_MA60()", "BOLL布林线_BOLL": "BOLL_BOLL()", "BOLL布林线_UB": "BOLL_UB()", "BOLL布林线_LB": "BOLL_LB()", "PBX瀑布线_PBX1": "PBX_PBX1()", "PBX瀑布线_PBX2": "PBX_PBX2()", "PBX瀑布线_PBX3": "PBX_PBX3()", "PBX瀑布线_PBX4": "PBX_PBX4()", "PBX瀑布线_PBX5": "PBX_PBX5()", "PBX瀑布线_PBX6": "PBX_PBX6()", "ENE轨道线_UPPER": "ENE_UPPER()", "ENE轨道线_LOWER": "ENE_LOWER()", "ENE轨道线_ENE": "ENE_ENE()", "MIKE麦克支撑压力_STOR": "MIKE_STOR()", "MIKE麦克支撑压力_MIDR": "MIKE_MIDR()", "MIKE麦克支撑压力_WEKR": "MIKE_WEKR()", "MIKE麦克支撑压力_WEKS": "MIKE_WEKS()", "MIKE麦克支撑压力_MIDS": "MIKE_MIDS()", "MIKE麦克支撑压力_STOS": "MIKE_STOS()", "XS薛斯通道_SUP": "XS_SUP()", "XS薛斯通道_SDN": "XS_SDN()", "XS薛斯通道_LUP": "XS_LUP()", "XS薛斯通道_LDN": "XS_LDN()", "TQN唐奇安通道_周期高点": "TQN_周期高点()", "TQN唐奇安通道_周期低点": "TQN_周期低点()", "TQN唐奇安通道_平空开多": "TQN_平空开多()", "TQN唐奇安通道_平多开空": "TQN_平多开空()", "SAR抛物线指标": "SAR()", "MA交易_MA1": "MA_交易_MA1()", "MA交易_MA2": "MA_交易_MA2()", "MA交易_平空开多": "MA_交易_平空开多()", "MA交易_平多开空": "MA_交易_平多开空()", "MACD交易_DIFF": "MACD_交易_DIFF()", "MACD交易_DEA": "MACD_交易_DEA()", "MACD交易_MACD": "MACD_交易_MACD()", "MACD交易_平空开多": "MACD_交易_平空开多()", "MACD交易_平多开空": "MACD_交易_平多开空()", "KDJ交易_K": "KDJ_交易_K()", "KDJ交易_D": "KDJ_交易_D()", "KDJ交易_J": "KDJ_交易_J()", "KDJ交易_平空开多": "KDJ_交易_平空开多()", "KDJ交易_平多开空": "KDJ_交易_平多开空()", "SG_XDT心电图_QR": "SG_XDT_QR()", "SG_XDT心电图_MQR1": "SG_XDT_MQR1()", "SG_XDT心电图_MQR2": "SG_XDT_MQR2()", "SG_NDB脑电波_DK": "SG_NDB_DK()", "SG_NDB脑电波_MDK1": "SG_NDB_MDK1()", "SG_NDB脑电波_MDK2": "SG_NDB_MDK2()", "SG_SMX生命线_ZY1": "SG_SMX_ZY1()", "SG_SMX生命线_ZY2": "SG_SMX_ZY2()", "SG_SMX生命线_ZY3": "SG_SMX_ZY3()", "SG_LB量比_量比": "SG_LB_量比()", "SG_LB量比_MA5": "SG_LB_MA5()", "SG_LB量比_MA10": "SG_LB_MA10()", "SG_PF强势股评分": "SG_PF()", "RAD威力雷达_RADER1": "RAD_RADER1()", "RAD威力雷达_RADERMA": "RAD_RADERMA()", "LON龙系长线_LON": "LON_LON()", "LON龙系长线_LONMA": "LON_LONMA()", "LON龙系长线_LONT": "LON_LONT()", "SHT龙系短线_SHT": "SHT_SHT()", "SHT龙系短线_SHTMA": "SHT_SHTMA()", "ZLJC主力进出_JCS": "ZLJC_JCS()", "ZLJC主力进出_JCM": "ZLJC_JCM()", "ZLJC主力进出_JCL": "ZLJC_JCL()", "ZLMM主力买卖_MMS": "ZLMM_MMS()", "ZLMM主力买卖_MMM": "ZLMM_MMM()", "ZLMM主力买卖_MML": "ZLMM_MML()", "SLZT神龙在天_白龙": "SLZT_白龙()", "SLZT神龙在天_黄龙": "SLZT_黄龙()", "SLZT神龙在天_紫龙": "SLZT_紫龙()", "SLZT神龙在天_青龙": "SLZT_青龙()", "SLZT神龙在天_红龙": "SLZT_红龙()", "SLZT神龙在天_蓝龙": "SLZT_蓝龙()", "ADVOL龙系离散量_ADVOL": "ADVOL_ADVOL()", "ADVOL龙系离散量_MA1": "ADVOL_MA1()", "ADVOL龙系离散量_MA2": "ADVOL_MA2()", "CYS市场盈亏": "CYS()", "CYW主力控盘": "CYW()", "JAX济安线_J": "JAX_J()", "JAX济安线_A": "JAX_A()", "JAX济安线_X": "JAX_X()", "XJDX超级短线_J": "XJDX_J()", "XJDX超级短线_D": "XJDX_D()", "XJDX超级短线_K": "XJDX_K()", "ZJTJ庄家抬轿_无庄控盘": "ZJTJ_无庄控盘()", "ZJTJ庄家抬轿_开始控盘": "ZJTJ_开始控盘()", "ZJTJ庄家抬轿_有庄控盘": "ZJTJ_有庄控盘()", "ZJTJ庄家抬轿_主力出货": "ZJTJ_主力出货()", "BDZX波段之星_AK": "BDZX_AK()", "BDZX波段之星_AD1": "BDZX_AD1()", "BDZX波段之星_AJ": "BDZX_AJ()", "BDZX波段之星_买进": "BDZX_买进()", "BDZX波段之星_卖出": "BDZX_卖出()", "LHXJ猎狐先觉_主力弃盘": "LHXJ_主力弃盘()", "LHXJ猎狐先觉_主力控盘": "LHXJ_主力控盘()", "LYJH猎鹰歼狐_机构做空能量线": "LYJH_机构做空能量线()", "LYJH猎鹰歼狐_机构做多能量线": "LYJH_机构做多能量线()", "JFZX飓风智能中线_多头力量": "JFZX_多头力量()", "JFZX飓风智能中线_空头力量": "JFZX_空头力量()", "CYHT财运亨通_SK": "CYHT_SK()", "CYHT财运亨通_SD": "CYHT_SD()", "CYHT财运亨通_卖出": "CYHT_卖出()", "CYHT财运亨通_买进": "CYHT_买进()", "BSQJ买卖区间_B买": "BSQJ_B买()", "BSQJ买卖区间_持仓": "BSQJ_持仓()", "BSQJ买卖区间_S卖": "BSQJ_S卖()", "BSQJ买卖区间_空仓": "BSQJ_空仓()", "CDP_STD逆势操作_CDP": "CDP_STD_CDP()", "CDP_STD逆势操作_AH": "CDP_STD_AH()", "CDP_STD逆势操作_NH": "CDP_STD_NH()", "CDP_STD逆势操作_NL": "CDP_STD_NL()", "CDP_STD逆势操作_AL": "CDP_STD_AL()", "Alpha001": "alpha001()", "Alpha002": "alpha002()", "Alpha003": "alpha003()", "Alpha004": "alpha004()", "Alpha005": "alpha005()", "Alpha006": "alpha006()", "Alpha007": "alpha007()", "Alpha008": "alpha008()", "Alpha009": "alpha009()", "Alpha010": "alpha010()", "Alpha011": "alpha011()", "Alpha012": "alpha012()", "Alpha013": "alpha013()", "Alpha014": "alpha014()", "Alpha015": "alpha015()", "Alpha016": "alpha016()", "Alpha017": "alpha017()", "Alpha018": "alpha018()", "Alpha019": "alpha019()", "Alpha020": "alpha020()", "Alpha021": "alpha021()", "Alpha022": "alpha022()", "Alpha023": "alpha023()", "Alpha024": "alpha024()", "Alpha025": "alpha025()", "Alpha026": "alpha026()", "Alpha027": "alpha027()", "Alpha028": "alpha028()", "Alpha029": "alpha029()", "Alpha030": "alpha030()", "Alpha031": "alpha031()", "Alpha032": "alpha032()", "Alpha033": "alpha033()", "Alpha034": "alpha034()", "Alpha035": "alpha035()", "Alpha036": "alpha036()", "Alpha037": "alpha037()", "Alpha038": "alpha038()", "Alpha039": "alpha039()", "Alpha040": "alpha040()", "Alpha041": "alpha041()", "Alpha042": "alpha042()", "Alpha043": "alpha043()", "Alpha044": "alpha044()", "Alpha045": "alpha045()", "Alpha046": "alpha046()", "Alpha047": "alpha047()", "Alpha048": "alpha048()", "Alpha049": "alpha049()", "Alpha050": "alpha050()", "Alpha051": "alpha051()", "Alpha052": "alpha052()", "Alpha053": "alpha053()", "Alpha054": "alpha054()", "Alpha055": "alpha055()", "Alpha056": "alpha056()", "Alpha057": "alpha057()", "Alpha058": "alpha058()", "Alpha059": "alpha059()", "Alpha060": "alpha060()", "Alpha061": "alpha061()", "Alpha062": "alpha062()", "Alpha063": "alpha063()", "Alpha064": "alpha064()", "Alpha065": "alpha065()", "Alpha066": "alpha066()", "Alpha067": "alpha067()", "Alpha068": "alpha068()", "Alpha069": "alpha069()", "Alpha070": "alpha070()", "Alpha071": "alpha071()", "Alpha072": "alpha072()", "Alpha073": "alpha073()", "Alpha074": "alpha074()", "Alpha075": "alpha075()", "Alpha076": "alpha076()", "Alpha077": "alpha077()", "Alpha078": "alpha078()", "Alpha079": "alpha079()", "Alpha080": "alpha080()", "Alpha081": "alpha081()", "Alpha082": "alpha082()", "Alpha083": "alpha083()", "Alpha084": "alpha084()", "Alpha085": "alpha085()", "Alpha086": "alpha086()", "Alpha087": "alpha087()", "Alpha088": "alpha088()", "Alpha089": "alpha089()", "Alpha090": "alpha090()", "Alpha091": "alpha091()", "Alpha092": "alpha092()", "Alpha093": "alpha093()", "Alpha094": "alpha094()", "Alpha095": "alpha095()", "Alpha096": "alpha096()", "Alpha097": "alpha097()", "Alpha098": "alpha098()", "Alpha099": "alpha099()", "Alpha100": "alpha100()", "Alpha101": "alpha101()", "Alpha102": "alpha102()", "Alpha103": "alpha103()", "Alpha104": "alpha104()", "Alpha105": "alpha105()", "Alpha106": "alpha106()", "Alpha107": "alpha107()", "Alpha108": "alpha108()", "Alpha109": "alpha109()", "Alpha110": "alpha110()", "Alpha111": "alpha111()", "Alpha112": "alpha112()", "Alpha113": "alpha113()", "Alpha114": "alpha114()", "Alpha115": "alpha115()", "Alpha116": "alpha116()", "Alpha117": "alpha117()", "Alpha118": "alpha118()", "Alpha119": "alpha119()", "Alpha120": "alpha120()", "Alpha121": "alpha121()", "Alpha122": "alpha122()", "Alpha123": "alpha123()", "Alpha124": "alpha124()", "Alpha125": "alpha125()", "Alpha126": "alpha126()", "Alpha127": "alpha127()", "Alpha128": "alpha128()", "Alpha129": "alpha129()", "Alpha130": "alpha130()", "Alpha131": "alpha131()", "Alpha132": "alpha132()", "Alpha133": "alpha133()", "Alpha134": "alpha134()", "Alpha135": "alpha135()", "Alpha136": "alpha136()", "Alpha137": "alpha137()", "Alpha138": "alpha138()", "Alpha139": "alpha139()", "Alpha140": "alpha140()", "Alpha141": "alpha141()", "Alpha142": "alpha142()", "Alpha143": "alpha143()", "Alpha144": "alpha144()", "Alpha145": "alpha145()", "Alpha146": "alpha146()", "Alpha147": "alpha147()", "Alpha148": "alpha148()", "Alpha149": "alpha149()", "Alpha150": "alpha150()", "Alpha151": "alpha151()", "Alpha152": "alpha152()", "Alpha153": "alpha153()", "Alpha154": "alpha154()", "Alpha155": "alpha155()", "Alpha156": "alpha156()", "Alpha157": "alpha157()", "Alpha158": "alpha158()", "Alpha159": "alpha159()", "Alpha160": "alpha160()", "Alpha161": "alpha161()", "Alpha162": "alpha162()", "Alpha163": "alpha163()", "Alpha164": "alpha164()", "Alpha165": "alpha165()", "Alpha166": "alpha166()", "Alpha167": "alpha167()", "Alpha168": "alpha168()", "Alpha169": "alpha169()", "Alpha170": "alpha170()", "Alpha171": "alpha171()", "Alpha172": "alpha172()", "Alpha173": "alpha173()", "Alpha174": "alpha174()", "Alpha175": "alpha175()", "Alpha176": "alpha176()", "Alpha177": "alpha177()", "Alpha178": "alpha178()", "Alpha179": "alpha179()", "Alpha180": "alpha180()", "Alpha181": "alpha181()", "Alpha182": "alpha182()", "Alpha183": "alpha183()", "Alpha184": "alpha184()", "Alpha185": "alpha185()", "Alpha186": "alpha186()", "Alpha187": "alpha187()", "Alpha188": "alpha188()", "Alpha189": "alpha189()", "Alpha190": "alpha190()", "Alpha191": "alpha191()" } #全部因子的计算公式 from xg_tdx_func.xg_tdx_func import * import empyrical as ep import pandas as pd import numpy as np import os from datetime import datetime, timedelta import json import warnings from concurrent.futures import ThreadPoolExecutor, as_completed import threading from scipy import stats import statsmodels.api as sm import math import warnings warnings.filterwarnings("ignore", category=DeprecationWarning, module="pandas")
class xg_factor: ''' 小果因子库计算系统 ''' def init(self, df='', index_df='',): self.path = os.path.dirname(os.path.abspath(file)) self.df = df.copy() if df is not None and not isinstance(df, str) and hasattr(df, 'copy') else df self.index_df = index_df.copy() if index_df is not None and not isinstance(index_df, str) and hasattr(index_df, 'copy') else index_df
# 数据重命名（一次性完成）
if isinstance(self.df, pd.DataFrame) and not self.df.empty:
rename_dict = {
"close": "closePrice",
"open": "openPrice",
"low": "lowestPrice",
"high": "highestPrice",
"volume": "turnoverVol",
"amount": "turnoverValue"
}
# 只重命名存在的列
self.df.rename(columns={k: v for k, v in rename_dict.items() if k in self.df.columns}, inplace=True)
# 提取核心数据列（使用重命名后的列名）
self.closePrice = self.df['closePrice'] if 'closePrice' in self.df.columns else pd.Series()
self.openPrice = self.df['openPrice'] if 'openPrice' in self.df.columns else pd.Series()
self.lowestPrice = self.df['lowestPrice'] if 'lowestPrice' in self.df.columns else pd.Series()
self.highestPrice = self.df['highestPrice'] if 'highestPrice' in self.df.columns else pd.Series()
self.turnoverVol = self.df['turnoverVol'] if 'turnoverVol' in self.df.columns else pd.Series()
self.turnoverValue = self.df['turnoverValue'] if 'turnoverValue' in self.df.columns else pd.Series()
# 统一简写命名（方便调用）
self.C = self.closePrice
self.H = self.highestPrice
self.L = self.lowestPrice
self.O = self.openPrice
self.V = self.turnoverVol
self.AMOUNT = self.turnoverValue
# 保留原始简写（兼容旧代码）
self.close = self.closePrice
self.high = self.highestPrice
self.low = self.lowestPrice
self.open = self.openPrice
self.volume = self.turnoverVol
self.amount = self.turnoverValue
else:
# 空数据时的默认值
self.closePrice = pd.Series()
self.openPrice = pd.Series()
self.lowestPrice = pd.Series()
self.highestPrice = pd.Series()
self.turnoverVol = pd.Series()
self.turnoverValue = pd.Series()
self.C = pd.Series()
self.H = pd.Series()
self.L = pd.Series()
self.O = pd.Series()
self.V = pd.Series()
self.AMOUNT = pd.Series()
self.close = pd.Series()
self.high = pd.Series()
self.low = pd.Series()
self.open = pd.Series()
self.volume = pd.Series()
self.amount = pd.Series()
# ========== 辅助函数 ==========
def _sma(self, series, n, m):
"""SMA: 移动平均，alpha = m/n"""
return series.ewm(adjust=False, alpha=m/n, min_periods=0, ignore_na=False).mean()
def _tsrank(self, series, n):
"""TSRANK: 时间序列排名"""
def rank_last(x):
return stats.rankdata(x)[-1] / len(x) if len(x) > 0 else np.nan
return series.rolling(window=n, min_periods=n).apply(rank_last)
def _tsrank_fixed(self, series, n):
"""改进的TSRANK函数"""
result = pd.Series(index=series.index, dtype=float)
for i in range(len(series)):
start = max(0, i - n + 1)
window_data = series.iloc[start:i+1]
valid_data = window_data.dropna()
if len(valid_data) >= max(2, n // 2):
current_val = series.iloc[i]
rank = (valid_data < current_val).sum() + 1
result.iloc[i] = rank / len(valid_data)
else:
result.iloc[i] = np.nan
return result.fillna(method='ffill').fillna(method='bfill')
def _decaylinear(self, series, n):
"""DECAYLINEAR: 线性衰减加权和"""
w = np.arange(1, n + 1)
return series.rolling(window=n, min_periods=n).apply(lambda x: np.dot(x, w))
def _regbeta(self, y, x):
"""REGBETA: 回归beta"""
y_vals = y.values
x_vals = x.values if isinstance(x, pd.Series) else np.array(x)
x_vals = sm.add_constant(x_vals)
try:
result = sm.OLS(y_vals, x_vals).fit()
return result.params[1]
except:
return np.nan
def six_pulse_excalibur_hist(self):
'''
六脉神剑
'''
markers=0
signal=0
#df=self.data.get_hist_data_em(stock=stock)
CLOSE=self.C
LOW=self.L
HIGH=self.H
DIFF=EMA(CLOSE,8)-EMA(CLOSE,13)
DEA=EMA(DIFF,5)
#如果满足DIFF>DEA 在1的位置标记1的图标
#DRAWICON(DIFF>DEA,1,1);
markers+=IF(DIFF>DEA,1,0)
#如果满足DIFF<DEA 在1的位置标记2的图标
#DRAWICON(DIFF<DEA,1,2);
markers+=IF(DIFF<DEA,1,0)
#DRAWTEXT(ISLASTBAR=1,1,'. MACD'),COLORFFFFFF;{微信公众号:尊重市场}
ABC1=DIFF>DEA
signal+=IF(ABC1,1,0)
尊重市场1=(CLOSE-LLV(LOW,8))/(HHV(HIGH,8)-LLV(LOW,8))*100
K=SMA(尊重市场1,3,1)
D=SMA(K,3,1)
#如果满足k>d 在2的位置标记1的图标
markers+=IF(K>D,1,0)
#DRAWICON(K>D,2,1);
markers+=IF(K<D,1,0)
#DRAWICON(K<D,2,2);
#DRAWTEXT(ISLASTBAR=1,2,'. KDJ'),COLORFFFFFF;
ABC2=K>D
signal+=IF(ABC2,1,0)
指标营地=REF(CLOSE,1)
RSI1=(SMA(MAX(CLOSE-指标营地,0),5,1))/(SMA(ABS(CLOSE-指标营地),5,1))*100
RSI2=(SMA(MAX(CLOSE-指标营地,0),13,1))/(SMA(ABS(CLOSE-指标营地),13,1))*100
markers+=IF(RSI1>RSI2,1,0)
#DRAWICON(RSI1>RSI2,3,1);
markers+=IF(RSI1<RSI2,1,0)
#DRAWICON(RSI1<RSI2,3,2);
#DRAWTEXT(ISLASTBAR=1,3,'. RSI'),COLORFFFFFF;
ABC3=RSI1>RSI2
signal+=IF(ABC3,1,0)
尊重市场=-(HHV(HIGH,13)-CLOSE)/(HHV(HIGH,13)-LLV(LOW,13))*100
LWR1=SMA(尊重市场,3,1)
LWR2=SMA(LWR1,3,1)
#DRAWICON(LWR1>LWR2,4,1);
markers+=IF(LWR1>LWR2,1,0)
#DRAWICON(LWR1<LWR2,4,2);
markers+=IF(LWR1<LWR2,1,0)
#DRAWTEXT(ISLASTBAR=1,4,'. LWR'),COLORFFFFFF;
ABC4=LWR1>LWR2
signal+=IF(ABC4,1,0)
BBI=(MA(CLOSE,3)+MA(CLOSE,5)+MA(CLOSE,8)+MA(CLOSE,13))/4
#DRAWICON(CLOSE>BBI,5,1);
markers+=IF(CLOSE>BBI,1,0)
#DRAWICON(CLOSE<BBI,5,2);
markers+=IF(CLOSE<BBI,1,0)
#DRAWTEXT(ISLASTBAR=1,5,'. BBI'),COLORFFFFFF;
ABC10=7
ABC5=CLOSE>BBI
signal+=IF(ABC5,1,0)
MTM=CLOSE-REF(CLOSE,1)
MMS=100*EMA(EMA(MTM,5),3)/EMA(EMA(ABS(MTM),5),3)
MMM=100*EMA(EMA(MTM,13),8)/EMA(EMA(ABS(MTM),13),8)
markers+=IF(MMS>MMM,1,0)
#DRAWICON(MMS>MMM,6,1);
markers+=IF(MMS<MMM,1,0)
#DRAWICON(MMS<MMM,6,2);
#DRAWTEXT(ISLASTBAR=1,6,'. ZLMM'),COLORFFFFFF;
ABC6=MMS>MMM
signal+=IF(ABC6,1,0)
return signal
def small_fruit_band_trading_1(self):
'''
小波段交易
'''
df=self.df
CLOSE=self.C
C=self.C
LOW=self.L
L=self.L
HIGH=self.H
H=self.H
OPEN=self.O
O=self.O
volume=self.V
V=self.V
N1=7
N2=5
N3=3
ABC1=(((HIGH + LOW)+(CLOSE*2)) / 4)
ABC3=EMA(ABC1,N1)
ABC4=STD(ABC1,N1)
ABC5=((ABC1 - ABC3)*100) / ABC4
ABC6=EMA(ABC5,N2)
RK7=EMA(ABC6,N1)
UP=(EMA(ABC6,10)+(100 / 2)) - 5
DOWN=EMA(UP,N3)
ACB1=EMA(DOWN,N3)
ACB2=EMA(ACB1,N3)
ACB3=EMA(ACB2,N3)
ACB4=EMA(ACB3,N3)
#STICKLINE(UP < REF(UP,1),UP,MA(UP,3),5,0),COLORBLUE;
#STICKLINE(UP > REF(UP,1),UP,EMA(UP,3),5,0),COLORMAGENTA;
df['柱子']=IF(UP > REF(UP,1),'红色','蓝色')
df['买']=IF(AND(UP > REF(UP,1),REF(UP,1) < REF(UP,2)),'买',None)
df['卖']=IF(AND(UP < REF(UP,1),REF(UP,1) > REF(UP,2)),'卖',None)
#DRAWTEXT(UP > REF(UP,1) AND REF(UP,1) < REF(UP,2) ,UP,'买'),COLORRED;
#DRAWTEXT(UP < REF(UP,1) AND REF(UP,1) > REF(UP,2) ,UP,'卖'),COLORGREEN;
stats_list=[]
for buy,sell in zip(df['买'].tolist(),df['卖'].tolist()):
if buy=='买':
stats_list.append(True)
elif sell=='卖':
stats_list.append(False)
else:
stats_list.append(None)
df['stats']=stats_list
df['stats']=df['stats'].fillna(method='ffill')
return df['stats']
def small_fruit_band_trading_2(self):
'''
大波段交易
'''
df=self.df
CLOSE=self.C
C=self.C
LOW=self.L
L=self.L
HIGH=self.H
H=self.H
OPEN=self.O
O=self.O
volume=self.V
V=self.V
N1=18
N2=15
N3=12
ABC1=(((HIGH + LOW)+(CLOSE*2)) / 4)
ABC3=EMA(ABC1,N1)
ABC4=STD(ABC1,N1)
ABC5=((ABC1 - ABC3)*100) / ABC4
ABC6=EMA(ABC5,N2)
RK7=EMA(ABC6,N1)
UP=(EMA(ABC6,10)+(100 / 2)) - 5
DOWN=EMA(UP,N3)
ACB1=EMA(DOWN,N3)
ACB2=EMA(ACB1,N3)
ACB3=EMA(ACB2,N3)
ACB4=EMA(ACB3,N3)
#STICKLINE(UP < REF(UP,1),UP,MA(UP,3),5,0),COLORBLUE;
#STICKLINE(UP > REF(UP,1),UP,EMA(UP,3),5,0),COLORMAGENTA;
df['柱子']=IF(UP > REF(UP,1),'红色','蓝色')
df['买']=IF(AND(UP > REF(UP,1),REF(UP,1) < REF(UP,2)),'买',None)
df['卖']=IF(AND(UP < REF(UP,1),REF(UP,1) > REF(UP,2)),'卖',None)
#DRAWTEXT(UP > REF(UP,1) AND REF(UP,1) < REF(UP,2) ,UP,'买'),COLORRED;
#DRAWTEXT(UP < REF(UP,1) AND REF(UP,1) > REF(UP,2) ,UP,'卖'),COLORGREEN;
stats_list=[]
for buy,sell in zip(df['买'].tolist(),df['卖'].tolist()):
if buy=='买':
stats_list.append(True)
elif sell=='卖':
stats_list.append(False)
else:
stats_list.append(None)
df['stats']=stats_list
df['stats']=df['stats'].fillna(method='ffill')
return df['stats']
def band_supe_buy_sell(self):
'''
波段超级买卖
尊重市场1赋值:收盘价的6.5日[1日权重]移动平均
尊重市场2赋值:收盘价的13.5日[1日权重]移动平均
尊重市场11赋值:收盘价的3日[1日权重]移动平均
尊重市场21赋值:收盘价的8日[1日权重]移动平均
当满足条件尊重市场1>尊重市场2时,在尊重市场1和尊重市场2位置之间画柱状线,宽度为2.5,0不为0则画空心柱.,画红色,线宽为2
当满足条件尊重市场2>尊重市场1时,在尊重市场1和尊重市场2位置之间画柱状线,宽度为2.5,0不为0则画空心柱.,画蓝色,线宽为2
当满足条件尊重市场1上穿尊重市场2时,在最低价*0.98位置画5号图标
当满足条件尊重市场21上穿尊重市场11时,在最高价*1.02位置书写文字,画黄色
BBI赋值:(收盘价的3日简单移动平均+收盘价的6日简单移动平均+收盘价的12日简单移动平均+收盘价的24日简单移动平均)/4
UPR赋值:BBI+3*BBI的13日估算标准差,线宽为2
DWN赋值:BBI-3*BBI的13日估算标准差
安全赋值:收盘价的60日简单移动平均,线宽为2
LC赋值:1日前的收盘价
RSI赋值:收盘价-LC和0的较大值的6日[1日权重]移动平均/收盘价-LC的绝对值的6日[1日权重]移动平均*100
A7赋值:(2*收盘价+最高价+最低价)/4
输出操作线:A7的5日简单移动平均,线宽为1
操作线1赋值:A7的5日简单移动平均*1.03,线宽为2
操作线2赋值:A7的5日简单移动平均*0.97,线宽为2
输出ABC1:21日内A7的最低值
输出ABC2:21日内A7的最高值
SK赋值:(A7-ABC1)/(ABC2-ABC1)*100的7日指数移动平均
SD赋值:0.667*1日前的SK+0.333*SK的5日指数移动平均
当满足条件如果统计8日中满足收盘价<1日前的收盘价的天数/8>6/10ANDVOL>=1.5*成交量(手)的5日简单移动平均ANDCOUNT(SK>=SD,3)ANDREF(最低价,1)=120日内最低价的最低值,返回1,否则返回0时,在最低价*0.98位置画9号图标
当满足条件如果统计13日中满足收盘价<1日前的收盘价的天数/13>6/10ANDCOUNT(SK>SD,6)ANDREF(最低价,5)=120日内最低价的最低值ANDREF(收盘价>=开盘价,4)ANDREF(收阳线,3)ANDREF(收阳线,2)ANDREF(开盘价>CLOS,返回?,否则返回?时,在,1)ANDOPEN>1日前的收盘价,1,0)位置书写文字 ,画黄色
当满足条件如果统计13日中满足收盘价<1日前的收盘价的天数/13>6/10ANDCOUNT(SK>SD,6)ANDREF(最低价,5)=120日内最低价的最低值ANDREF(收盘价>=开盘价,4)ANDREF(收阳线,3)ANDREF(收阳线,2)ANDREF(开盘价>CLOS,返回?,否则返回?时,在,1)ANDOPEN>1日前的收盘价,1,0)位置画最低价*0.98号图标
'''
df=self.df
CLOSE=self.C
C=self.C
LOW=self.L
L=self.L
HIGH=self.H
H=self.H
OPEN=self.O
O=self.O
volume=self.V
V=self.V
尊重市场1=SMA(C,6.5,1)
尊重市场2=SMA(C,13.5,1)
尊重市场11=SMA(C,3,1)
尊重市场21=SMA(C,8,1)
'''
STICKLINE(尊重市场1>尊重市场2 , 尊重市场1,尊重市场2 ,2.5, 0),COLORRED,LINETHICK2;
STICKLINE(尊重市场2>尊重市场1,尊重市场1,尊重市场2,2.5,0),COLORBLUE,LINETHICK2;
'''
df['柱子']=IF(尊重市场1>尊重市场2,'红色','蓝色')
#DRAWICON( CROSS(尊重市场1,尊重市场2),L*0.98,5);
df['笑脸']=CROSS(尊重市场1,尊重市场2)
#DRAWTEXT(CROSS(尊重市场21,尊重市场11),H*1.02,''),COLORYELLOW;
df['标记文字']=CROSS(尊重市场21,尊重市场11)
BBI=(MA(CLOSE,3)+MA(CLOSE,6)+MA(CLOSE,12)+MA(CLOSE,24))/4
UPR=BBI+3*STD(BBI,13)
DWN=BBI-3*STD(BBI,13)
安全=MA(CLOSE,60)
LC=REF(CLOSE,1)
RSI=SMA(MAX(CLOSE-LC,0),6,1)/SMA(ABS(CLOSE-LC),6,1)*100
A7=(2*C+H+L)/4
操作线=MA(A7,5)
df['操作线']=操作线
操作线1=MA(A7,5)*1.03
df['操作线1']=操作线1
操作线2=MA(A7,5)*0.97
df['操作线2']=操作线2
ABC1=LLV(A7,21)
ABC2=HHV(A7,21)
SK=EMA((A7-ABC1)/(ABC2-ABC1)*100,7)
SD=EMA(0.667*REF(SK,1)+0.333*SK,5)
'''
DRAWICON(IF(COUNT(CLOSE<REF(CLOSE,1),8)/8>6/10 AND VOL>=1.5*MA(VOL,5) AND
COUNT(SK>=SD,3) AND REF(LOW,1)=LLV(LOW,120),1,0),L*0.98,9);
{DRAWTEXT(IF(COUNT(CLOSE<REF(CLOSE,1),8)/8>6/10 AND VOL>=1.5*MA(VOL,5) AND
COUNT(SK>=SD,3) AND REF(LOW,1)=LLV(LOW,120),1,0),LOW*0.98,'底买') ,COLOR0099FF;}
DRAWTEXT(IF(COUNT(CLOSE<REF(CLOSE,1),13)/13>6/10 AND
COUNT(SK>SD,6) AND REF(LOW,5)=LLV(LOW,120) AND REF(CLOSE>=OPEN,4) AND
REF(CLOSE>OPEN,3) AND REF(CLOSE>OPEN,2) AND REF(OPEN>CLOSE,1) AND
OPEN>REF(CLOSE,1),1,0),LOW*0.98,'底买') ,COLORYELLOW;
DRAWICON(IF(COUNT(CLOSE<REF(CLOSE,1),13)/13>6/10 AND
COUNT(SK>SD,6) AND REF(LOW,5)=LLV(LOW,120) AND REF(CLOSE>=OPEN,4) AND
REF(CLOSE>OPEN,3) AND REF(CLOSE>OPEN,2) AND REF(OPEN>CLOSE,1) AND
OPEN>REF(CLOSE,1),1,0),L*0.98,9);
'''
趋势=CLOSE>=操作线
df['趋势']=CLOSE>=操作线
df['stats']=IF(AND(趋势,尊重市场1>尊重市场2),True,False)
return df['stats']
def KDJ_KD金叉(self):
'''
KDJ_KD金叉 的 Docstring
'''
K,D,J=KDJ(CLOSE=self.C,HIGH=self.H,LOW=self.L)
result=CROSS(K,D)
#result=IF(result==True,0,1)
return result
def KDJ_KD死叉(self):
'''
KDJ_KD金叉 的 Docstring
'''
K,D,J=KDJ(CLOSE=self.C,HIGH=self.H,LOW=self.L)
result=CROSS(D,K)
#result=IF(result==True,0,1)
return result
def RSI_金叉(self):
'''
RSI_金叉 的 Docstring
'''
RSI1,RSI2,RSI3=RSI(CLOSE=self.C)
result=CROSS(RSI1,RSI2)
#result=IF(result==True,0,1)
return result
def RSI_死叉(self):
'''
RSI_金叉 的 Docstring
'''
RSI1,RSI2,RSI3=RSI(CLOSE=self.C)
result=CROSS(RSI2,RSI1)
#result=IF(result==True,0,1)
return result
def WR_金叉(self):
'''
WR_金叉 的 Docstring
'''
WR1,WR2=WR(CLOSE=self.C,LOW=self.L,HIGH=self.H)
result=CROSS(WR1,WR2)
#result=IF(result==True,0,1)
return result
def WR_金叉(self):
'''
WR_金叉 的 Docstring
'''
WR1,WR2=WR(CLOSE=self.C,LOW=self.L,HIGH=self.H)
result=CROSS(WR1,WR2)
#result=IF(result==True,0,1)
return result
def MACD_金叉(self):
'''
MACD_金叉 的 Docstring
'''
DIF,DEA,MACD_1=MACD(CLOSE=self.C)
result=CROSS(DIF,DEA)
#result=IF(result==True,0,1)
return result
def MACD_死叉(self):
'''
MACD_金叉 的 Docstring
'''
DIF,DEA,MACD_1=MACD(CLOSE=self.C)
result=CROSS(DEA,DIF)
#result=IF(result==True,0,1)
return result
def PSY_金叉(self):
'''
PSY_金叉 的 Docstring
'''
PSY_1,PSYMA=PSY(CLOSE=self.C)
result=CROSS(PSY_1,PSYMA)
#result=IF(result==True,0,1)
return result
def PSY_死叉(self):
'''
PSY_金叉 的 Docstring
'''
PSY_1,PSYMA=PSY(CLOSE=self.C)
result=CROSS(PSYMA,PSY_1)
#result=IF(result==True,0,1)
return result
def roll_alpha(self,n=5):
'''
5日alpha
'''
result=ep.roll_alpha(self.C.pct_change(),self.index_df['close'].pct_change(),window=n)
return result
def roll_beta(self,n=5):
'''
5日beta
'''
result=ep.roll_beta(self.C.pct_change(),self.index_df['close'].pct_change(),window=n)
return result
def roll_sharpe_ratio(self,n=5):
'''
5日夏普
'''
result=ep.roll_sharpe_ratio(self.C.pct_change(),window=n)
return result
def roll_annual_volatility(self,n=5):
'''
5日年华波动率
'''
result=ep.roll_annual_volatility(self.C.pct_change(),window=n)
return result
def roll_max_drawdown(self,n=5):
'''
5日最大回撤
'''
result=ep.roll_max_drawdown(self.C.pct_change(),window=n)
return result
def roll_up_capture(self,n=5):
'''
5日上涨捕获率
'''
result=ep.roll_up_capture(self.C.pct_change(),self.index_df['close'].pct_change(),window=n)
return result
def roll_down_capture(self,n=5):
'''
5日下跌捕获率
'''
result=ep.roll_down_capture(self.C.pct_change(),self.index_df['close'].pct_change(),window=n)
return result
# ========== 因子方法 ==========
def SMA(self, period=5):
"""
SMA
"""
return MA(self.C, N=period)
def CROSS_UP(self, n1=5, n2=10):
"""
金叉判断
"""
result = CROSS(MA(self.C, n1), MA(self.C, n2))
return result
def CROSS_DOWN(self, n1=10, n2=5):
"""
死叉判断
"""
result = CROSS(MA(self.C, n1), MA(self.C, n2))
return result
def BARSLASTCOUNT_UP(self):
"""
连续上涨
"""
return BARSLASTCOUNT(self.C > self.O)
def BARSLASTCOUNT_DOWN(self):
"""
连续下跌
"""
return BARSLASTCOUNT(self.C < self.O)
def PRICE_MA_LINE_ANAL(self, n=5):
"""
价格在5均线上
"""
#IF(self.C >= MA(self.C, n), 0, 1)
return self.C >= MA(self.C, n)
def MA_LINE_ANAL(self, n1=5, n2=10):
"""
5均线在10均线上
"""
#IF(MA(self.C, n1) >= MA(self.C, n2), 0, 1)
return MA(self.C, n1) >= MA(self.C, n2)
def HHVBARS(self, n=5):
"""
5日最高值到当前周期
"""
return HHVBARS(self.C, n)
def LLVBARS(self, n=5):
"""
5日最低值到当前周期
"""
return LLVBARS(self.C, n)
def cacal_zdf(self, n=5):
"""
5日涨跌幅
"""
return (self.C / REF(self.C, n) - 1) * 100
def cacal_price_line_zdf(self, n=5):
"""
价格距离5日均线涨跌幅
"""
result=((self.C-MA(self.C,n))/MA(self.C,n))*100
return result
def cacal_line_line_zdf(self, n1=5,n2=10):
"""
5日均线距离10日均线涨跌幅
"""
result=((MA(self.C,n1)-MA(self.C,n2))/MA(self.C,n2))*100
return result
def cacal_skew(self,n=5):
'''
5日偏度
'''
result=self.C.rolling(window=n).skew()
return result
def cacal_kurt(self,n=5):
'''
5日峰度
'''
result=self.C.rolling(window=n).kurt()
return result
def calculate_momentum_score(self, n=3):
"""
n日回归动量 - 返回时间序列
"""
df = self.df.copy()
mom_daily = n
# 创建与df相同索引的Series，初始全部为NaN
result = pd.Series(index=df.index, dtype=float)
# 从 n-1 开始，因为需要至少 n 个数据点来计算
for i in range(mom_daily - 1, len(df)):
# 获取从 i-n+1 到 i 的窗口数据 (共 n 个数据点)
start_idx = i - mom_daily + 1
df_sub = df.iloc[start_idx:i+1].copy()
# 检查数据是否足够
if len(df_sub) < mom_daily:
continue
# 检查价格数据是否有效
close_data = df_sub['closePrice'].values
if np.any(np.isnan(close_data)) or np.any(np.isinf(close_data)) or np.any(close_data <= 0):
continue
try:
y = np.log(close_data)
y_len = len(y)
weights = np.linspace(1, 2, y_len)
x = np.arange(y_len)
slope, intercept = np.polyfit(x, y, 1, w=weights)
annualized_returns = math.pow(math.exp(slope), 250) - 1
residuals = y - (slope * x + intercept)
weighted_residuals = weights * residuals**2
y_mean = np.mean(y)
r_squared = 1 - (np.sum(weighted_residuals) / np.sum(weights * (y - y_mean)**2))
score = annualized_returns * r_squared
result.iloc[i] = score
except Exception as e:
continue
return result
def SLOPE(self, n=5):
'''
5日回归斜率
'''
result = SLOPE(self.close, N=n)
return result
def STD(self, n=5):
'''
5日标准差
'''
result = STD(self.close, N=n)
return result
# ===== 超卖超买类 =====
def CCI(self):
'''
CCI商品路径指标
'''
TYP = (self.H + self.L + self.C) / 3
result = (TYP - MA(TYP, 14)) * 1000 / (15 * AVEDEV(TYP, 14))
return result
def MFI(self):
'''
最近流量指标
'''
return MFI(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V, N=14)
def MTM_MTM(self):
'''动量线 - MTM值'''
mtm_val, mtmma_val = MTM(CLOSE=self.C, N=12, M=6)
return mtm_val
def MTM_MTMMA(self):
'''动量线 - MTMMA值'''
mtm_val, mtmma_val = MTM(CLOSE=self.C, N=12, M=6)
return mtmma_val
def RSI1(self):
'''相对强弱指标 - RSI1'''
rsi1_val, rsi2_val, rsi3_val = RSI(CLOSE=self.C, N1=6, N2=12, N3=24)
return rsi1_val
def RSI2(self):
'''相对强弱指标 - RSI2'''
rsi1_val, rsi2_val, rsi3_val = RSI(CLOSE=self.C, N1=6, N2=12, N3=24)
return rsi2_val
def RSI3(self):
'''相对强弱指标 - RSI3'''
rsi1_val, rsi2_val, rsi3_val = RSI(CLOSE=self.C, N1=6, N2=12, N3=24)
return rsi3_val
def KDJ_K(self):
'''KDJ指标 - K值'''
k_val, d_val, j_val = KDJ(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3, M2=3)
return k_val
def KDJ_D(self):
'''KDJ指标 - D值'''
k_val, d_val, j_val = KDJ(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3, M2=3)
return d_val
def KDJ_J(self):
'''KDJ指标 - J值'''
k_val, d_val, j_val = KDJ(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3, M2=3)
return j_val
def SKDJ_K(self):
'''慢速随机指标 - K值'''
k_val, d_val = SKDJ(CLOSE=self.C, LOW=self.L, HIGH=self.H, N=9, M=3)
return k_val
def SKDJ_D(self):
'''慢速随机指标 - D值'''
k_val, d_val = SKDJ(CLOSE=self.C, LOW=self.L, HIGH=self.H, N=9, M=3)
return d_val
def UDL_UDL(self):
'''引力线 - UDL值'''
udl_val, maudl_val = UDL(CLOSE=self.C, N1=3, N2=5, N3=10, N4=20, M=6)
return udl_val
def UDL_MAUDL(self):
'''引力线 - MAUDL值'''
udl_val, maudl_val = UDL(CLOSE=self.C, N1=3, N2=5, N3=10, N4=20, M=6)
return maudl_val
def WR1(self):
'''威廉指标 - WR1'''
wr1_val, wr2_val = WR(CLOSE=self.C, LOW=self.L, HIGH=self.H, N=10, N1=6)
return wr1_val
def WR2(self):
'''威廉指标 - WR2'''
wr1_val, wr2_val = WR(CLOSE=self.C, LOW=self.L, HIGH=self.H, N=10, N1=6)
return wr2_val
def LWR1(self):
'''LWR指标 - LWR1'''
lwr1_val, lwr2_val = LWR(CLOSE=self.C, LOW=self.L, HIGH=self.H, N=9, M1=3, M2=3)
return lwr1_val
def LWR2(self):
'''LWR指标 - LWR2'''
lwr1_val, lwr2_val = LWR(CLOSE=self.C, LOW=self.L, HIGH=self.H, N=9, M1=3, M2=3)
return lwr2_val
def MARSI1(self):
'''相对强弱平均线 - RSI1'''
rsi1_val, rsi2_val = MARSI(CLOSE=self.C, M1=10, M2=6)
return rsi1_val
def MARSI2(self):
'''相对强弱平均线 - RSI2'''
rsi1_val, rsi2_val = MARSI(CLOSE=self.C, M1=10, M2=6)
return rsi2_val
def BIAS1(self):
'''乖离率 - BIAS1(6日)'''
bias1_val, bias2_val, bias3_val = BIAS(CLOSE=self.C, N1=6, N2=12, N3=24)
return bias1_val
def BIAS2(self):
'''乖离率 - BIAS2(12日)'''
bias1_val, bias2_val, bias3_val = BIAS(CLOSE=self.C, N1=6, N2=12, N3=24)
return bias2_val
def BIAS3(self):
'''乖离率 - BIAS3(24日)'''
bias1_val, bias2_val, bias3_val = BIAS(CLOSE=self.C, N1=6, N2=12, N3=24)
return bias3_val
def BIAS_QL_BIAS(self):
'''乖离率-传统版 - BIAS值'''
bias_val, biasma_val = BIAS_QL(CLOSE=self.C, N=6, M=6)
return bias_val
def BIAS_QL_BIASMA(self):
'''乖离率-传统版 - BIASMA值'''
bias_val, biasma_val = BIAS_QL(CLOSE=self.C, N=6, M=6)
return biasma_val
def BIAS36_BIAS36(self):
'''三六乖离 - BIAS36'''
bias36_val, bias612_val, mabias_val = BIAS36(CLOSE=self.C, M=6)
return bias36_val
def BIAS36_BIAS612(self):
'''三六乖离 - BIAS612'''
bias36_val, bias612_val, mabias_val = BIAS36(CLOSE=self.C, M=6)
return bias612_val
def BIAS36_MABIAS(self):
'''三六乖离 - MABIAS'''
bias36_val, bias612_val, mabias_val = BIAS36(CLOSE=self.C, M=6)
return mabias_val
def ACCER(self):
'''幅度涨速'''
return ACCER(CLOSE=self.C, N=8)
# ===== 趋势类型 =====
def ASI_ASI(self):
'''振动升降指标 - ASI'''
asi_val, asit_val = ASI(OPEN=self.O, CLOSE=self.C, HIGH=self.H, LOW=self.L, M1=26, M2=10)
return asi_val
def ASI_ASIT(self):
'''振动升降指标 - ASIT'''
asi_val, asit_val = ASI(OPEN=self.O, CLOSE=self.C, HIGH=self.H, LOW=self.L, M1=26, M2=10)
return asit_val
def CHO_CHO(self):
'''佳庆指标 - CHO'''
cho_val, macho_val = CHO(CLOSE=self.C, OPEN=self.O, LOW=self.L, HIGH=self.H, VOL=self.V, N1=10, N2=20, M=6)
return cho_val
def CHO_MACHO(self):
'''佳庆指标 - MACHO'''
cho_val, macho_val = CHO(CLOSE=self.C, OPEN=self.O, LOW=self.L, HIGH=self.H, VOL=self.V, N1=10, N2=20, M=6)
return macho_val
def DMA_XT_DIF(self):
'''平均差 - DIF'''
dif_val, difma_val = DMA_XT(CLOSE=self.C, N1=10, N2=50, M=10)
return dif_val
def DMA_XT_DIFMA(self):
'''平均差 - DIFMA'''
dif_val, difma_val = DMA_XT(CLOSE=self.C, N1=10, N2=50, M=10)
return difma_val
def DMI_PDI(self):
'''趋向指标 - PDI'''
pdi_val, mdi_val, adx_val, adxr_val = DMI(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=14, M=6)
return pdi_val
def DMI_MDI(self):
'''趋向指标 - MDI'''
pdi_val, mdi_val, adx_val, adxr_val = DMI(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=14, M=6)
return mdi_val
def DMI_ADX(self):
'''趋向指标 - ADX'''
pdi_val, mdi_val, adx_val, adxr_val = DMI(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=14, M=6)
return adx_val
def DMI_ADXR(self):
'''趋向指标 - ADXR'''
pdi_val, mdi_val, adx_val, adxr_val = DMI(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=14, M=6)
return adxr_val
def DPO_DPO(self):
'''区间震荡线 - DPO'''
dpo_val, madpo_val = DPO(CLOSE=self.C, N=21, M=6)
return dpo_val
def DPO_MADPO(self):
'''区间震荡线 - MADPO'''
dpo_val, madpo_val = DPO(CLOSE=self.C, N=21, M=6)
return madpo_val
def EMV_EMV(self):
'''简易波动指标 - EMV'''
emv_val, maemv_val = EMV(HIGH=self.H, LOW=self.L, VOL=self.V, N=14, M=9)
return emv_val
def EMV_MAEMV(self):
'''简易波动指标 - MAEMV'''
emv_val, maemv_val = EMV(HIGH=self.H, LOW=self.L, VOL=self.V, N=14, M=9)
return maemv_val
def MACD_DIF(self):
'''平滑异同平均线 - DIF'''
dif_val, dea_val, macd_val = MACD(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return dif_val
def MACD_DEA(self):
'''平滑异同平均线 - DEA'''
dif_val, dea_val, macd_val = MACD(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return dea_val
def MACD_MACD(self):
'''平滑异同平均线 - MACD'''
dif_val, dea_val, macd_val = MACD(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return macd_val
def VMACD_DIF(self):
'''量平滑异同平均线 - DIF'''
dif_val, dea_val, macd_val = VMACD(VOL=self.V, SHORT=12, LONG=26, MID=9)
return dif_val
def VMACD_DEA(self):
'''量平滑异同平均线 - DEA'''
dif_val, dea_val, macd_val = VMACD(VOL=self.V, SHORT=12, LONG=26, MID=9)
return dea_val
def VMACD_MACD(self):
'''量平滑异同平均线 - MACD'''
dif_val, dea_val, macd_val = VMACD(VOL=self.V, SHORT=12, LONG=26, MID=9)
return macd_val
def SMACD_DEA(self):
'''单线平滑异同平均线 - DEA'''
dea_val, macd_val = SMACD(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return dea_val
def SMACD_MACD(self):
'''单线平滑异同平均线 - MACD'''
dea_val, macd_val = SMACD(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return macd_val
def QACD_DIF(self):
'''快速异同平均线 - DIF'''
dif_val, macd_val, ddif_val = QACD(CLOSE=self.C, N1=12, N2=12, M=9)
return dif_val
def QACD_MACD(self):
'''快速异同平均线 - MACD'''
dif_val, macd_val, ddif_val = QACD(CLOSE=self.C, N1=12, N2=12, M=9)
return macd_val
def QACD_DDIF(self):
'''快速异同平均线 - DDIF'''
dif_val, macd_val, ddif_val = QACD(CLOSE=self.C, N1=12, N2=12, M=9)
return ddif_val
def TRIX_TRIX(self):
'''三重指数平均线 - TRIX'''
trix_val, matrix_val = TRIX(CLOSE=self.C, N=12, M=9)
return trix_val
def TRIX_MATRIX(self):
'''三重指数平均线 - MATRIX'''
trix_val, matrix_val = TRIX(CLOSE=self.C, N=12, M=9)
return matrix_val
def UOS_UOS(self):
'''终极指标 - UOS'''
uos_val, mauos_val = UOS(CLOSE=self.C, HIGH=self.H, LOW=self.L, N1=7, N2=14, N3=28, M=6)
return uos_val
def UOS_MAUOS(self):
'''终极指标 - MAUOS'''
uos_val, mauos_val = UOS(CLOSE=self.C, HIGH=self.H, LOW=self.L, N1=7, N2=14, N3=28, M=6)
return mauos_val
def VTP_VPT(self):
'''量价曲线 - VPT'''
vpt_val, mavp_val = VTP(CLOSE=self.C, VOL=self.V, N=51, M=6)
return vpt_val
def VTP_MAVP(self):
'''量价曲线 - MAVP'''
vpt_val, mavp_val = VTP(CLOSE=self.C, VOL=self.V, N=51, M=6)
return mavp_val
def WVAD_WVAD(self):
'''威廉变异离散量 - WVAD'''
wvad_val, mawvad_val = WVAD(CLOSE=self.C, OPEN=self.O, HIGH=self.H, LOW=self.L, VOL=self.V, N=24, M=6)
return wvad_val
def WVAD_MAWVAD(self):
'''威廉变异离散量 - MAWVAD'''
wvad_val, mawvad_val = WVAD(CLOSE=self.C, OPEN=self.O, HIGH=self.H, LOW=self.L, VOL=self.V, N=24, M=6)
return mawvad_val
def JS_JS(self):
'''加数线 - JS'''
js_val, majs1_val, majs2_val, majs3_val = JS(CLOSE=self.C, N=5, M1=5, M2=10, M3=20)
return js_val
def JS_MAJS1(self):
'''加数线 - MAJS1'''
js_val, majs1_val, majs2_val, majs3_val = JS(CLOSE=self.C, N=5, M1=5, M2=10, M3=20)
return majs1_val
def JS_MAJS2(self):
'''加数线 - MAJS2'''
js_val, majs1_val, majs2_val, majs3_val = JS(CLOSE=self.C, N=5, M1=5, M2=10, M3=20)
return majs2_val
def JS_MAJS3(self):
'''加数线 - MAJS3'''
js_val, majs1_val, majs2_val, majs3_val = JS(CLOSE=self.C, N=5, M1=5, M2=10, M3=20)
return majs3_val
def CYE_CYEL(self):
'''市场趋势 - CYEL'''
cyel_val, cyes_val = CYE(CLOSE=self.C)
return cyel_val
def CYE_CYES(self):
'''市场趋势 - CYES'''
cyel_val, cyes_val = CYE(CLOSE=self.C)
return cyes_val
def GDX_轨道(self):
'''轨道线 - 轨道'''
轨道_val, 压力线_val, 支撑线_val = GDX(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=30, M=9)
return 轨道_val
def GDX_压力线(self):
'''轨道线 - 压力线'''
轨道_val, 压力线_val, 支撑线_val = GDX(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=30, M=9)
return 压力线_val
def GDX_支撑线(self):
'''轨道线 - 支撑线'''
轨道_val, 压力线_val, 支撑线_val = GDX(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=30, M=9)
return 支撑线_val
def JLHB_B(self):
'''绝路航标 - B'''
b_val, var2_val, 绝路航标_val = JLHB(CLOSE=self.C, LOW=self.L,HIGH=self.H, N=7, M=5)
return b_val
def JLHB_VAR2(self):
'''绝路航标 - VAR2'''
b_val, var2_val, 绝路航标_val = JLHB(CLOSE=self.C, LOW=self.L,HIGH=self.H, N=7, M=5)
return var2_val
def JLHB_绝路航标(self):
'''绝路航标 - 绝路航标'''
b_val, var2_val, 绝路航标_val = JLHB(CLOSE=self.C, LOW=self.L,HIGH=self.H, N=7, M=5)
return 绝路航标_val
# ===== 能量类型 =====
def BRAR_BR(self):
'''情绪指标 - BR'''
br_val, ar_val = BRAR(OPEN=self.O, HIGH=self.H, LOW=self.L,CLOSE=self.C, N=26)
return br_val
def BRAR_AR(self):
'''情绪指标 - AR'''
br_val, ar_val = BRAR(OPEN=self.O, HIGH=self.H, LOW=self.L,CLOSE=self.C, N=26)
return ar_val
def CR_CR(self):
'''带状能量线 - CR'''
cr_val, ma1_val, ma2_val, ma3_val, ma4_val = CR(HIGH=self.H, LOW=self.L, N=26, M1=10, M2=20, M3=40, M4=60)
return cr_val
def CR_MA1(self):
'''带状能量线 - MA1'''
cr_val, ma1_val, ma2_val, ma3_val, ma4_val = CR(HIGH=self.H, LOW=self.L, N=26, M1=10, M2=20, M3=40, M4=60)
return ma1_val
def CR_MA2(self):
'''带状能量线 - MA2'''
cr_val, ma1_val, ma2_val, ma3_val, ma4_val = CR(HIGH=self.H, LOW=self.L, N=26, M1=10, M2=20, M3=40, M4=60)
return ma2_val
def CR_MA3(self):
'''带状能量线 - MA3'''
cr_val, ma1_val, ma2_val, ma3_val, ma4_val = CR(HIGH=self.H, LOW=self.L, N=26, M1=10, M2=20, M3=40, M4=60)
return ma3_val
def CR_MA4(self):
'''带状能量线 - MA4'''
cr_val, ma1_val, ma2_val, ma3_val, ma4_val = CR(HIGH=self.H, LOW=self.L, N=26, M1=10, M2=20, M3=40, M4=60)
return ma4_val
def MASS_MASS(self):
'''梅斯线 - MASS'''
mass_val, mamass_val = MASS(HIGH=self.H, LOW=self.L, N1=9, N2=25, M=6)
return mass_val
def MASS_MAMASS(self):
'''梅斯线 - MAMASS'''
mass_val, mamass_val = MASS(HIGH=self.H, LOW=self.L, N1=9, N2=25, M=6)
return mamass_val
def PSY_PSY(self):
'''心理线 - PSY'''
psy_val, psyma_val = PSY(CLOSE=self.C, N=12, M=6)
return psy_val
def PSY_PSYMA(self):
'''心理线 - PSYMA'''
psy_val, psyma_val = PSY(CLOSE=self.C, N=12, M=6)
return psyma_val
def VR_VR(self):
'''成交量变异率 - VR'''
vr_val, mavr_val = VR(CLOSE=self.C,VOL=self.V, N=26, M=6)
return vr_val
def VR_MAVR(self):
'''成交量变异率 - MAVR'''
vr_val, mavr_val = VR(CLOSE=self.C,VOL=self.V, N=26, M=6)
return mavr_val
def WAD_WAD(self):
'''威廉多空力度线 - WAD'''
wad_val, mawad_val = WAD(CLOSE=self.C, LOW=self.L,HIGH=self.H, M=30)
return wad_val
def WAD_MAWAD(self):
'''威廉多空力度线 - MAWAD'''
wad_val, mawad_val = WAD(CLOSE=self.C, LOW=self.L,HIGH=self.H, M=30)
return mawad_val
def PCNT_PCNT(self):
'''幅度比 - PCNT'''
pcnt_val, mapcnt_val = PCNT(CLOSE=self.C, M=5)
return pcnt_val
def PCNT_MAPCNT(self):
'''幅度比 - MAPCNT'''
pcnt_val, mapcnt_val = PCNT(CLOSE=self.C, M=5)
return mapcnt_val
def CYR_CYR(self):
'''市场强弱 - CYR'''
cyr_val, macyr_val = CYR(AMOUNT=self.AMOUNT,VOL=self.V, N=13, M=5)
return cyr_val
def CYR_MACYR(self):
'''市场强弱 - MACYR'''
cyr_val, macyr_val = CYR(AMOUNT=self.AMOUNT,VOL=self.V, N=13, M=5)
return macyr_val
# ===== 能量型 =====
def AMO_AMOW(self):
'''成交金额 - AMOW'''
amow_val, amo1_val, amo2_val = AMO(AMOUNT=self.AMOUNT, M1=5, M2=10)
return amow_val
def AMO_AMO1(self):
'''成交金额 - AMO1'''
amow_val, amo1_val, amo2_val = AMO(AMOUNT=self.AMOUNT, M1=5, M2=10)
return amo1_val
def AMO_AMO2(self):
'''成交金额 - AMO2'''
amow_val, amo1_val, amo2_val = AMO(AMOUNT=self.AMOUNT, M1=5, M2=10)
return amo2_val
def OBV_OBV(self):
'''累积能量线 - OBV'''
obv_val, maobv_val = OBV(VOL=self.V, CLOSE=self.C, M=30)
return obv_val
def OBV_MAOBV(self):
'''累积能量线 - MAOBV'''
obv_val, maobv_val = OBV(VOL=self.V, CLOSE=self.C, M=30)
return maobv_val
def VOL_XT_MAVOL1(self):
'''成交量 - MAVOL1'''
mavol1_val, mavol2_val = VOL_XT(VOL=self.V, M1=5, M2=10)
return mavol1_val
def VOL_XT_MAVOL2(self):
'''成交量 - MAVOL2'''
mavol1_val, mavol2_val = VOL_XT(VOL=self.V, M1=5, M2=10)
return mavol2_val
def VRSI1(self):
'''相对强弱量 - RSI1'''
rsi1_val, rsi2_val, rsi3_val = VRSI(VOL=self.V, N1=6, N2=12, N3=24)
return rsi1_val
def VRSI2(self):
'''相对强弱量 - RSI2'''
rsi1_val, rsi2_val, rsi3_val = VRSI(VOL=self.V, N1=6, N2=12, N3=24)
return rsi2_val
def VRSI3(self):
'''相对强弱量 - RSI3'''
rsi1_val, rsi2_val, rsi3_val = VRSI(VOL=self.V, N1=6, N2=12, N3=24)
return rsi3_val
def HSL_HSL(self):
'''换手线 - HSL'''
hsl_val, mahsl_val = HSL(HSL=self.V, N=5)
return hsl_val
def HSL_MAHSL(self):
'''换手线 - MAHSL'''
hsl_val, mahsl_val = HSL(HSL=self.V, N=5)
return mahsl_val
# ===== 均线系统 =====
def MA_XT_MA1(self):
'''均线 - MA1(5日)'''
ma1_val, ma2_val, ma3_val, ma4_val = MA_XT(CLOSE=self.C, M1=5, M2=10, M3=20, M4=60)
return ma1_val
def MA_XT_MA2(self):
'''均线 - MA2(10日)'''
ma1_val, ma2_val, ma3_val, ma4_val = MA_XT(CLOSE=self.C, M1=5, M2=10, M3=20, M4=60)
return ma2_val
def MA_XT_MA3(self):
'''均线 - MA3(20日)'''
ma1_val, ma2_val, ma3_val, ma4_val = MA_XT(CLOSE=self.C, M1=5, M2=10, M3=20, M4=60)
return ma3_val
def MA_XT_MA4(self):
'''均线 - MA4(60日)'''
ma1_val, ma2_val, ma3_val, ma4_val = MA_XT(CLOSE=self.C, M1=5, M2=10, M3=20, M4=60)
return ma4_val
def ACD_ACD(self):
'''升降线 - ACD'''
acd_val, maacd_val = ACD(CLOSE=self.C, HIGH=self.H, LOW=self.L, M=20)
return acd_val
def ACD_MAACD(self):
'''升降线 - MAACD'''
acd_val, maacd_val = ACD(CLOSE=self.C, HIGH=self.H, LOW=self.L, M=20)
return maacd_val
def BBI(self):
'''多空均线'''
return BBI(CLOSE=self.C, M1=3, M2=6, M3=12, M4=24)
def EXPMA_EXP1(self):
'''指数平均线 - EXP1(12日)'''
exp1_val, exp2_val = EXPMA(CLOSE=self.C, M1=12, M2=50)
return exp1_val
def EXPMA_EXP2(self):
'''指数平均线 - EXP2(50日)'''
exp1_val, exp2_val = EXPMA(CLOSE=self.C, M1=12, M2=50)
return exp2_val
def HMA_HMA1(self):
'''高价平均线 - HMA1'''
hma1_val, hma2_val, hma3_val, hma4_val, hma5_val = HMA(HIGH=self.H, M1=6, M2=12, M3=30, M4=70, M5=90)
return hma1_val
def HMA_HMA2(self):
'''高价平均线 - HMA2'''
hma1_val, hma2_val, hma3_val, hma4_val, hma5_val = HMA(HIGH=self.H, M1=6, M2=12, M3=30, M4=70, M5=90)
return hma2_val
def HMA_HMA3(self):
'''高价平均线 - HMA3'''
hma1_val, hma2_val, hma3_val, hma4_val, hma5_val = HMA(HIGH=self.H, M1=6, M2=12, M3=30, M4=70, M5=90)
return hma3_val
def HMA_HMA4(self):
'''高价平均线 - HMA4'''
hma1_val, hma2_val, hma3_val, hma4_val, hma5_val = HMA(HIGH=self.H, M1=6, M2=12, M3=30, M4=70, M5=90)
return hma4_val
def HMA_HMA5(self):
'''高价平均线 - HMA5'''
hma1_val, hma2_val, hma3_val, hma4_val, hma5_val = HMA(HIGH=self.H, M1=6, M2=12, M3=30, M4=70, M5=90)
return hma5_val
def LMA_LMA1(self):
'''低价平均线 - LMA1'''
lma1_val, lma2_val, lma3_val, lma4_val, lma5_val = LMA(LOW=self.L, M1=6, M2=12, M3=30, M4=70, M5=90)
return lma1_val
def LMA_LMA2(self):
'''低价平均线 - LMA2'''
lma1_val, lma2_val, lma3_val, lma4_val, lma5_val = LMA(LOW=self.L, M1=6, M2=12, M3=30, M4=70, M5=90)
return lma2_val
def LMA_LMA3(self):
'''低价平均线 - LMA3'''
lma1_val, lma2_val, lma3_val, lma4_val, lma5_val = LMA(LOW=self.L, M1=6, M2=12, M3=30, M4=70, M5=90)
return lma3_val
def LMA_LMA4(self):
'''低价平均线 - LMA4'''
lma1_val, lma2_val, lma3_val, lma4_val, lma5_val = LMA(LOW=self.L, M1=6, M2=12, M3=30, M4=70, M5=90)
return lma4_val
def LMA_LMA5(self):
'''低价平均线 - LMA5'''
lma1_val, lma2_val, lma3_val, lma4_val, lma5_val = LMA(LOW=self.L, M1=6, M2=12, M3=30, M4=70, M5=90)
return lma5_val
def VMA_VMA1(self):
'''变异平均线 - VMA1'''
vma1_val, vma2_val, vma3_val, vma4_val, vma5_val = VMA(HIGH=self.H, OPEN=self.O, LOW=self.L, CLOSE=self.C, M1=6, M2=12, M3=30, M4=70, M5=90)
return vma1_val
def VMA_VMA2(self):
'''变异平均线 - VMA2'''
vma1_val, vma2_val, vma3_val, vma4_val, vma5_val = VMA(HIGH=self.H, OPEN=self.O, LOW=self.L, CLOSE=self.C, M1=6, M2=12, M3=30, M4=70, M5=90)
return vma2_val
def VMA_VMA3(self):
'''变异平均线 - VMA3'''
vma1_val, vma2_val, vma3_val, vma4_val, vma5_val = VMA(HIGH=self.H, OPEN=self.O, LOW=self.L, CLOSE=self.C, M1=6, M2=12, M3=30, M4=70, M5=90)
return vma3_val
def VMA_VMA4(self):
'''变异平均线 - VMA4'''
vma1_val, vma2_val, vma3_val, vma4_val, vma5_val = VMA(HIGH=self.H, OPEN=self.O, LOW=self.L, CLOSE=self.C, M1=6, M2=12, M3=30, M4=70, M5=90)
return vma4_val
def VMA_VMA5(self):
'''变异平均线 - VMA5'''
vma1_val, vma2_val, vma3_val, vma4_val, vma5_val = VMA(HIGH=self.H, OPEN=self.O, LOW=self.L, CLOSE=self.C, M1=6, M2=12, M3=30, M4=70, M5=90)
return vma5_val
def AMV_AMV1(self):
'''成本均线 - AMV1(5日)'''
amv1_val, amv2_val, amv3_val, amv4_val = AMV(OPEN=self.O, CLOSE=self.C, VOL=self.V, M1=5, M2=13, M3=34, M4=60)
return amv1_val
def AMV_AMV2(self):
'''成本均线 - AMV2(13日)'''
amv1_val, amv2_val, amv3_val, amv4_val = AMV(OPEN=self.O, CLOSE=self.C, VOL=self.V, M1=5, M2=13, M3=34, M4=60)
return amv2_val
def AMV_AMV3(self):
'''成本均线 - AMV3(34日)'''
amv1_val, amv2_val, amv3_val, amv4_val = AMV(OPEN=self.O, CLOSE=self.C, VOL=self.V, M1=5, M2=13, M3=34, M4=60)
return amv3_val
def AMV_AMV4(self):
'''成本均线 - AMV4(60日)'''
amv1_val, amv2_val, amv3_val, amv4_val = AMV(OPEN=self.O, CLOSE=self.C, VOL=self.V, M1=5, M2=13, M3=34, M4=60)
return amv4_val
def BBIBOLL_BBIBOLL(self):
'''多空布林线 - BBIBOLL'''
bbiboll_val, upr_val, dwn_val = BBIBOLL(CLOSE=self.C, N=11, M=6)
return bbiboll_val
def BBIBOLL_UPR(self):
'''多空布林线 - UPR'''
bbiboll_val, upr_val, dwn_val = BBIBOLL(CLOSE=self.C, N=11, M=6)
return upr_val
def BBIBOLL_DWN(self):
'''多空布林线 - DWN'''
bbiboll_val, upr_val, dwn_val = BBIBOLL(CLOSE=self.C, N=11, M=6)
return dwn_val
def ALLIGAT_上唇(self):
'''鳄鱼线 - 上唇'''
上唇_val, 牙齿_val, 下颚_val = ALLIGAT(HIGH=self.H, LOW=self.L)
return 上唇_val
def ALLIGAT_牙齿(self):
'''鳄鱼线 - 牙齿'''
上唇_val, 牙齿_val, 下颚_val = ALLIGAT(HIGH=self.H, LOW=self.L)
return 牙齿_val
def ALLIGAT_下颚(self):
'''鳄鱼线 - 下颚'''
上唇_val, 牙齿_val, 下颚_val = ALLIGAT(HIGH=self.H, LOW=self.L)
return 下颚_val
def GMMA_MA3(self):
'''顾比均线 - MA3'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma3_val
def GMMA_MA5(self):
'''顾比均线 - MA5'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma5_val
def GMMA_MA8(self):
'''顾比均线 - MA8'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma8_val
def GMMA_MA10(self):
'''顾比均线 - MA10'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma10_val
def GMMA_MA12(self):
'''顾比均线 - MA12'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma12_val
def GMMA_MA15(self):
'''顾比均线 - MA15'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma15_val
def GMMA_MA30(self):
'''顾比均线 - MA30'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma30_val
def GMMA_MA35(self):
'''顾比均线 - MA35'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma35_val
def GMMA_MA40(self):
'''顾比均线 - MA40'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma40_val
def GMMA_MA45(self):
'''顾比均线 - MA45'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma45_val
def GMMA_MA50(self):
'''顾比均线 - MA50'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma50_val
def GMMA_MA60(self):
'''顾比均线 - MA60'''
ma3_val, ma5_val, ma8_val, ma10_val, ma12_val, ma15_val, ma30_val, ma35_val, ma40_val, ma45_val, ma50_val, ma60_val = GMMA(CLOSE=self.C)
return ma60_val
# ===== 路径类 =====
def BOLL_BOLL(self):
'''布林线 - BOLL'''
boll_val, ub_val, lb_val = BOLL(CLOSE=self.C, M=20)
return boll_val
def BOLL_UB(self):
'''布林线 - UB'''
boll_val, ub_val, lb_val = BOLL(CLOSE=self.C, M=20)
return ub_val
def BOLL_LB(self):
'''布林线 - LB'''
boll_val, ub_val, lb_val = BOLL(CLOSE=self.C, M=20)
return lb_val
def PBX_PBX1(self):
'''瀑布线 - PBX1'''
pbx1_val, pbx2_val, pbx3_val, pbx4_val, pbx5_val, pbx6_val = PBX(CLOSE=self.C, M1=4, M2=6, M3=9, M4=13, M5=18, M6=24)
return pbx1_val
def PBX_PBX2(self):
'''瀑布线 - PBX2'''
pbx1_val, pbx2_val, pbx3_val, pbx4_val, pbx5_val, pbx6_val = PBX(CLOSE=self.C, M1=4, M2=6, M3=9, M4=13, M5=18, M6=24)
return pbx2_val
def PBX_PBX3(self):
'''瀑布线 - PBX3'''
pbx1_val, pbx2_val, pbx3_val, pbx4_val, pbx5_val, pbx6_val = PBX(CLOSE=self.C, M1=4, M2=6, M3=9, M4=13, M5=18, M6=24)
return pbx3_val
def PBX_PBX4(self):
'''瀑布线 - PBX4'''
pbx1_val, pbx2_val, pbx3_val, pbx4_val, pbx5_val, pbx6_val = PBX(CLOSE=self.C, M1=4, M2=6, M3=9, M4=13, M5=18, M6=24)
return pbx4_val
def PBX_PBX5(self):
'''瀑布线 - PBX5'''
pbx1_val, pbx2_val, pbx3_val, pbx4_val, pbx5_val, pbx6_val = PBX(CLOSE=self.C, M1=4, M2=6, M3=9, M4=13, M5=18, M6=24)
return pbx5_val
def PBX_PBX6(self):
'''瀑布线 - PBX6'''
pbx1_val, pbx2_val, pbx3_val, pbx4_val, pbx5_val, pbx6_val = PBX(CLOSE=self.C, M1=4, M2=6, M3=9, M4=13, M5=18, M6=24)
return pbx6_val
def ENE_UPPER(self):
'''轨道线 - UPPER'''
upper_val, lower_val, ene_val = ENE(CLOSE=self.C, N=25, M1=6, M2=6)
return upper_val
def ENE_LOWER(self):
'''轨道线 - LOWER'''
upper_val, lower_val, ene_val = ENE(CLOSE=self.C, N=25, M1=6, M2=6)
return lower_val
def ENE_ENE(self):
'''轨道线 - ENE'''
upper_val, lower_val, ene_val = ENE(CLOSE=self.C, N=25, M1=6, M2=6)
return ene_val
def MIKE_STOR(self):
'''麦克支撑压力 - STOR'''
stor_val, midr_val, wekr_val, weks_val, mids_val, stos_val = MIKE(HIGH=self.H, LOW=self.L, CLOSE=self.C, N=10)
return stor_val
def MIKE_MIDR(self):
'''麦克支撑压力 - MIDR'''
stor_val, midr_val, wekr_val, weks_val, mids_val, stos_val = MIKE(HIGH=self.H, LOW=self.L, CLOSE=self.C, N=10)
return midr_val
def MIKE_WEKR(self):
'''麦克支撑压力 - WEKR'''
stor_val, midr_val, wekr_val, weks_val, mids_val, stos_val = MIKE(HIGH=self.H, LOW=self.L, CLOSE=self.C, N=10)
return wekr_val
def MIKE_WEKS(self):
'''麦克支撑压力 - WEKS'''
stor_val, midr_val, wekr_val, weks_val, mids_val, stos_val = MIKE(HIGH=self.H, LOW=self.L, CLOSE=self.C, N=10)
return weks_val
def MIKE_MIDS(self):
'''麦克支撑压力 - MIDS'''
stor_val, midr_val, wekr_val, weks_val, mids_val, stos_val = MIKE(HIGH=self.H, LOW=self.L, CLOSE=self.C, N=10)
return mids_val
def MIKE_STOS(self):
'''麦克支撑压力 - STOS'''
stor_val, midr_val, wekr_val, weks_val, mids_val, stos_val = MIKE(HIGH=self.H, LOW=self.L, CLOSE=self.C, N=10)
return stos_val
def XS_SUP(self):
'''薛斯通道 - SUP'''
sup_val, sdn_val, lup_val, ldn_val = XS(CLOSE=self.C, VOL=self.V, N=13)
return sup_val
def XS_SDN(self):
'''薛斯通道 - SDN'''
sup_val, sdn_val, lup_val, ldn_val = XS(CLOSE=self.C, VOL=self.V, N=13)
return sdn_val
def XS_LUP(self):
'''薛斯通道 - LUP'''
sup_val, sdn_val, lup_val, ldn_val = XS(CLOSE=self.C, VOL=self.V, N=13)
return lup_val
def XS_LDN(self):
'''薛斯通道 - LDN'''
sup_val, sdn_val, lup_val, ldn_val = XS(CLOSE=self.C, VOL=self.V, N=13)
return ldn_val
def TQN_周期高点(self):
'''唐奇安通道 - 周期高点'''
周期高点_val, 周期低点_val, 平空开多_val, 平多开空_val = TQN(HIGH=self.H, LOW=self.L, X1=20, X2=20)
return 周期高点_val
def TQN_周期低点(self):
'''唐奇安通道 - 周期低点'''
周期高点_val, 周期低点_val, 平空开多_val, 平多开空_val = TQN(HIGH=self.H, LOW=self.L, X1=20, X2=20)
return 周期低点_val
def TQN_平空开多(self):
'''唐奇安通道 - 平空开多信号'''
周期高点_val, 周期低点_val, 平空开多_val, 平多开空_val = TQN(HIGH=self.H, LOW=self.L, X1=20, X2=20)
return 平空开多_val
def TQN_平多开空(self):
'''唐奇安通道 - 平多开空信号'''
周期高点_val, 周期低点_val, 平空开多_val, 平多开空_val = TQN(HIGH=self.H, LOW=self.L, X1=20, X2=20)
return 平多开空_val
# ===== 停损 =====
def SAR(self):
'''抛物线指标'''
return SAR(HIGH=self.H, LOW=self.L, M=10, af=2, amax=20)
# ===== 交易类型 =====
def MA_交易_MA1(self):
'''MA交易 - MA1(短期均线)'''
ma1_val, ma2_val, 平空开多_val, 平多开空_val = MA_交易(CLOSE=self.C, SHORT=5, LONG=20)
return ma1_val
def MA_交易_MA2(self):
'''MA交易 - MA2(长期均线)'''
ma1_val, ma2_val, 平空开多_val, 平多开空_val = MA_交易(CLOSE=self.C, SHORT=5, LONG=20)
return ma2_val
def MA_交易_平空开多(self):
'''MA交易 - 平空开多信号'''
ma1_val, ma2_val, 平空开多_val, 平多开空_val = MA_交易(CLOSE=self.C, SHORT=5, LONG=20)
return 平空开多_val
def MA_交易_平多开空(self):
'''MA交易 - 平多开空信号'''
ma1_val, ma2_val, 平空开多_val, 平多开空_val = MA_交易(CLOSE=self.C, SHORT=5, LONG=20)
return 平多开空_val
def MACD_交易_DIFF(self):
'''MACD交易 - DIFF'''
diff_val, dea_val, macd_val, 平空开多_val, 平多开空_val = MACD_交易(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return diff_val
def MACD_交易_DEA(self):
'''MACD交易 - DEA'''
diff_val, dea_val, macd_val, 平空开多_val, 平多开空_val = MACD_交易(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return dea_val
def MACD_交易_MACD(self):
'''MACD交易 - MACD'''
diff_val, dea_val, macd_val, 平空开多_val, 平多开空_val = MACD_交易(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return macd_val
def MACD_交易_平空开多(self):
'''MACD交易 - 平空开多信号'''
diff_val, dea_val, macd_val, 平空开多_val, 平多开空_val = MACD_交易(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return 平空开多_val
def MACD_交易_平多开空(self):
'''MACD交易 - 平多开空信号'''
diff_val, dea_val, macd_val, 平空开多_val, 平多开空_val = MACD_交易(CLOSE=self.C, SHORT=12, LONG=26, MID=9)
return 平多开空_val
def KDJ_交易_K(self):
'''KDJ交易 - K值'''
k_val, d_val, j_val, 平空开多_val, 平多开空_val = KDJ_交易(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3)
return k_val
def KDJ_交易_D(self):
'''KDJ交易 - D值'''
k_val, d_val, j_val, 平空开多_val, 平多开空_val = KDJ_交易(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3)
return d_val
def KDJ_交易_J(self):
'''KDJ交易 - J值'''
k_val, d_val, j_val, 平空开多_val, 平多开空_val = KDJ_交易(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3)
return j_val
def KDJ_交易_平空开多(self):
'''KDJ交易 - 平空开多信号'''
k_val, d_val, j_val, 平空开多_val, 平多开空_val = KDJ_交易(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3)
return 平空开多_val
def KDJ_交易_平多开空(self):
'''KDJ交易 - 平多开空信号'''
k_val, d_val, j_val, 平空开多_val, 平多开空_val = KDJ_交易(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=9, M1=3)
return 平多开空_val
# ===== 神系 =====
def SG_XDT_QR(self):
'''心电图 - QR强弱指标'''
qr_val, mqr1_val, mqr2_val = SG_XDT(CLOSE=self.C, INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series())
return qr_val
def SG_XDT_MQR1(self):
'''心电图 - MQR1(5日均线)'''
qr_val, mqr1_val, mqr2_val = SG_XDT(CLOSE=self.C, INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series())
return mqr1_val
def SG_XDT_MQR2(self):
'''心电图 - MQR2(10日均线)'''
qr_val, mqr1_val, mqr2_val = SG_XDT(CLOSE=self.C, INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series())
return mqr2_val
def SG_NDB_DK(self):
'''脑电波 - DK'''
dk_val, mdk1_val, mdk2_val = SG_NDB(CLOSE=self.C, HIGH=self.H, LOW=self.L, P1=5, P2=10)
return dk_val
def SG_NDB_MDK1(self):
'''脑电波 - MDK1'''
dk_val, mdk1_val, mdk2_val = SG_NDB(CLOSE=self.C, HIGH=self.H, LOW=self.L, P1=5, P2=10)
return mdk1_val
def SG_NDB_MDK2(self):
'''脑电波 - MDK2'''
dk_val, mdk1_val, mdk2_val = SG_NDB(CLOSE=self.C, HIGH=self.H, LOW=self.L, P1=5, P2=10)
return mdk2_val
def SG_SMX_ZY1(self):
'''生命线 - ZY1(3日EMA)'''
zy1_val, zy2_val, zy3_val = SG_SMX(CLOSE=self.C, HIGH=self.H, LOW=self.L,
INDEXH=self.index_df['high'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXL=self.index_df['low'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
N=50)
return zy1_val
def SG_SMX_ZY2(self):
'''生命线 - ZY2(17日EMA)'''
zy1_val, zy2_val, zy3_val = SG_SMX(CLOSE=self.C, HIGH=self.H, LOW=self.L,
INDEXH=self.index_df['high'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXL=self.index_df['low'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
N=50)
return zy2_val
def SG_SMX_ZY3(self):
'''生命线 - ZY3(34日EMA)'''
zy1_val, zy2_val, zy3_val = SG_SMX(CLOSE=self.C, HIGH=self.H, LOW=self.L,
INDEXH=self.index_df['high'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXL=self.index_df['low'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
N=50)
return zy3_val
def SG_LB_量比(self):
'''量比'''
量比_val, ma5_val, ma10_val = SG_LB(VOL=self.V, INDEXV=self.index_df['volume'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series())
return 量比_val
def SG_LB_MA5(self):
'''量比 - MA5'''
量比_val, ma5_val, ma10_val = SG_LB(VOL=self.V, INDEXV=self.index_df['volume'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series())
return ma5_val
def SG_LB_MA10(self):
'''量比 - MA10'''
量比_val, ma5_val, ma10_val = SG_LB(VOL=self.V, INDEXV=self.index_df['volume'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series())
return ma10_val
def SG_PF(self):
'''强势股评分'''
return SG_PF(CLOSE=self.C, INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series())
# ===== 龙系 =====
def RAD_RADER1(self):
'''威力雷达 - RADER1'''
rader1_val, rader_ma_val = RAD(OPEN=self.O, HIGH=self.H, CLOSE=self.C, LOW=self.L,
INDEXO=self.index_df['open'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXH=self.index_df['high'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXL=self.index_df['low'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
D=3, S=30, M=30)
return rader1_val
def RAD_RADERMA(self):
'''威力雷达 - RADERMA'''
rader1_val, rader_ma_val = RAD(OPEN=self.O, HIGH=self.H, CLOSE=self.C, LOW=self.L,
INDEXO=self.index_df['open'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXH=self.index_df['high'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXL=self.index_df['low'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
INDEXC=self.index_df['close'] if hasattr(self, 'index_df') and not self.index_df.empty else pd.Series(),
D=3, S=30, M=30)
return rader_ma_val
def LON_LON(self):
'''龙系长线 - LON'''
lon_val, lonma_val, lont_val = LON(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V, N=10)
return lon_val
def LON_LONMA(self):
'''龙系长线 - LONMA'''
lon_val, lonma_val, lont_val = LON(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V, N=10)
return lonma_val
def LON_LONT(self):
'''龙系长线 - LONT'''
lon_val, lonma_val, lont_val = LON(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V, N=10)
return lont_val
def SHT_SHT(self):
'''龙系短线 - SHT'''
sht_val, shtma_val = SHT(CLOSE=self.C, VOL=self.V, N=5)
return sht_val
def SHT_SHTMA(self):
'''龙系短线 - SHTMA'''
sht_val, shtma_val = SHT(CLOSE=self.C, VOL=self.V, N=5)
return shtma_val
def ZLJC_JCS(self):
'''主力进出 - JCS'''
jcs_val, jcm_val, jcl_val = ZLJC(CLOSE=self.C, LOW=self.L, HIGH=self.H,VOL=self.V)
return jcs_val
def ZLJC_JCM(self):
'''主力进出 - JCM'''
jcs_val, jcm_val, jcl_val = ZLJC(CLOSE=self.C, LOW=self.L, HIGH=self.H,VOL=self.V)
return jcm_val
def ZLJC_JCL(self):
'''主力进出 - JCL'''
jcs_val, jcm_val, jcl_val = ZLJC(CLOSE=self.C, LOW=self.L, HIGH=self.H,VOL=self.V)
return jcl_val
def ZLMM_MMS(self):
'''主力买卖 - MMS'''
mms_val, mmm_val, mml_val = ZLMM(CLOSE=self.C)
return mms_val
def ZLMM_MMM(self):
'''主力买卖 - MMM'''
mms_val, mmm_val, mml_val = ZLMM(CLOSE=self.C)
return mmm_val
def ZLMM_MML(self):
'''主力买卖 - MML'''
mms_val, mmm_val, mml_val = ZLMM(CLOSE=self.C)
return mml_val
def SLZT_白龙(self):
'''神龙在天 - 白龙'''
白龙_val, 黄龙_val, 紫龙_val, 青龙_val, 红龙_val, 蓝龙_val = SLZT(CLOSE=self.C, LOW=self.L,HIGH=self.H)
return 白龙_val
def SLZT_黄龙(self):
'''神龙在天 - 黄龙'''
白龙_val, 黄龙_val, 紫龙_val, 青龙_val, 红龙_val, 蓝龙_val = SLZT(CLOSE=self.C, LOW=self.L,HIGH=self.H)
return 黄龙_val
def SLZT_紫龙(self):
'''神龙在天 - 紫龙'''
白龙_val, 黄龙_val, 紫龙_val, 青龙_val, 红龙_val, 蓝龙_val = SLZT(CLOSE=self.C, LOW=self.L,HIGH=self.H)
return 紫龙_val
def SLZT_青龙(self):
'''神龙在天 - 青龙'''
白龙_val, 黄龙_val, 紫龙_val, 青龙_val, 红龙_val, 蓝龙_val = SLZT(CLOSE=self.C, LOW=self.L,HIGH=self.H)
return 青龙_val
def SLZT_红龙(self):
'''神龙在天 - 红龙'''
白龙_val, 黄龙_val, 紫龙_val, 青龙_val, 红龙_val, 蓝龙_val = SLZT(CLOSE=self.C, LOW=self.L,HIGH=self.H)
return 红龙_val
def SLZT_蓝龙(self):
'''神龙在天 - 蓝龙'''
白龙_val, 黄龙_val, 紫龙_val, 青龙_val, 红龙_val, 蓝龙_val = SLZT(CLOSE=self.C, LOW=self.L,HIGH=self.H)
return 蓝龙_val
def ADVOL_ADVOL(self):
'''龙系离散量 - ADVOL'''
advol_val, ma1_val, ma2_val = ADVOL(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V)
return advol_val
def ADVOL_MA1(self):
'''龙系离散量 - MA1'''
advol_val, ma1_val, ma2_val = ADVOL(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V)
return ma1_val
def ADVOL_MA2(self):
'''龙系离散量 - MA2'''
advol_val, ma1_val, ma2_val = ADVOL(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V)
return ma2_val
# ===== 鬼系 =====
def CYS(self):
'''市场盈亏'''
return CYS(CLOSE=self.C, AMOUNT=self.AMOUNT, VOL=self.V)
def CYW(self):
'''主力控盘'''
return CYW(CLOSE=self.C, HIGH=self.H, LOW=self.L, VOL=self.V)
# ===== 其他系 =====
def JAX_J(self):
'''济安线 - J'''
j_val, a_val, x_val = JAX(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=30)
return j_val
def JAX_A(self):
'''济安线 - A'''
j_val, a_val, x_val = JAX(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=30)
return a_val
def JAX_X(self):
'''济安线 - X'''
j_val, a_val, x_val = JAX(CLOSE=self.C, HIGH=self.H, LOW=self.L, N=30)
return x_val
def XJDX_J(self):
'''超级短线 - J'''
j_val, d_val, k_val = XJDX(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return j_val
def XJDX_D(self):
'''超级短线 - D'''
j_val, d_val, k_val = XJDX(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return d_val
def XJDX_K(self):
'''超级短线 - K'''
j_val, d_val, k_val = XJDX(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return k_val
def ZJTJ_无庄控盘(self):
'''庄家抬轿 - 无庄控盘'''
无庄控盘_val, 开始控盘_val, 有庄控盘_val, 主力出货_val = ZJTJ(CLOSE=self.C)
return 无庄控盘_val
def ZJTJ_开始控盘(self):
'''庄家抬轿 - 开始控盘'''
无庄控盘_val, 开始控盘_val, 有庄控盘_val, 主力出货_val = ZJTJ(CLOSE=self.C)
return 开始控盘_val
def ZJTJ_有庄控盘(self):
'''庄家抬轿 - 有庄控盘'''
无庄控盘_val, 开始控盘_val, 有庄控盘_val, 主力出货_val = ZJTJ(CLOSE=self.C)
return 有庄控盘_val
def ZJTJ_主力出货(self):
'''庄家抬轿 - 主力出货'''
无庄控盘_val, 开始控盘_val, 有庄控盘_val, 主力出货_val = ZJTJ(CLOSE=self.C)
return 主力出货_val
def BDZX_AK(self):
'''波段之星 - AK'''
ak_val, ad1_val, aj_val, aa_val, bb_val, cc_val, 买进_val, 卖出_val = BDZX(HIGH=self.H, LOW=self.L, CLOSE=self.C)
return ak_val
def BDZX_AD1(self):
'''波段之星 - AD1'''
ak_val, ad1_val, aj_val, aa_val, bb_val, cc_val, 买进_val, 卖出_val = BDZX(HIGH=self.H, LOW=self.L, CLOSE=self.C)
return ad1_val
def BDZX_AJ(self):
'''波段之星 - AJ'''
ak_val, ad1_val, aj_val, aa_val, bb_val, cc_val, 买进_val, 卖出_val = BDZX(HIGH=self.H, LOW=self.L, CLOSE=self.C)
return aj_val
def BDZX_买进(self):
'''波段之星 - 买进信号'''
ak_val, ad1_val, aj_val, aa_val, bb_val, cc_val, 买进_val, 卖出_val = BDZX(HIGH=self.H, LOW=self.L, CLOSE=self.C)
return 买进_val
def BDZX_卖出(self):
'''波段之星 - 卖出信号'''
ak_val, ad1_val, aj_val, aa_val, bb_val, cc_val, 买进_val, 卖出_val = BDZX(HIGH=self.H, LOW=self.L, CLOSE=self.C)
return 卖出_val
def LHXJ_主力弃盘(self):
'''猎狐先觉 - 主力弃盘'''
主力弃盘_val, 主力控盘_val = LHXJ(HIGH=self.H, LOW=self.L, CLOSE=self.C)
return 主力弃盘_val
def LHXJ_主力控盘(self):
'''猎狐先觉 - 主力控盘'''
主力弃盘_val, 主力控盘_val = LHXJ(HIGH=self.H, LOW=self.L, CLOSE=self.C)
return 主力控盘_val
def LYJH_机构做空能量线(self):
'''猎鹰歼狐 - 机构做空能量线'''
机构做空能量线_val, 机构做多能量线_val, lh_val, lh1_val = LYJH(CLOSE=self.C, HIGH=self.H, LOW=self.L, M=80, M1=50)
return 机构做空能量线_val
def LYJH_机构做多能量线(self):
'''猎鹰歼狐 - 机构做多能量线'''
机构做空能量线_val, 机构做多能量线_val, lh_val, lh1_val = LYJH(CLOSE=self.C, HIGH=self.H, LOW=self.L, M=80, M1=50)
return 机构做多能量线_val
def JFZX_多头力量(self):
'''飓风智能中线 - 多头力量'''
多头力量_val, 空头力量_val, 多空平衡_val = JFZX(OPEN=self.O, CLOSE=self.C, VOL=self.V, N=30)
return 多头力量_val
def JFZX_空头力量(self):
'''飓风智能中线 - 空头力量'''
多头力量_val, 空头力量_val, 多空平衡_val = JFZX(OPEN=self.O, CLOSE=self.C, VOL=self.V, N=30)
return 空头力量_val
def CYHT_SK(self):
'''财运亨通 - SK'''
高抛_val, sk_val, sd_val, 低吸_val, 强弱分界_val, 卖出_val, 买进_val = CYHT(CLOSE=self.C, HIGH=self.H, LOW=self.L, OPEN=self.O)
return sk_val
def CYHT_SD(self):
'''财运亨通 - SD'''
高抛_val, sk_val, sd_val, 低吸_val, 强弱分界_val, 卖出_val, 买进_val = CYHT(CLOSE=self.C, HIGH=self.H, LOW=self.L, OPEN=self.O)
return sd_val
def CYHT_卖出(self):
'''财运亨通 - 卖出信号'''
高抛_val, sk_val, sd_val, 低吸_val, 强弱分界_val, 卖出_val, 买进_val = CYHT(CLOSE=self.C, HIGH=self.H, LOW=self.L, OPEN=self.O)
return 卖出_val
def CYHT_买进(self):
'''财运亨通 - 买进信号'''
高抛_val, sk_val, sd_val, 低吸_val, 强弱分界_val, 卖出_val, 买进_val = CYHT(CLOSE=self.C, HIGH=self.H, LOW=self.L, OPEN=self.O)
return 买进_val
def BSQJ_B买(self):
'''买卖区间 - B买信号'''
b买_val, 持仓_val, s卖_val, 空仓_val = BSQJ(CLOSE=self.C)
return b买_val
def BSQJ_持仓(self):
'''买卖区间 - 持仓信号'''
b买_val, 持仓_val, s卖_val, 空仓_val = BSQJ(CLOSE=self.C)
return 持仓_val
def BSQJ_S卖(self):
'''买卖区间 - S卖信号'''
b买_val, 持仓_val, s卖_val, 空仓_val = BSQJ(CLOSE=self.C)
return s卖_val
def BSQJ_空仓(self):
'''买卖区间 - 空仓信号'''
b买_val, 持仓_val, s卖_val, 空仓_val = BSQJ(CLOSE=self.C)
return 空仓_val
def CDP_STD_CDP(self):
'''逆势操作 - CDP'''
cdp_val, ah_val, nh_val, nl_val, al_val = CDP_STD(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return cdp_val
def CDP_STD_AH(self):
'''逆势操作 - AH'''
cdp_val, ah_val, nh_val, nl_val, al_val = CDP_STD(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return ah_val
def CDP_STD_NH(self):
'''逆势操作 - NH'''
cdp_val, ah_val, nh_val, nl_val, al_val = CDP_STD(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return nh_val
def CDP_STD_NL(self):
'''逆势操作 - NL'''
cdp_val, ah_val, nh_val, nl_val, al_val = CDP_STD(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return nl_val
def CDP_STD_AL(self):
'''逆势操作 - AL'''
cdp_val, ah_val, nh_val, nl_val, al_val = CDP_STD(CLOSE=self.C, HIGH=self.H, LOW=self.L)
return al_val
# ===== Alpha因子 =====
def alpha001(self, max_window=6):
"""
(-1 * CORR(RANK(DELTA(LOG(VOLUME),1)), RANK((CLOSE-OPEN)/OPEN), 6))
"""
rank_sizenl = np.log(self.V).diff(1).rank(axis=0, pct=True)
rank_ret = ((self.C - self.O) / self.O).rank(axis=0, pct=True)
return -1 * rank_sizenl.rolling(window=max_window, min_periods=max_window).corr(rank_ret)
def alpha002(self, max_window=2):
"""
-1*delta(((close-low)-(high-close))/(high-low),1)
"""
win_ratio = (max_window * self.C - self.L - self.H) / (self.H - self.L)
return -1 * win_ratio.diff(1)
def alpha003(self):
"""
-1*SUM((CLOSE=DELAY(CLOSE,1)?0:CLOSE-(CLOSE>DELAY(CLOSE,1)?MIN(LOW,DELAY(CLOSE,1)):MAX(HIGH,DELAY(CLOSE,1)))),6)
"""
alpha = self.C.copy()
condition2 = self.C.diff(periods=1) > 0.0
condition3 = self.C.diff(periods=1) < 0.0
alpha[condition2] = self.C[condition2] - np.minimum(self.C[condition2].shift(1).replace(np.NaN, 10000), self.L[condition2])
alpha[condition3] = self.C[condition3] - np.maximum(self.C[condition3].shift(1).replace(np.NaN, 0), self.H[condition3])
return -1 * alpha.sum(axis=0)
def alpha004(self, max_window=20):
"""
(((SUM(CLOSE,8)/8)+STD(CLOSE,8))<(SUM(CLOSE,2)/2))
?-1:(SUM(CLOSE,2)/2<(SUM(CLOSE,8)/8-STD(CLOSE,8))
?1:(1<=(VOLUME/MEAN(VOLUME,20))
?1:-1))
"""
ma8 = self.C.rolling(window=8, min_periods=8).mean()
std8 = self.C.rolling(window=8, min_periods=8).std()
ma2 = self.C.rolling(window=2, min_periods=2).mean()
ma20_vol = self.V.rolling(window=max_window, min_periods=max_window).mean()
result = np.where(
(ma8 + std8) < ma2,
-1,
np.where(
ma2 < (ma8 - std8),
1,
np.where(1 <= (self.V / ma20_vol), 1, -1)
)
)
return pd.Series(result, index=self.df.index, name='alpha004')
# ... 继续 alpha005 到 alpha191（保持原有代码不变）
def alpha005(self):
"""
-1*TSMAX(CORR(TSRANK(VOLUME,5),TSRANK(HIGH,5),5),3)
"""
ts_volume = self.V.rolling(window=5, min_periods=5).apply(lambda x: stats.rankdata(x)[-1] / 5.0)
ts_high = self.H.rolling(window=5, min_periods=5).apply(lambda x: stats.rankdata(x)[-1] / 5.0)
corr_ts = ts_volume.rolling(window=5, min_periods=5).corr(ts_high)
return -1 * corr_ts.rolling(window=3, min_periods=3).max()
def alpha006(self):
"""
-1*RANK(SIGN(DELTA(OPEN*0.85+HIGH*0.15,4)))
"""
weighted_price = self.O * 0.85 + self.H * 0.15
delta = weighted_price.diff(periods=4)
sign_val = np.sign(delta)
rank_val = sign_val.rank(axis=0, pct=True)
return -1 * rank_val
def alpha007(self):
"""
(RANK(MAX(VWAP-CLOSE,3))+RANK(MIN(VWAP-CLOSE,3)))*RANK(DELTA(VOLUME,3))
"""
vwap = self.AMOUNT / self.V
part1 = (vwap - self.C).rolling(window=3, min_periods=3).max().rank(axis=0, pct=True)
part2 = (vwap - self.C).rolling(window=3, min_periods=3).min().rank(axis=0, pct=True)
part3 = self.V.diff(3).rank(axis=0, pct=True)
return (part1 + part2) * part3
def alpha008(self):
"""
-1*RANK(DELTA((HIGH+LOW)/10+VWAP*0.8,4))
"""
vwap = self.AMOUNT / self.V
ma_price = (self.H + self.L) / 10 + vwap * 0.8
return -1 * ma_price.diff(4).rank(axis=0, pct=True)
def alpha009(self):
"""
SMA(((HIGH+LOW)/2-(DELAY(HIGH,1)+DELAY(LOW,1))/2)*(HIGH-LOW)/VOLUME,7,2)
"""
part1 = (self.H + self.L) * 0.5 - (self.H.shift(1) + self.L.shift(1)) * 0.5
part2 = part1 * (self.H - self.L) / self.V
return part2.ewm(adjust=False, alpha=float(2) / 7, min_periods=7).mean()
def alpha010(self):
"""
RANK(MAX(((RET<0)?STD(RET,20):CLOSE)^2,5))
"""
ret = self.C.pct_change(periods=1)
std_ret = ret.rolling(window=20, min_periods=20).std()
part1 = np.where(ret < 0, std_ret, self.C)
part1 = pd.Series(part1, index=self.df.index)
return (part1 ** 2).rolling(window=5, min_periods=5).max().rank(axis=0, pct=True)
def alpha011(self):
"""
SUM(((CLOSE-LOW)-(HIGH-CLOSE))/(HIGH-LOW)*VOLUME,6)
"""
raw = ((2 * self.C - self.L - self.H) / (self.H - self.L)) * self.V
return raw.rolling(window=6, min_periods=6).sum()
def alpha012(self):
"""
RANK(OPEN-MA(VWAP,10))*RANK(ABS(CLOSE-VWAP))*(-1)
"""
vwap = self.AMOUNT / self.V
part1 = (self.O - vwap.rolling(window=10, min_periods=10).mean()).rank(axis=0, pct=True)
part2 = abs(self.C - vwap).rank(axis=0, pct=True)
return -1 * part1 * part2
def alpha013(self):
"""
((HIGH*LOW)^0.5)-VWAP
"""
vwap = self.AMOUNT / self.V
return np.sqrt(self.H * self.L) - vwap
def alpha014(self):
"""
CLOSE-DELAY(CLOSE,5)
"""
return self.C.diff(5)
def alpha015(self):
"""
OPEN/DELAY(CLOSE,1)-1
"""
return self.O / self.C.shift(1) - 1.0
def alpha016(self):
"""
(-1*TSMAX(RANK(CORR(RANK(VOLUME),RANK(VWAP),5)),5))
"""
vwap = self.AMOUNT / self.V
rank_vol = self.V.rank(axis=0, pct=True)
rank_vwap = vwap.rank(axis=0, pct=True)
corr_vol_vwap = rank_vol.rolling(window=5, min_periods=5).corr(rank_vwap)
rank_corr = corr_vol_vwap.rank(axis=0, pct=True)
return -1 * rank_corr.rolling(window=5, min_periods=5).max()
def alpha017(self):
"""
RANK(VWAP-MAX(VWAP,15))^DELTA(CLOSE,5)
"""
vwap = self.AMOUNT / self.V
delta_price = self.C.diff(5)
base = (vwap - vwap.rolling(window=15, min_periods=15).max()).rank(axis=0, pct=True)
return base ** delta_price
def alpha018(self):
"""
CLOSE/DELAY(CLOSE,5)
"""
return self.C / self.C.shift(5)
def alpha019(self):
"""
(CLOSE<DELAY(CLOSE,5)?(CLOSE/DELAY(CLOSE,5)-1):(CLOSE=DELAY(CLOSE,5)?0:(1-DELAY(CLOSE,5)/CLOSE)))
"""
condition1 = self.C <= self.C.shift(5)
alpha = self.C.copy()
alpha[condition1] = self.C.pct_change(periods=5)[condition1]
alpha[~condition1] = -self.C.pct_change(periods=5)[~condition1]
return alpha
def alpha020(self):
"""
(CLOSE/DELAY(CLOSE,6)-1)*100
"""
return self.C.pct_change(periods=6) * 100.0
def alpha021(self):
"""
REGBETA(MEAN(CLOSE,6),SEQUENCE(6))
"""
close_ma = self.C.rolling(window=6, min_periods=6).mean()
result = pd.Series(index=self.df.index, dtype=float)
for i in range(6, len(self.df)):
y = close_ma.iloc[i-6:i]
x = np.arange(1, 7)
result.iloc[i] = self._regbeta(y, x)
return result.fillna(0)
def alpha022(self):
"""
SMEAN((CLOSE/MEAN(CLOSE,6)-1-DELAY(CLOSE/MEAN(CLOSE,6)-1,3)),12,1)
"""
ratio = self.C / self.C.rolling(window=6, min_periods=6).mean() - 1.0
alpha = ratio.diff(3)
return self._sma(alpha, 12, 1)
def alpha023(self):
"""
SMA((CLOSE>DELAY(CLOSE,1)?STD(CLOSE,20):0),20,1) /
(SMA((CLOSE>DELAY(CLOSE,1)?STD(CLOSE,20):0),20,1)+SMA((CLOSE<=DELAY(CLOSE,1)?STD(CLOSE,20):0),20,1))*100
"""
prc_std = self.C.rolling(window=20, min_periods=20).std()
condition1 = self.C > self.C.shift(1)
part1 = prc_std.copy()
part2 = prc_std.copy()
part1[~condition1] = 0.0
part2[condition1] = 0.0
sma1 = self._sma(part1, 20, 1)
sma2 = self._sma(part2, 20, 1)
return sma1 / (sma1 + sma2) * 100
def alpha024(self):
"""
SMA(CLOSE-DELAY(CLOSE,5),5,1)
"""
return self._sma(self.C.diff(5), 5, 1)
def alpha025(self):
"""
(-1*RANK(DELTA(CLOSE,7)*(1-RANK(DECAYLINEAR(VOLUME/MEAN(VOLUME,20),9)))))*(1+RANK(SUM(RET,250)))
"""
n_rows = len(self.df)
if n_rows < 50:
return pd.Series(index=self.df.index, dtype=float)
if n_rows < 260:
ret_window = min(250, n_rows - 10)
else:
ret_window = 250
w = np.arange(1, 10)
ret = self.C.pct_change().fillna(0)
part1 = self.C.diff(7).fillna(0)
vol_ma = self.V.rolling(window=20, min_periods=5).mean().fillna(method='ffill').fillna(method='bfill')
volume_ratio = (self.V / vol_ma).fillna(method='ffill').fillna(method='bfill')
decay_linear = volume_ratio.rolling(window=9, min_periods=4).apply(
lambda x: np.dot(x, w[:len(x)]) if len(x) >= 4 else np.nan
).fillna(method='ffill').fillna(method='bfill')
rank_decay = decay_linear.rank(method='min', pct=True).fillna(method='ffill').fillna(method='bfill')
part2 = 1.0 - rank_decay
sum_ret = ret.rolling(window=ret_window, min_periods=max(10, ret_window//5)).sum().fillna(method='ffill').fillna(method='bfill')
rank_sum_ret = sum_ret.rank(method='min', pct=True).fillna(method='ffill').fillna(method='bfill')
part3 = 1.0 + rank_sum_ret
part1_part2 = (part1 * part2).fillna(method='ffill').fillna(method='bfill')
rank_part1_part2 = part1_part2.rank(method='min', pct=True).fillna(method='ffill').fillna(method='bfill')
alpha = -1.0 * rank_part1_part2 * part3
return alpha.fillna(method='ffill').fillna(method='bfill')
def alpha026(self):
"""
(SUM(CLOSE,7)/7-CLOSE+CORR(VWAP,DELAY(CLOSE,5),230))
"""
n_rows = len(self.df)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
part1 = (self.C.rolling(window=7, min_periods=3).mean() - self.C).fillna(method='ffill').fillna(method='bfill')
if n_rows < 230:
corr_window = max(30, n_rows // 2)
else:
corr_window = 230
close_lag5 = self.C.shift(5)
part2 = vwap.rolling(window=corr_window, min_periods=max(10, corr_window//5)).corr(close_lag5).fillna(method='ffill').fillna(method='bfill')
alpha = (part1 + part2).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha027(self):
"""
WMA((CLOSE-DELTA(CLOSE,3))/DELAY(CLOSE,3)*100+(CLOSE-DELAY(CLOSE,6))/DELAY(CLOSE,6)*100,12)
"""
part1 = self.C.pct_change(periods=3) * 100.0 + self.C.pct_change(periods=6) * 100.0
w = np.arange(1, 13)
return part1.rolling(window=12, min_periods=12).apply(lambda x: np.dot(x, w))
def alpha028(self):
"""
3*SMA((CLOSE-TSMIN(LOW,9))/(TSMAX(HIGH,9)-TSMIN(LOW,9))*100,3,1)
-2*SMA(SMA((CLOSE-TSMIN(LOW,9))/(TSMAX(HIGH,9)-TSMIN(LOW,9))*100,3,1),3,1)
"""
part1 = self.C - self.C.rolling(window=9, min_periods=9).min()
part2 = self.H.rolling(window=9, min_periods=9).max() - self.L.rolling(window=9, min_periods=9).min()
rsv = part1 / part2 * 100
sma1 = self._sma(rsv, 3, 1)
sma2 = self._sma(sma1, 3, 1)
return 3 * sma1 - 2 * sma2
def alpha029(self):
"""
(CLOSE-DELAY(CLOSE,6))/DELAY(CLOSE,6)*VOLUME
"""
return self.C.pct_change(periods=6) * self.V
def alpha030(self):
"""
WMA((REGRESI(RET,MKT,SMB,HML,60))^2,20)
单只股票版本：使用市场指数作为基准
"""
ret = self.C.pct_change().fillna(0.0)
if 'index_close' in self.df.columns:
mkt_ret = self.df['index_close'].pct_change().fillna(0.0)
else:
mkt_ret = ret.rolling(window=20, min_periods=20).mean().fillna(0.0)
smb_ret = pd.Series(0, index=ret.index)
hml_ret = pd.Series(0, index=ret.index)
result = pd.Series(index=self.df.index, dtype=float)
for i in range(60, len(self.df)):
y = ret.iloc[i-60:i]
X = pd.DataFrame({
'const': 1,
'mkt': mkt_ret.iloc[i-60:i],
'smb': smb_ret.iloc[i-60:i],
'hml': hml_ret.iloc[i-60:i]
}).dropna()
y = y.loc[X.index]
if len(y) >= 20:
try:
result.iloc[i] = sm.OLS(y, X).fit().resid.iloc[-1]
except:
result.iloc[i] = np.nan
else:
result.iloc[i] = np.nan
w = np.arange(1, 21) / np.arange(1, 21).sum()
return (result ** 2).rolling(window=20, min_periods=20).apply(lambda x: np.dot(x, w))
def alpha031(self):
"""
(CLOSE-MEAN(CLOSE,12))/MEAN(CLOSE,12)*100
"""
ma = self.C.rolling(window=12, min_periods=12).mean()
return (self.C / ma - 1.0) * 100
def alpha032(self):
"""
(-1*SUM(RANK(CORR(RANK(HIGH),RANK(VOLUME),3)),3))
"""
part1 = self.H.rank(pct=True).rolling(window=3, min_periods=3).corr(self.V.rank(pct=True))
return -1 * part1.rank(pct=True).rolling(window=3, min_periods=3).sum()
def alpha033(self):
"""
(-1*TSMIN(LOW,5)+DELAY(TSMIN(LOW,5),5))*RANK((SUM(RET,240)-SUM(RET,20))/220)*TSRANK(VOLUME,5)
"""
n_rows = len(self.df)
if n_rows < 10:
return pd.Series(0, index=self.df.index)
self.V = self.V.replace(0, np.nan).fillna(method='ffill').fillna(method='bfill')
low_min5 = self.L.rolling(window=5, min_periods=3).min().fillna(method='ffill').fillna(method='bfill')
part1 = -1 * low_min5.diff(5).fillna(0)
if n_rows < 240:
sum_window1 = min(240, n_rows - 5)
sum_window2 = min(20, n_rows // 3)
else:
sum_window1 = 240
sum_window2 = 20
ret = self.C.pct_change().fillna(0)
sum_ret1 = ret.rolling(window=sum_window1, min_periods=max(5, sum_window1//10)).sum().fillna(method='ffill').fillna(method='bfill')
sum_ret2 = ret.rolling(window=sum_window2, min_periods=max(3, sum_window2//5)).sum().fillna(method='ffill').fillna(method='bfill')
part2_series = ((sum_ret1 - sum_ret2) / 220).fillna(method='ffill').fillna(method='bfill')
part2 = part2_series.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
part3 = self._tsrank_fixed(self.V, 5).fillna(method='ffill').fillna(method='bfill')
alpha = (part1 * part2 * part3).fillna(0)
return alpha
def alpha034(self):
"""
MEAN(CLOSE,12)/CLOSE
"""
return self.C.rolling(window=12, min_periods=12).mean() / self.C
def alpha035(self):
"""
(MIN(RANK(DECAYLINEAR(DELTA(OPEN,1),15)),RANK(DECAYLINEAR(CORR(VOLUME,OPEN*0.65+CLOSE*0.35,17),7)))*-1)
"""
w7 = np.arange(1, 8)
w15 = np.arange(1, 16)
part1 = self.O.diff().rolling(window=15, min_periods=15).apply(lambda x: np.dot(x, w15)).rank(pct=True)
part2 = (self.O * 0.65 + self.C * 0.35).rolling(window=17, min_periods=17).corr(self.V)
part2 = part2.rolling(window=7, min_periods=7).apply(lambda x: np.dot(x, w7)).rank(pct=True)
return np.minimum(part1, part2) * (-1)
def alpha036(self):
"""
RANK(SUM(CORR(RANK(VOLUME),RANK(VWAP),6),2))
"""
self.V = self.V.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill')
vol_rank = self.V.rank(pct=True, method='min')
vwap_rank = vwap.rank(pct=True, method='min')
part1 = vol_rank.rolling(window=6, min_periods=3).corr(vwap_rank)
return part1.rolling(window=2, min_periods=1).sum().rank(pct=True, method='min')
def alpha037(self):
"""
(-1*RANK(SUM(OPEN,5)*SUM(RET,5)-DELAY(SUM(OPEN,5)*SUM(RET,5),10)))
"""
part1 = self.O.rolling(window=5, min_periods=5).sum() * self.C.pct_change().rolling(window=5, min_periods=5).sum()
return -1 * part1.diff(10)
def alpha038(self):
"""
((SUM(HIGH,20)/20)<HIGH)?(-1*DELTA(HIGH,2)):0
"""
condition = self.H.rolling(window=20, min_periods=20).mean() < self.H
alpha = -1 * self.H.diff(2)
alpha[~condition] = 0.0
return alpha
def alpha039(self):
"""
(RANK(DECAYLINEAR(DELTA(CLOSE,2),8))-RANK(DECAYLINEAR(CORR(VWAP*0.3+OPEN*0.7,SUM(MEAN(VOLUME,180),37),14),12)))*-1
使用填充版本
"""
n_rows = len(self.df)
if n_rows < 200:
return self._alpha039_small_data()
w8 = np.arange(1, 9)
w12 = np.arange(1, 13)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
parta = vwap * 0.3 + self.O * 0.7
V_filled = self.V.fillna(method='ffill').fillna(method='bfill')
vol_window = min(180, n_rows // 2)
vol_min_periods = min(vol_window, max(10, vol_window // 10))
vol_ma = V_filled.rolling(window=vol_window, min_periods=vol_min_periods).mean().fillna(method='ffill').fillna(method='bfill')
sum_window = min(37, n_rows // 4)
sum_min_periods = min(sum_window, max(5, sum_window // 4))
partb = vol_ma.rolling(window=sum_window, min_periods=sum_min_periods).sum().fillna(method='ffill').fillna(method='bfill')
part1 = self.C.diff(2).fillna(0)
decay_window1 = 8
decay_min_periods1 = min(decay_window1, max(3, decay_window1 // 2))
part1_decay = part1.rolling(window=decay_window1, min_periods=decay_min_periods1).apply(
lambda x: np.dot(x, w8[:len(x)]) if len(x) >= decay_min_periods1 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
corr_window = min(14, n_rows // 8)
corr_min_periods = min(corr_window, max(3, corr_window // 2))
part2_corr = parta.rolling(window=corr_window, min_periods=corr_min_periods).corr(partb).fillna(0)
decay_window2 = min(12, n_rows // 8)
decay_min_periods2 = min(decay_window2, max(3, decay_window2 // 2))
part2_decay = part2_corr.rolling(window=decay_window2, min_periods=decay_min_periods2).apply(
lambda x: np.dot(x, w12[:len(x)]) if len(x) >= decay_min_periods2 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min')
return -1 * (part1 - part2).fillna(method='ffill').fillna(method='bfill')
def _alpha039_small_data(self):
"""
小数据量版本
"""
n_rows = len(self.df)
w8 = np.arange(1, 9)
w12 = np.arange(1, 13)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
parta = vwap * 0.3 + self.O * 0.7
V_filled = self.V.fillna(method='ffill').fillna(method='bfill')
vol_window = min(30, n_rows // 2)
vol_min_periods = min(vol_window, max(3, vol_window // 3))
vol_ma = V_filled.rolling(window=vol_window, min_periods=vol_min_periods).mean().fillna(method='ffill').fillna(method='bfill')
sum_window = min(10, n_rows // 3)
sum_min_periods = min(sum_window, max(3, sum_window // 2))
partb = vol_ma.rolling(window=sum_window, min_periods=sum_min_periods).sum().fillna(method='ffill').fillna(method='bfill')
part1 = self.C.diff(2).fillna(0)
decay_window1 = min(8, n_rows // 3)
decay_min_periods1 = min(decay_window1, max(2, decay_window1 // 2))
part1_decay = part1.rolling(window=decay_window1, min_periods=decay_min_periods1).apply(
lambda x: np.dot(x, w8[:len(x)]) if len(x) >= decay_min_periods1 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
corr_window = min(8, n_rows // 4)
corr_min_periods = min(corr_window, max(2, corr_window // 2))
part2_corr = parta.rolling(window=corr_window, min_periods=corr_min_periods).corr(partb).fillna(0)
decay_window2 = min(8, n_rows // 4)
decay_min_periods2 = min(decay_window2, max(2, decay_window2 // 2))
part2_decay = part2_corr.rolling(window=decay_window2, min_periods=decay_min_periods2).apply(
lambda x: np.dot(x, w12[:len(x)]) if len(x) >= decay_min_periods2 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min')
return -1 * (part1 - part2).fillna(method='ffill').fillna(method='bfill')
def alpha040(self):
"""
SUM(CLOSE>DELAY(CLOSE,1)?VOLUME:0,26)/SUM(CLOSE<=DELAY(CLOSE,1)?VOLUME:0,26)*100
"""
diff = self.C.diff()
part1 = ((diff > 0) * self.V).rolling(window=26, min_periods=26).sum()
part2 = ((diff <= 0) * self.V).rolling(window=26, min_periods=26).sum()
return part1 / part2 * 100
def alpha041(self):
"""
RANK(MAX(DELTA(VWAP,3),5))*-1
"""
self.V = self.V.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
vwap_diff = vwap.diff(3)
vwap_max = vwap_diff.rolling(window=5, min_periods=3).max()
return -1 * vwap_max.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
def alpha042(self):
"""
(-1*RANK(STD(HIGH,10)))*CORR(HIGH,VOLUME,10)
"""
part1 = -1 * self.H.rolling(window=10, min_periods=10).std().rank(pct=True)
part2 = self.H.rolling(window=10, min_periods=10).corr(self.V)
return part1 * part2
def alpha043(self):
"""
(SUM(CLOSE>DELAY(CLOSE,1)?VOLUME:(CLOSE<DELAY(CLOSE,1)?-VOLUME:0),6))
"""
diff = self.C.diff()
part1 = ((diff > 0) * self.V).rolling(window=6, min_periods=6).sum()
part2 = ((diff < 0) * -self.V).rolling(window=6, min_periods=6).sum()
return part1 + part2
def alpha044(self):
"""
(TSRANK(DECAYLINEAR(CORR(LOW,MEAN(VOLUME,10),7),6),4)+TSRANK(DECAYLINEAR(DELTA(VWAP,3),10),15))
"""
w6 = np.arange(1, 7)
w10 = np.arange(1, 11)
self.V = self.V.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
vol_ma = self.V.rolling(window=10, min_periods=5).mean().fillna(method='ffill').fillna(method='bfill')
part1_corr = vol_ma.rolling(window=7, min_periods=4).corr(self.L).fillna(method='ffill').fillna(method='bfill')
part1_decay = part1_corr.rolling(window=6, min_periods=3).apply(
lambda x: np.dot(x, w6[:len(x)]) if len(x) >= 3 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = self._tsrank_fixed(part1_decay, 4)
vwap_diff = vwap.diff(3).fillna(0)
part2_decay = vwap_diff.rolling(window=10, min_periods=5).apply(
lambda x: np.dot(x, w10[:len(x)]) if len(x) >= 5 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = self._tsrank_fixed(part2_decay, 15)
return part1 + part2
def alpha045(self):
"""
(RANK(DELTA(CLOSE*0.6+OPEN*0.4,1))*RANK(CORR(VWAP,MEAN(VOLUME,150),15)))
调整版本：根据数据量动态调整窗口
"""
n_rows = len(self.df)
vol_window = max(20, n_rows // 3) if n_rows < 150 else 150
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
weighted_price = self.C * 0.6 + self.O * 0.4
part1 = weighted_price.diff().fillna(0).rank(pct=True, method='min')
vol_ma = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//10)).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = vwap.rolling(window=15, min_periods=5).corr(vol_ma).fillna(method='ffill').fillna(method='bfill')
part2 = part2_corr.rank(pct=True, method='min')
return (part1 * part2).fillna(method='ffill').fillna(method='bfill')
def alpha046(self):
"""
(MEAN(CLOSE,3)+MEAN(CLOSE,6)+MEAN(CLOSE,12)+MEAN(CLOSE,24))/(4*CLOSE)
"""
ma3 = self.C.rolling(window=3, min_periods=3).mean()
ma6 = self.C.rolling(window=6, min_periods=6).mean()
ma12 = self.C.rolling(window=12, min_periods=12).mean()
ma24 = self.C.rolling(window=24, min_periods=24).mean()
return (ma3 + ma6 + ma12 + ma24) / (4 * self.C)
def alpha047(self):
"""
SMA((TSMAX(HIGH,6)-CLOSE)/(TSMAX(HIGH,6)-TSMIN(LOW,6))*100,9,1)
"""
high_max = self.H.rolling(window=6, min_periods=6).max()
low_min = self.L.rolling(window=6, min_periods=6).min()
part1 = (high_max - self.C) / (high_max - low_min) * 100
return self._sma(part1, 9, 1)
def alpha048(self):
"""
-1*RANK(SIGN(CLOSE-DELAY(CLOSE,1))+SIGN(DELAY(CLOSE,1)-DELAY(CLOSE,2))+SIGN(DELAY(CLOSE,2)-DELAY(CLOSE,3)))*SUM(VOLUME,5)/SUM(VOLUME,20)
"""
diff1 = self.C.diff()
part1 = (np.sign(diff1) + np.sign(diff1.shift(1)) + np.sign(diff1.shift(2))).rank(pct=True)
part2 = self.V.rolling(window=5, min_periods=5).sum() / self.V.rolling(window=20, min_periods=20).sum()
return -1 * part1 * part2
def alpha049(self):
"""
SUM(HIGH+LOW>=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12)/
(SUM(HIGH+LOW>=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12)+
SUM(HIGH+LOW<=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12))
"""
hl_sum = self.H + self.L
condition1 = hl_sum >= hl_sum.shift(1)
condition2 = hl_sum <= hl_sum.shift(1)
max_abs = np.maximum(abs(self.H.diff()), abs(self.L.diff()))
part1 = max_abs.copy()
part2 = max_abs.copy()
part1[condition1] = 0.0
part2[condition2] = 0.0
sum1 = part1.rolling(window=12, min_periods=12).sum()
sum2 = part2.rolling(window=12, min_periods=12).sum()
return sum1 / (sum1 + sum2)
def alpha050(self):
"""
SUM(HIGH+LOW<=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12)/
(SUM(HIGH+LOW<=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12)
+SUM(HIGH+LOW>=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12))
-SUM(HIGH+LOW>=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12)/
(SUM(HIGH+LOW>=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12)
+SUM(HIGH+LOW<=DELAY(HIGH,1)+DELAY(LOW,1)?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1))),12))
"""
hl_sum = self.H + self.L
condition1 = hl_sum >= hl_sum.shift(1)
condition2 = hl_sum <= hl_sum.shift(1)
max_abs = np.maximum(abs(self.H.diff()), abs(self.L.diff()))
part1 = max_abs.copy()
part2 = max_abs.copy()
part1[condition2] = 0.0
part2[condition1] = 0.0
sum1 = part1.rolling(window=12, min_periods=12).sum()
sum2 = part2.rolling(window=12, min_periods=12).sum()
return sum1 / (sum1 + sum2) - sum2 / (sum1 + sum2)
def alpha051(self):
"""
SUM(((HIGH+LOW)<=(DELAY(HIGH,1)+DELAY(LOW,1))?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1)))),12)/
(SUM(((HIGH+LOW)<=(DELAY(HIGH,1)+DELAY(LOW,1))?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1)))),12)
+SUM(((HIGH+LOW)>=(DELAY(HIGH,1)+DELAY(LOW,1))?0:MAX(ABS(HIGH-DELAY(HIGH,1)),ABS(LOW-DELAY(LOW,1)))),12))
"""
hl_sum = self.H + self.L
condition1 = hl_sum <= hl_sum.shift(1)
condition2 = hl_sum >= hl_sum.shift(1)
max_abs = np.maximum(abs(self.H.diff()), abs(self.L.diff()))
part1 = max_abs.copy()
part2 = max_abs.copy()
part1[condition1] = 0.0
part2[condition2] = 0.0
sum1 = part1.rolling(window=12, min_periods=12).sum()
sum2 = part2.rolling(window=12, min_periods=12).sum()
return sum1 / (sum1 + sum2)
def alpha052(self):
"""
SUM(MAX(0,HIGH-DELAY((HIGH+LOW+CLOSE)/3,1)),26)/SUM(MAX(0,DELAY((HIGH+LOW+CLOSE)/3,1)-L),26)*100
"""
ma = (self.H + self.L + self.C) / 3.0
part1 = np.maximum(0.0, self.H - ma.shift(1)).rolling(window=26, min_periods=26).sum()
part2 = np.maximum(0.0, ma.shift(1) - self.L).rolling(window=26, min_periods=26).sum()
return part1 / part2 * 100.0
def alpha053(self):
"""
COUNT(CLOSE>DELAY(CLOSE,1),12)/12*100
"""
return (self.C.diff() > 0.0).rolling(window=12, min_periods=12).sum() / 12.0 * 100
def alpha054(self):
"""
(-1*RANK(STD(ABS(CLOSE-OPEN))+CLOSE-OPEN+CORR(CLOSE,OPEN,10)))
"""
part1 = abs(self.C - self.O).rolling(window=10, min_periods=10).std() + self.C - self.O + self.C.rolling(window=10, min_periods=10).corr(self.O)
return -1 * part1.rank(pct=True)
def alpha055(self):
"""
SUM(16*(CLOSE+(CLOSE-OPEN)/2-DELAY(OPEN,1))/
((ABS(HIGH-DELAY(CLOSE,1))>ABS(LOW-DELAY(CLOSE,1)) & ABS(HIGH-DELAY(CLOSE,1))>ABS(HIGH-DELAY(LOW,1)) ?
ABS(HIGH-DELAY(CLOSE,1))+ABS(LOW-DELAY(CLOSE,1))/2+ABS(DELAY(CLOSE,1)-DELAY(OPEN,1))/4:
(ABS(LOW-DELAY(CLOSE,1))>ABS(HIGH-DELAY(LOW,1)) & ABS(LOW-DELAY(CLOSE,1))>ABS(HIGH-DELAY(CLOSE,1)) ?
ABS(LOW-DELAY(CLOSE,1))+ABS(HIGH-DELAY(CLOSE,1))/2+ABS(DELAY(CLOSE,1)-DELAY(OPEN,1))/4:
ABS(HIGH-DELAY(LOW,1))+ABS(DELAY(CLOSE,1)-DELAY(OPEN,1))/4)))
*MAX(ABS(HIGH-DELAY(CLOSE,1)),ABS(LOW-DELAY(CLOSE,1))),20)
"""
part1 = self.C * 1.5 - self.O * 0.5 - self.O.shift(1)
part2 = abs(self.H - self.C.shift(1)) + abs(self.L - self.C.shift(1)) / 2.0 + abs(self.C - self.O).shift(1) / 4.0
condition1 = np.logical_and(
abs(self.H - self.C.shift(1)) > abs(self.L - self.C.shift(1)),
abs(self.H - self.C.shift(1)) > abs(self.H - self.L.shift(1))
)
condition2 = np.logical_and(
abs(self.L - self.C.shift(1)) > abs(self.H - self.L.shift(1)),
abs(self.L - self.C.shift(1)) > abs(self.H - self.C.shift(1))
)
part2[~condition1 & condition2] = abs(self.L - self.C.shift(1)) + abs(self.H - self.C.shift(1)) / 2.0 + abs(self.C - self.O).shift(1) / 4.0
part2[~condition1 & ~condition2] = abs(self.H - self.L.shift(1)) + abs(self.C - self.O).shift(1) / 4.0
part3 = np.maximum(abs(self.H - self.C.shift(1)), abs(self.L - self.C.shift(1)))
alpha = (part1 / part2 * part3 * 16.0).rolling(window=20, min_periods=20).sum()
return alpha
def alpha056(self):
"""
RANK(OPEN-TSMIN(OPEN,12))<RANK(RANK(CORR(SUM((HIGH +LOW)/2,19),SUM(MEAN(VOLUME,40),19),13))^5)
"""
part1 = (self.O - self.O.rolling(window=12, min_periods=12).min()).rank(pct=True)
t1 = (self.H * 0.5 + self.L * 0.5).rolling(window=19, min_periods=19).sum()
t2 = self.V.rolling(window=40, min_periods=40).mean().rolling(window=19, min_periods=19).sum()
part2 = (t1.rolling(window=13, min_periods=13).corr(t2).rank(pct=True) ** 5).rank(pct=True)
return part2 - part1
def alpha057(self):
"""
SMA((CLOSE-TSMIN(LOW,9))/(TSMAX(HIGH,9)-TSMIN(LOW,9))*100,3,1)
"""
part1 = self.C - self.C.rolling(window=9, min_periods=9).min()
part2 = self.H.rolling(window=9, min_periods=9).max() - self.L.rolling(window=9, min_periods=9).min()
rsv = part1 / part2 * 100
return self._sma(rsv, 3, 1)
def alpha058(self):
"""
COUNT(CLOSE>DELAY(CLOSE,1),20)/20*100
"""
return (self.C.diff() > 0.0).rolling(window=20, min_periods=20).sum() / 20.0 * 100
def alpha059(self):
"""
SUM((CLOSE=DELAY(CLOSE,1)?0:CLOSE-(CLOSE>DELAY(CLOSE,1)?MIN(LOW,DELAY(CLOSE,1)):MAX(HIGH,DELAY(CLOSE,1)))),20)
"""
alpha = self.C.copy()
diff = self.C.diff()
condition1 = diff > 0.0
condition2 = diff < 0.0
alpha[condition1] = self.C[condition1] - np.minimum(self.L[condition1], self.C.shift(1)[condition1])
alpha[condition2] = self.C[condition2] - np.maximum(self.H[condition2], self.C.shift(1)[condition2])
alpha[diff == 0] = 0.0
return alpha.rolling(window=20, min_periods=20).sum()
def alpha060(self):
"""
SUM((2*CLOSE-LOW-HIGH)/(HIGH-LOW)*VOLUME,20)
"""
price_range = (self.H - self.L).replace(0, 1e-10)
numerator = 2 * self.C - self.L - self.H
ratio = numerator / price_range
part1 = (ratio * self.V).fillna(method='ffill').fillna(method='bfill')
alpha = part1.rolling(window=20, min_periods=10).sum().fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha061(self):
"""
MAX(RANK(DECAYLINEAR(DELTA(VWAP,1),12)),RANK(DECAYLINEAR(RANK(CORR(LOW,MEAN(VOLUME,80),8)),17)))*-1
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
w12 = np.arange(1, 13)
vwap_diff = vwap.diff().fillna(0)
part1_decay = vwap_diff.rolling(window=12, min_periods=6).apply(
lambda x: np.dot(x, w12[:len(x)]) if len(x) >= 6 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
vol_window = min(80, len(self.df) // 2)
turnover_ma = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//10)).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = turnover_ma.rolling(window=8, min_periods=4).corr(self.L).fillna(method='ffill').fillna(method='bfill')
part2_rank = part2_corr.rank(pct=True, method='min')
w17 = np.arange(1, 18)
part2_decay = part2_rank.rolling(window=17, min_periods=8).apply(
lambda x: np.dot(x, w17[:len(x)]) if len(x) >= 8 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min')
return -1 * np.maximum(part1, part2).fillna(method='ffill').fillna(method='bfill')
def alpha062(self):
"""
-1*CORR(HIGH,RANK(VOLUME),5)
"""
return -1 * self.V.rank(pct=True).rolling(window=5, min_periods=5).corr(self.H)
def alpha063(self):
"""
SMA(MAX(CLOSE-DELAY(CLOSE,1),0),6,1)/SMA(ABS(CLOSE-DELAY(CLOSE,1)),6,1)*100
"""
diff = self.C.diff()
part1 = np.maximum(diff, 0.0)
part2 = abs(diff)
sma1 = self._sma(part1, 6, 1)
sma2 = self._sma(part2, 6, 1)
return sma1 / sma2 * 100.0
def alpha064(self):
"""
(MAX(RANK(DECAYLINEAR(CORR(RANK(VWAP),RANK(VOLUME),4),4)),RANK(DECAYLINEAR(MAX(CORR(RANK(CLOSE),RANK(MEAN(VOLUME,60)),4),13),14)))*-1)
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w4 = np.arange(1, 5)
w14 = np.arange(1, 15)
vwap_rank = vwap.rank(pct=True, method='min')
vol_rank = self.V.rank(pct=True, method='min')
part1_corr = vwap_rank.rolling(window=4, min_periods=3).corr(vol_rank).fillna(method='ffill').fillna(method='bfill')
part1_decay = part1_corr.rolling(window=4, min_periods=3).apply(
lambda x: np.dot(x, w4[:len(x)]) if len(x) >= 3 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
vol_window = min(60, len(self.df) // 2)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//10)).mean().fillna(method='ffill').fillna(method='bfill')
vol_ma_rank = vol_ma.rank(pct=True, method='min')
close_rank = self.C.rank(pct=True, method='min')
part2_corr = close_rank.rolling(window=4, min_periods=3).corr(vol_ma_rank).fillna(method='ffill').fillna(method='bfill')
part2_max = part2_corr.rolling(window=13, min_periods=7).max().fillna(method='ffill').fillna(method='bfill')
part2_decay = part2_max.rolling(window=14, min_periods=7).apply(
lambda x: np.dot(x, w14[:len(x)]) if len(x) >= 7 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min')
return -1 * np.maximum(part1, part2).fillna(method='ffill').fillna(method='bfill')
def alpha065(self):
"""
MEAN(CLOSE,6)/CLOSE
"""
return self.C.rolling(window=6, min_periods=6).mean() / self.C
def alpha066(self):
"""
(CLOSE-MEAN(CLOSE,6))/MEAN(CLOSE,6)*100
"""
ma = self.C.rolling(window=6, min_periods=6).mean()
return (self.C - ma) / ma * 100
def alpha067(self):
"""
SMA(MAX(CLOSE-DELAY(CLOSE,1),0),24,1)/SMA(ABS(CLOSE-DELAY(CLOSE,1)),24,1)*100
"""
diff = self.C.diff()
part1 = np.maximum(diff, 0.0)
part2 = abs(diff)
sma1 = self._sma(part1, 24, 1)
sma2 = self._sma(part2, 24, 1)
return sma1 / sma2 * 100
def alpha068(self):
"""
SMA(((HIGH+LOW)/2-(DELAY(HIGH,1)+DELAY(LOW,1))/2)*(HIGH-LOW)/VOLUME,15,2)
"""
part1 = (self.H.diff() * 0.5 + self.L.diff() * 0.5) * (self.H - self.L) / self.V
return self._sma(part1, 15, 2)
def alpha069(self):
"""
(SUM(DTM,20)>SUM(DBM,20)?(SUM(DTM,20)-SUM(DBM,20))/SUM(DTM,20):
(SUM(DTM,20)=SUM(DBM,20)?0:(SUM(DTM,20)-SUM(DBM,20))/SUM(DBM,20)))
"""
dtm = (self.O.diff() <= 0) * np.maximum(self.H - self.O, self.O.diff())
dbm = (self.O.diff() >= 0) * np.maximum(self.O - self.L, self.O.diff())
dtm_sum = dtm.rolling(window=20, min_periods=20).sum()
dbm_sum = dbm.rolling(window=20, min_periods=20).sum()
result = pd.Series(index=self.df.index, dtype=float)
mask_gt = dtm_sum > dbm_sum
mask_eq = dtm_sum == dbm_sum
mask_lt = dtm_sum < dbm_sum
result[mask_gt] = (dtm_sum[mask_gt] - dbm_sum[mask_gt]) / dtm_sum[mask_gt]
result[mask_eq] = 0.0
result[mask_lt] = (dtm_sum[mask_lt] - dbm_sum[mask_lt]) / dbm_sum[mask_lt]
return result
def alpha070(self):
"""
STD(AMOUNT,6)
"""
return self.AMOUNT.rolling(window=6, min_periods=6).std()
def alpha071(self):
"""
(CLOSE-MEAN(CLOSE,24))/MEAN(CLOSE,24)*100
"""
ma = self.C.rolling(window=24, min_periods=24).mean()
return (self.C - ma) / ma * 100
def alpha072(self):
"""
SMA((TSMAX(HIGH,6)-CLOSE)/(TSMAX(HIGH,6)-TSMIN(LOW,6))*100,15,1)
"""
high_max = self.H.rolling(window=6, min_periods=6).max()
low_min = self.L.rolling(window=6, min_periods=6).min()
part1 = (high_max - self.C) / (high_max - low_min) * 100.0
return self._sma(part1, 15, 1)
def alpha073(self):
"""
((TSRANK(DECAYLINEAR(DECAYLINEAR(CORR(CLOSE,VOLUME,10),16),4),5)-RANK(DECAYLINEAR(CORR(VWAP,MEAN(VOLUME,30),4),3)))*-1)
ETF专用版本
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
def normalize(series):
return (series - series.min()) / (series.max() - series.min() + 1e-10)
part1_corr = self.C.rolling(window=10, min_periods=5).corr(self.V).fillna(method='ffill').fillna(method='bfill')
part1_ema1 = part1_corr.ewm(span=16, adjust=False, min_periods=5).mean()
part1_ema2 = part1_ema1.ewm(span=4, adjust=False, min_periods=3).mean()
part1 = normalize(part1_ema2)
vol_window = min(30, len(self.df) // 2)
vol_ma = self.V.rolling(window=vol_window, min_periods=5).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = vwap.rolling(window=4, min_periods=3).corr(vol_ma).fillna(method='ffill').fillna(method='bfill')
w3 = np.arange(1, 4) / np.arange(1, 4).sum()
part2 = part2_corr.rolling(window=3, min_periods=2).apply(
lambda x: np.sum(x * w3[:len(x)]) if len(x) >= 2 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = normalize(part2)
return -1 * (part1 - part2).fillna(method='ffill').fillna(method='bfill')
def alpha074(self):
"""
RANK(CORR(SUM(LOW*0.35+VWAP*0.65,20),SUM(MEAN(VOLUME,40),20),7))+RANK(CORR(RANK(VWAP),RANK(VOLUME),6))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
weighted_price = self.L * 0.35 + vwap * 0.65
sum1 = weighted_price.rolling(window=20, min_periods=10).sum().fillna(method='ffill').fillna(method='bfill')
vol_window = min(40, len(self.df) // 2)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//8)).mean().fillna(method='ffill').fillna(method='bfill')
sum2 = vol_ma.rolling(window=20, min_periods=10).sum().fillna(method='ffill').fillna(method='bfill')
part1_corr = sum1.rolling(window=7, min_periods=4).corr(sum2).fillna(method='ffill').fillna(method='bfill')
part1 = part1_corr.rank(pct=True, method='min')
vwap_rank = vwap.rank(pct=True, method='min')
vol_rank = self.V.rank(pct=True, method='min')
part2_corr = vwap_rank.rolling(window=6, min_periods=4).corr(vol_rank).fillna(method='ffill').fillna(method='bfill')
part2 = part2_corr.rank(pct=True, method='min')
return (part1 + part2).fillna(method='ffill').fillna(method='bfill')
def alpha075(self):
"""
COUNT(CLOSE>OPEN & BANCHMARK_INDEX_CLOSE<BANCHMARK_INDEX_OPEN,50)/COUNT(BANCHMARK_INDEX_CLOSE<BANCHMARK_INDEX_OPEN,50)
使用指数数据
"""
n_rows = len(self.df)
if hasattr(self, 'index_df') and self.index_df is not None:
index_data = self.index_df.copy()
if 'close' in index_data.columns:
index_close = index_data['close']
else:
index_close = index_data.iloc[:, 0]
if 'open' in index_data.columns:
index_open = index_data['open']
else:
index_open = index_close.shift(1).fillna(index_close)
if 'date' in self.df.columns and 'date' in index_data.columns:
self.df['date'] = pd.to_datetime(self.df['date'])
index_data['date'] = pd.to_datetime(index_data['date'])
close_map = dict(zip(index_data['date'], index_close))
open_map = dict(zip(index_data['date'], index_open))
bm_close = self.df['date'].map(close_map).fillna(method='ffill').fillna(method='bfill')
bm_open = self.df['date'].map(open_map).fillna(method='ffill').fillna(method='bfill')
else:
bm_close = index_close.reindex(self.df.index, method='ffill')
bm_open = index_open.reindex(self.df.index, method='ffill')
else:
bm_close = self.C.rolling(window=20, min_periods=5).mean().fillna(method='ffill').fillna(method='bfill')
bm_open = self.O.rolling(window=20, min_periods=5).mean().fillna(method='ffill').fillna(method='bfill')
bm_down = bm_close < bm_open
stock_up = self.C > self.O
condition = stock_up & bm_down
if condition.sum() == 0:
bm_ret = bm_close.pct_change().fillna(0)
bm_down_ret = bm_ret < 0
condition = stock_up & bm_down_ret
bm_down = bm_down_ret
window = min(50, n_rows // 2)
min_periods = max(5, window // 3)
numerator = condition.rolling(window=window, min_periods=min_periods).sum()
denominator = bm_down.rolling(window=window, min_periods=min_periods).sum().replace(0, np.nan)
alpha = (numerator / denominator).fillna(method='ffill').fillna(method='bfill').fillna(0)
return alpha
def alpha076(self):
"""
STD(ABS(CLOSE/DELAY(CLOSE,1)-1)/VOLUME,20)/MEAN(ABS(CLOSE/DELAY(CLOSE,1)-1)/VOLUME,20)
"""
self.V = self.V.replace(0, np.nan).fillna(method='ffill').fillna(method='bfill')
ret = self.C.pct_change().fillna(0).replace([np.inf, -np.inf], 0)
ret_vol = (abs(ret) / self.V).fillna(method='ffill').fillna(method='bfill')
std = ret_vol.rolling(window=20, min_periods=10).std()
mean = ret_vol.rolling(window=20, min_periods=10).mean().replace(0, np.nan)
alpha = (std / mean).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha077(self):
"""
MIN(RANK(DECAYLINEAR(HIGH*0.5+LOW*0.5-VWAP,20)),RANK(DECAYLINEAR(CORR(HIGH*0.5+LOW*0.5,MEAN(VOLUME,40),3),6)))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w6 = np.arange(1, 7)
w20 = np.arange(1, 21)
hl_avg = self.H * 0.5 + self.L * 0.5
part1_series = hl_avg - vwap
part1_decay = part1_series.rolling(window=20, min_periods=10).apply(
lambda x: np.dot(x, w20[:len(x)]) if len(x) >= 10 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
vol_window = min(40, len(self.df) // 2)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//8)).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = hl_avg.rolling(window=3, min_periods=2).corr(vol_ma).fillna(method='ffill').fillna(method='bfill')
part2_decay = part2_corr.rolling(window=6, min_periods=3).apply(
lambda x: np.dot(x, w6[:len(x)]) if len(x) >= 3 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min')
return np.minimum(part1, part2).fillna(method='ffill').fillna(method='bfill')
def alpha078(self):
"""
((HIGH+LOW+CLOSE)/3-MA((HIGH+LOW+CLOSE)/3,12))/(0.015*MEAN(ABS(CLOSE-MEAN((HIGH+LOW+CLOSE)/3,12)),12))
"""
tp = (self.H + self.L + self.C) / 3
tp_ma = tp.rolling(window=12, min_periods=12).mean()
part1 = tp - tp_ma
part2 = abs(self.C - tp_ma).rolling(window=12, min_periods=12).mean() * 0.015
return part1 / part2
def alpha079(self):
"""
SMA(MAX(CLOSE-DELAY(CLOSE,1),0),12,1)/SMA(ABS(CLOSE-DELAY(CLOSE,1)),12,1)*100
"""
diff = self.C.diff()
part1 = np.maximum(diff, 0.0)
part2 = abs(diff)
sma1 = self._sma(part1, 12, 1)
sma2 = self._sma(part2, 12, 1)
return sma1 / sma2 * 100
def alpha080(self):
"""
(VOLUME-DELAY(VOLUME,5))/DELAY(VOLUME,5)*100
"""
return self.V.pct_change(periods=5) * 100.0
def alpha081(self):
"""
SMA(VOLUME,21,2)
"""
return self._sma(self.V, 21, 2)
def alpha082(self):
"""
SMA((TSMAX(HIGH,6)-CLOSE)/(TSMAX(HIGH,6)-TSMIN(LOW,6))*100,20,1)
"""
high_max = self.H.rolling(window=6, min_periods=6).max()
low_min = self.L.rolling(window=6, min_periods=6).min()
part1 = (high_max - self.C) / (high_max - low_min) * 100
return self._sma(part1, 20, 1)
def alpha083(self):
"""
(-1*RANK(COVIANCE(RANK(HIGH),RANK(VOLUME),5)))
"""
alpha = self.H.rank(pct=True).rolling(window=5, min_periods=5).cov(self.V.rank(pct=True))
return -1 * alpha.rank(pct=True)
def alpha084(self):
"""
SUM((CLOSE>DELAY(CLOSE,1)?VOLUME:(CLOSE<DELAY(CLOSE,1)?-VOLUME:0)),20)
"""
part1 = np.sign(self.C.diff()) * self.V
return part1.rolling(window=20, min_periods=20).sum()
def alpha085(self):
"""
TSRANK(VOLUME/MEAN(VOLUME,20),20)*TSRANK(-1*DELTA(CLOSE,7),8)
"""
part1 = self.V / self.V.rolling(window=20, min_periods=20).mean()
part1 = self._tsrank(part1, 20)
part2 = -1 * self.C.diff(7)
part2 = self._tsrank(part2, 8)
return part1 * part2
def alpha086(self):
"""
((0.25<((DELAY(CLOSE,20)-DELAY(CLOSE,10))/10-(DELAY(CLOSE,10)-CLOSE)/10))?-1:((((DELAY(CLOSE,20)-DELAY(CLOSE,10))/10-(DELAY(CLOSE,10)-CLOSE)/10)<0)?1:(DELAY(CLOSE,1)-CLOSE)))
"""
part = (self.C.shift(20) - self.C.shift(10)) / 10 - (self.C.shift(10) - self.C) / 10
condition1 = part > 0.25
condition2 = part < 0.0
alpha = pd.Series(index=self.df.index, dtype=float)
alpha[condition1] = -1.0
alpha[~condition1 & condition2] = 1.0
alpha[~condition1 & ~condition2] = self.C.shift(1) - self.C
return alpha
def alpha087(self):
"""
(RANK(DECAYLINEAR(DELTA(VWAP,4),7))+TSRANK(DECAYLINEAR((LOW-VWAP)/(OPEN-(HIGH+LOW)/2),11),7))*-1
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w7 = np.arange(1, 8)
w11 = np.arange(1, 12)
vwap_diff = vwap.diff(4).fillna(0)
part1_decay = vwap_diff.rolling(window=7, min_periods=4).apply(
lambda x: np.dot(x, w7[:len(x)]) if len(x) >= 4 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
hl_avg = (self.H + self.L) / 2
denominator = self.O - hl_avg
denominator = denominator.replace(0, 1e-10)
mask_small = abs(denominator) < 1e-8
denominator[mask_small] = 1e-10 * np.sign(denominator[mask_small])
part2_series = (self.L - vwap) / denominator
part2_series = part2_series.replace([np.inf, -np.inf], np.nan).fillna(method='ffill').fillna(method='bfill').clip(-100, 100)
part2_decay = part2_series.rolling(window=11, min_periods=6).apply(
lambda x: np.dot(x, w11[:len(x)]) if len(x) >= 6 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = self._tsrank_fixed(part2_decay, 7)
return -1 * (part1 + part2).fillna(method='ffill').fillna(method='bfill')
def alpha088(self):
"""
(CLOSE-DELAY(CLOSE,20))/DELAY(CLOSE,20)*100
"""
return self.C.pct_change(periods=20) * 100
def alpha089(self):
"""
2*(SMA(CLOSE,13,2)-SMA(CLOSE,27,2)-SMA(SMA(CLOSE,13,2)-SMA(CLOSE,27,2),10,2))
"""
sma13 = self._sma(self.C, 13, 2)
sma27 = self._sma(self.C, 27, 2)
part = sma13 - sma27
return 2.0 * (part - self._sma(part, 10, 2))
def alpha090(self):
"""
(RANK(CORR(RANK(VWAP),RANK(VOLUME),5))*-1)
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
vwap_rank = vwap.rank(pct=True, method='min')
vol_rank = self.V.rank(pct=True, method='min')
corr = vwap_rank.rolling(window=5, min_periods=3).corr(vol_rank).fillna(method='ffill').fillna(method='bfill')
alpha = -1 * corr.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha091(self):
"""
((RANK(CLOSE-MAX(CLOSE,5))*RANK(CORR(MEAN(VOLUME,40),LOW,5)))*-1)
"""
part1 = (self.C - self.C.rolling(window=5, min_periods=5).max()).rank(pct=True)
part2 = self.V.rolling(window=40, min_periods=40).mean().rolling(window=5, min_periods=5).corr(self.L).rank(pct=True)
return -1 * part1 * part2
def alpha092(self):
"""
(MAX(RANK(DECAYLINEAR(DELTA(CLOSE*0.35+VWAP*0.65,2),3)),TSRANK(DECAYLINEAR(ABS(CORR((MEAN(VOLUME,180)),CLOSE,13)),5),15))*-1)
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w3 = np.arange(1, 4)
w5 = np.arange(1, 6)
weighted_price = self.C * 0.35 + vwap * 0.65
weighted_diff = weighted_price.diff(2).fillna(0)
part1_decay = weighted_diff.rolling(window=3, min_periods=2).apply(
lambda x: np.dot(x, w3[:len(x)]) if len(x) >= 2 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
vol_window = min(180, len(self.df) // 2)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//10)).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = vol_ma.rolling(window=13, min_periods=7).corr(self.C).fillna(method='ffill').fillna(method='bfill')
part2_abs = abs(part2_corr)
part2_decay = part2_abs.rolling(window=5, min_periods=3).apply(
lambda x: np.dot(x, w5[:len(x)]) if len(x) >= 3 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = self._tsrank_fixed(part2_decay, 15)
return -1 * np.maximum(part1, part2).fillna(method='ffill').fillna(method='bfill')
def alpha093(self):
"""
SUM(OPEN>=DELAY(OPEN,1)?0:MAX(OPEN-LOW,OPEN-DELAY(OPEN,1)),20)
"""
condition = self.O.diff() >= 0.0
alpha = np.maximum(self.O - self.L, self.O.diff())
alpha[condition] = 0.0
return alpha.rolling(window=20, min_periods=20).sum()
def alpha094(self):
"""
SUM((CLOSE>DELAY(CLOSE,1)?VOLUME:(CLOSE<DELAY(CLOSE,1)?-VOLUME:0)),30)
"""
part1 = np.sign(self.C.diff()) * self.V
return part1.rolling(window=30, min_periods=30).sum()
def alpha095(self):
"""
STD(AMOUNT,20)
"""
return self.AMOUNT.rolling(window=20, min_periods=20).std()
def alpha096(self):
"""
SMA(SMA((CLOSE-TSMIN(LOW,9))/(TSMAX(HIGH,9)-TSMIN(LOW,9))*100,3,1),3,1)
"""
part1 = self.C - self.C.rolling(window=9, min_periods=9).min()
part2 = self.H.rolling(window=9, min_periods=9).max() - self.L.rolling(window=9, min_periods=9).min()
rsv = part1 / part2 * 100
sma1 = self._sma(rsv, 3, 1)
return self._sma(sma1, 3, 1)
def alpha097(self):
"""
STD(VOLUME,10)
"""
return self.V.rolling(window=10, min_periods=10).std()
def alpha098(self):
"""
(DELTA(SUM(CLOSE,100)/100,100)/DELAY(CLOSE,100)<=0.05)?(-1*(CLOSE-TSMIN(CLOSE,100))):(-1*DELTA(CLOSE,3))
"""
condition1 = self.C.rolling(window=100, min_periods=100).mean().diff(100) / self.C.shift(100) <= 0.05
alpha = pd.Series(index=self.df.index, dtype=float)
alpha[condition1] = -1 * (self.C - self.C.rolling(window=100, min_periods=100).min())
alpha[~condition1] = -1 * self.C.diff(3)
return alpha
def alpha099(self):
"""
(-1*RANK(COVIANCE(RANK(CLOSE),RANK(VOLUME),5)))
"""
alpha = self.C.rank(pct=True).rolling(window=5, min_periods=5).cov(self.V.rank(pct=True))
return -1 * alpha.rank(pct=True)
def alpha100(self):
"""
STD(VOLUME,20)
"""
return self.V.rolling(window=20, min_periods=20).std()
def alpha101(self):
"""
(RANK(CORR(CLOSE,SUM(MEAN(VOLUME,30),37),15)) < RANK(CORR(RANK(HIGH*0.1+VWAP*0.9),RANK(VOLUME),11)))*-1
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
vol_window = min(30, len(self.df) // 3)
sum_window = min(37, len(self.df) // 3)
corr_window = min(15, len(self.df) // 4)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//6)).mean().fillna(method='ffill').fillna(method='bfill')
vol_sum = vol_ma.rolling(window=sum_window, min_periods=max(5, sum_window//6)).sum().fillna(method='ffill').fillna(method='bfill')
part1_corr = vol_sum.rolling(window=corr_window, min_periods=max(5, corr_window//3)).corr(self.C).fillna(method='ffill').fillna(method='bfill')
part1 = part1_corr.rank(pct=True, method='min')
weighted_price = self.H * 0.1 + vwap * 0.9
weighted_rank = weighted_price.rank(pct=True, method='min')
vol_rank = self.V.rank(pct=True, method='min')
corr_window2 = min(11, len(self.df) // 4)
part2_corr = weighted_rank.rolling(window=corr_window2, min_periods=max(4, corr_window2//3)).corr(vol_rank).fillna(method='ffill').fillna(method='bfill')
part2 = part2_corr.rank(pct=True, method='min')
return -1 * (part2 - part1).fillna(method='ffill').fillna(method='bfill')
def alpha102(self):
"""
SMA(MAX(VOLUME-DELAY(VOLUME,1),0),6,1)/SMA(ABS(VOLUME-DELAY(VOLUME,1)),6,1)*100
"""
diff = self.V.diff()
part1 = np.maximum(diff, 0.0)
part2 = abs(diff)
sma1 = self._sma(part1, 6, 1)
sma2 = self._sma(part2, 6, 1)
return sma1 / sma2 * 100
def alpha103(self):
"""
((20-LOWDAY(LOW,20))/20)*100
"""
def lowday(x):
return 19 - x.argmin() if len(x) == 20 else np.nan
return (20 - self.L.rolling(window=20, min_periods=20).apply(lowday)) / 20 * 100
def alpha104(self):
"""
-1*(DELTA(CORR(HIGH,VOLUME,5),5)*RANK(STD(CLOSE,20)))
"""
part1 = self.H.rolling(window=5, min_periods=5).corr(self.V).diff(5)
part2 = self.C.rolling(window=20, min_periods=20).std().rank(pct=True)
return -1 * part1 * part2
def alpha105(self):
"""
-1*CORR(RANK(OPEN),RANK(VOLUME),10)
"""
return -1 * self.O.rank(pct=True).rolling(window=10, min_periods=10).corr(self.V.rank(pct=True))
def alpha106(self):
"""
CLOSE-DELAY(CLOSE,20)
"""
return self.C.diff(20)
def alpha107(self):
"""
(-1*RANK(OPEN-DELAY(HIGH,1)))*RANK(OPEN-DELAY(CLOSE,1))*RANK(OPEN-DELAY(LOW,1))
"""
part1 = -1 * (self.O - self.H.shift(1)).rank(pct=True)
part2 = (self.O - self.C.shift(1)).rank(pct=True)
part3 = (self.O - self.L.shift(1)).rank(pct=True)
return part1 * part2 * part3
def alpha108(self):
"""
(RANK(HIGH-MIN(HIGH,2))^RANK(CORR(VWAP,MEAN(VOLUME,120),6)))*-1
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
high_min = self.H.rolling(window=2, min_periods=1).min()
part1 = (self.H - high_min).rank(pct=True, method='min')
vol_window = min(120, len(self.df) // 2)
corr_window = min(6, len(self.df) // 6)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//10)).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = vwap.rolling(window=corr_window, min_periods=max(3, corr_window//2)).corr(vol_ma).fillna(method='ffill').fillna(method='bfill')
part2 = part2_corr.rank(pct=True, method='min')
alpha = pd.Series(index=self.df.index, dtype=float)
for i in range(len(self.df)):
p1 = part1.iloc[i]
p2 = part2.iloc[i]
if pd.notna(p1) and pd.notna(p2):
p1 = max(0.001, min(p1, 0.999))
try:
alpha.iloc[i] = -(p1 ** p2)
except:
alpha.iloc[i] = np.nan
else:
alpha.iloc[i] = np.nan
return alpha.fillna(method='ffill').fillna(method='bfill')
def alpha109(self):
"""
SMA(HIGH-LOW,10,2)/SMA(SMA(HIGH-LOW,10,2),10,2)
"""
hl = self.H - self.L
sma = self._sma(hl, 10, 2)
return sma / self._sma(sma, 10, 2)
def alpha110(self):
"""
SUM(MAX(0,HIGH-DELAY(CLOSE,1)),20)/SUM(MAX(0,DELAY(CLOSE,1)-LOW),20)*100
"""
part1 = np.maximum(self.H - self.C.shift(1), 0.0).rolling(window=20, min_periods=20).sum()
part2 = np.maximum(self.C.shift(1) - self.L, 0.0).rolling(window=20, min_periods=20).sum()
return part1 / part2 * 100.0
def alpha111(self):
"""
SMA(VOL*(2*CLOSE-LOW-HIGH)/(HIGH-LOW),11,2)-SMA(VOL*(2*CLOSE-LOW-HIGH)/(HIGH-LOW),4,2)
"""
win_vol = self.V * (2 * self.C - self.L - self.H) / (self.H - self.L)
return self._sma(win_vol, 11, 2) - self._sma(win_vol, 4, 2)
def alpha112(self):
"""
(SUM((CLOSE-DELAY(CLOSE,1)>0?CLOSE-DELAY(CLOSE,1):0),12)-SUM((CLOSE-DELAY(CLOSE,1)<0?ABS(CLOSE-DELAY(CLOSE,1)):0),12))
/(SUM((CLOSE-DELAY(CLOSE,1)>0?CLOSE-DELAY(CLOSE,1):0),12)+SUM((CLOSE-DELAY(CLOSE,1)<0?ABS(CLOSE-DELAY(CLOSE,1)):0),12))*100
"""
diff = self.C.diff()
part1 = np.maximum(diff, 0.0).rolling(window=12, min_periods=12).sum()
part2 = abs(np.minimum(diff, 0.0)).rolling(window=12, min_periods=12).sum()
return (part1 - part2) / (part1 + part2) * 100
def alpha113(self):
"""
-1*RANK(SUM(DELAY(CLOSE,5),20)/20)*CORR(CLOSE,VOLUME,2)*RANK(CORR(SUM(CLOSE,5),SUM(CLOSE,20),2))
"""
self.V = self.V.replace(0, np.nan).fillna(method='ffill').fillna(method='bfill')
part1_series = self.C.shift(5).rolling(window=20, min_periods=10).mean().fillna(method='ffill').fillna(method='bfill')
part1 = part1_series.rank(pct=True, method='min')
part2 = self.C.rolling(window=2, min_periods=2).corr(self.V).fillna(method='ffill').fillna(method='bfill').clip(-1, 1)
sum5 = self.C.rolling(window=5, min_periods=3).sum().fillna(method='ffill').fillna(method='bfill')
sum20 = self.C.rolling(window=20, min_periods=10).sum().fillna(method='ffill').fillna(method='bfill')
part3_corr = sum5.rolling(window=2, min_periods=2).corr(sum20).fillna(method='ffill').fillna(method='bfill').clip(-1, 1)
part3 = part3_corr.rank(pct=True, method='min')
return -1 * part1 * part2 * part3
def alpha114(self):
"""
RANK(DELAY((HIGH-LOW)/(SUM(CLOSE,5)/5),2))*RANK(RANK(VOLUME))/((HIGH-LOW)/(SUM(CLOSE,5)/5)/(VWAP-CLOSE))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
hl = self.H - self.L
close_ma5 = self.C.rolling(window=5, min_periods=3).mean().fillna(method='ffill').fillna(method='bfill')
hl_ma = (hl / close_ma5).fillna(method='ffill').fillna(method='bfill')
part1 = hl_ma.shift(2).rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
vol_rank1 = self.V.rank(pct=True, method='min')
part2 = vol_rank1.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
vwap_close_diff = vwap - self.C
threshold = 0.0001
mask_small = abs(vwap_close_diff) < threshold
vwap_close_diff[mask_small] = threshold * np.sign(vwap_close_diff[mask_small])
vwap_close_diff = vwap_close_diff.replace(0, threshold)
part3 = (hl_ma / vwap_close_diff).fillna(method='ffill').fillna(method='bfill')
part3 = part3.clip(lower=part3.quantile(0.01), upper=part3.quantile(0.99))
alpha = (part1 * part2 / part3).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha115(self):
"""
(RANK(CORR(HIGH*0.9+CLOSE*0.1,MEAN(VOLUME,30),10))^RANK(CORR(TSRANK((HIGH+LOW)/2,4),TSRANK(VOLUME,10),7)))
"""
part1 = (self.H * 0.9 + self.C * 0.1).rolling(window=10, min_periods=10).corr(
self.V.rolling(window=30, min_periods=30).mean()
).rank(pct=True)
part2 = self._tsrank((self.H + self.L) / 2, 4)
part2 = part2.rolling(window=7, min_periods=7).corr(self._tsrank(self.V, 10)).rank(pct=True)
return part1 ** part2
def alpha116(self):
"""
REGBETA(CLOSE,SEQUENCE,20)
"""
result = pd.Series(index=self.df.index, dtype=float)
for i in range(20, len(self.df)):
y = self.C.iloc[i-20:i]
x = np.arange(1, 21)
result.iloc[i] = self._regbeta(y, x)
return result.fillna(0)
def alpha117(self):
"""
TSRANK(VOLUME,32)*(1-TSRANK(CLOSE+HIGH-LOW,16))*(1-TSRANK(RET,32))
"""
part1 = self._tsrank(self.V, 32)
part2 = 1.0 - self._tsrank(self.C + self.H - self.L, 16)
part3 = 1.0 - self._tsrank(self.C.pct_change(), 32)
return part1 * part2 * part3
def alpha118(self):
"""
SUM(HIGH-OPEN,20)/SUM(OPEN-LOW,20)*100
"""
part1 = (self.H - self.O).rolling(window=20, min_periods=20).sum()
part2 = (self.O - self.L).rolling(window=20, min_periods=20).sum()
return part1 / part2 * 100.0
def alpha119(self):
"""
RANK(DECAYLINEAR(CORR(VWAP,SUM(MEAN(VOLUME,5),26),5),7))-RANK(DECAYLINEAR(TSRANK(MIN(CORR(RANK(OPEN),RANK(MEAN(VOLUME,15)),21),9),7),8))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w7 = np.arange(1, 8)
w8 = np.arange(1, 9)
vol_ma5 = self.V.rolling(window=5, min_periods=3).mean().fillna(method='ffill').fillna(method='bfill')
sum_window = min(26, len(self.df) // 3)
corr_window1 = min(5, len(self.df) // 10)
decay_window1 = min(7, len(self.df) // 10)
vol_sum = vol_ma5.rolling(window=sum_window, min_periods=max(5, sum_window//5)).sum().fillna(method='ffill').fillna(method='bfill')
part1_corr = vol_sum.rolling(window=corr_window1, min_periods=max(3, corr_window1//2)).corr(vwap).fillna(method='ffill').fillna(method='bfill')
part1_decay = part1_corr.rolling(window=decay_window1, min_periods=max(3, decay_window1//2)).apply(
lambda x: np.dot(x, w7[:len(x)]) if len(x) >= 3 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min')
vol_window = min(15, len(self.df) // 3)
corr_window2 = min(21, len(self.df) // 3)
min_window = min(9, len(self.df) // 5)
decay_window2 = min(8, len(self.df) // 5)
vol_ma15 = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//3)).mean().fillna(method='ffill').fillna(method='bfill')
vol_ma15_rank = vol_ma15.rank(pct=True, method='min')
open_rank = self.O.rank(pct=True, method='min')
part2_corr = vol_ma15_rank.rolling(window=corr_window2, min_periods=max(5, corr_window2//4)).corr(open_rank).fillna(method='ffill').fillna(method='bfill')
part2_min = part2_corr.rolling(window=min_window, min_periods=max(3, min_window//3)).min().fillna(method='ffill').fillna(method='bfill')
part2_tsrank = self._tsrank_fixed(part2_min, 7)
part2_decay = part2_tsrank.rolling(window=decay_window2, min_periods=max(3, decay_window2//2)).apply(
lambda x: np.dot(x, w8[:len(x)]) if len(x) >= 3 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min')
return (part1 - part2).fillna(method='ffill').fillna(method='bfill')
def alpha120(self):
"""
RANK(VWAP-CLOSE)/RANK(VWAP+CLOSE)
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
denominator = vwap + self.C
denominator = denominator.replace(0, 1e-10)
alpha = ((vwap - self.C) / denominator).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha121(self):
"""
(RANK(VWAP-MIN(VWAP,12))^TSRANK(CORR(TSRANK(VWAP,20),TSRANK(MEAN(VOLUME,60),2),18),3))*-1
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
vwap_min = vwap.rolling(window=12, min_periods=6).min().fillna(method='ffill').fillna(method='bfill')
part1 = (vwap - vwap_min).rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill').clip(0.001, 0.999)
vol_window = min(60, len(self.df) // 2)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//6)).mean().fillna(method='ffill').fillna(method='bfill')
tsrank_vwap = self._tsrank_fixed(vwap, 20)
tsrank_vol = self._tsrank_fixed(vol_ma, 2)
corr_window = min(18, len(self.df) // 3)
part2_corr = tsrank_vwap.rolling(window=corr_window, min_periods=max(5, corr_window//3)).corr(tsrank_vol).fillna(method='ffill').fillna(method='bfill')
part2 = self._tsrank_fixed(part2_corr, 3).fillna(method='ffill').fillna(method='bfill').clip(0.001, 0.999)
alpha = pd.Series(index=self.df.index, dtype=float)
for i in range(len(self.df)):
p1 = part1.iloc[i]
p2 = part2.iloc[i]
if pd.notna(p1) and pd.notna(p2):
try:
alpha.iloc[i] = -(p1 ** p2)
except:
alpha.iloc[i] = np.nan
else:
alpha.iloc[i] = np.nan
return alpha.fillna(method='ffill').fillna(method='bfill')
def alpha122(self):
"""
(SMA(SMA(SMA(LOG(CLOSE),13,2),13,2),13,2)-DELAY(SMA(SMA(SMA(LOG(CLOSE),13,2),13,2),13,2),1))/DELAY(SMA(SMA(SMA(LOG(CLOSE),13,2),13,2),13,2),1)
"""
part1 = np.log(self.C)
part1 = self._sma(part1, 13, 2)
part1 = self._sma(part1, 13, 2)
part1 = self._sma(part1, 13, 2)
return part1.pct_change()
def alpha123(self):
"""
(RANK(CORR(SUM((HIGH+LOW)/2,20),SUM(MEAN(VOLUME,60),20),9)) < RANK(CORR(LOW,VOLUME,6)))*-1
"""
part1 = (self.H * 0.5 + self.L * 0.5).rolling(window=20, min_periods=20).sum()
part1 = self.V.rolling(window=60, min_periods=60).mean().rolling(window=20, min_periods=20).sum().rolling(window=9, min_periods=9).corr(part1).rank(pct=True)
part2 = self.L.rolling(window=6, min_periods=6).corr(self.V).rank(pct=True)
return -1 * (part2 - part1)
def alpha124(self):
"""
(CLOSE-VWAP)/DECAYLINEAR(RANK(TSMAX(CLOSE,30)),2)
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
part1 = self.C - vwap
close_max = self.C.rolling(window=30, min_periods=15).max().fillna(method='ffill').fillna(method='bfill')
part2_rank = close_max.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
w2 = np.arange(1, 3)
part2 = part2_rank.rolling(window=2, min_periods=1).apply(
lambda x: np.dot(x, w2[:len(x)]) if len(x) >= 1 else np.nan
).fillna(method='ffill').fillna(method='bfill').replace(0, 1e-10)
alpha = (part1 / part2).fillna(method='ffill').fillna(method='bfill')
alpha = alpha.clip(lower=alpha.quantile(0.01), upper=alpha.quantile(0.99))
return alpha
def alpha125(self):
"""
RANK(DECAYLINEAR(CORR(VWAP,MEAN(VOLUME,80),17),20))/RANK(DECAYLINEAR(DELTA(CLOSE*0.5+VWAP*0.5,3),16))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w20 = np.arange(1, 21)
w16 = np.arange(1, 17)
vol_window = min(80, len(self.df) // 2)
corr_window = min(17, len(self.df) // 3)
decay_window1 = min(20, len(self.df) // 3)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//8)).mean().fillna(method='ffill').fillna(method='bfill')
part1_corr = vol_ma.rolling(window=corr_window, min_periods=max(5, corr_window//3)).corr(vwap).fillna(method='ffill').fillna(method='bfill')
part1_decay = part1_corr.rolling(window=decay_window1, min_periods=max(5, decay_window1//4)).apply(
lambda x: np.dot(x, w20[:len(x)]) if len(x) >= 5 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
decay_window2 = min(16, len(self.df) // 3)
weighted_price = self.C * 0.5 + vwap * 0.5
part2_diff = weighted_price.diff(3).fillna(0)
part2_decay = part2_diff.rolling(window=decay_window2, min_periods=max(5, decay_window2//3)).apply(
lambda x: np.dot(x, w16[:len(x)]) if len(x) >= 5 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill').replace(0, 1e-10)
alpha = (part1 / part2).fillna(method='ffill').fillna(method='bfill')
alpha = alpha.clip(lower=alpha.quantile(0.01), upper=alpha.quantile(0.99))
return alpha
def alpha126(self):
"""
(CLOSE+HIGH+LOW)/3
"""
return (self.C + self.H + self.L) / 3.0
def alpha127(self):
"""
MEAN((100*(CLOSE-MAX(CLOSE,12))/MAX(CLOSE,12))^2)^(1/2)
"""
close_max = self.C.rolling(window=12, min_periods=12).max()
alpha = (self.C - close_max) / close_max * 100
return (alpha ** 2).rolling(window=12, min_periods=12).mean() ** 0.5
def alpha128(self):
"""
100-(100/(1+SUM(((HIGH+LOW+CLOSE)/3>DELAY((HIGH+LOW+CLOSE)/3,1)?(HIGH+LOW+CLOSE)/3*VOLUME:0),14)/
SUM(((HIGH+LOW+CLOSE)/3<DELAY((HIGH+LOW+CLOSE)/3,1)?(HIGH+LOW+CLOSE)/3*VOLUME:0),14)))
"""
tp = (self.H + self.L + self.C) / 3.0
condition1 = tp.diff() > 0.0
condition2 = tp.diff() < 0.0
part1 = tp * self.V
part1[~condition1] = 0.0
part1 = part1.rolling(window=14, min_periods=14).sum()
part2 = tp * self.V
part2[~condition2] = 0.0
part2 = part2.rolling(window=14, min_periods=14).sum()
return 100.0 - 100.0 / (1 + part1 / part2)
def alpha129(self):
"""
SUM((CLOSE-DELAY(CLOSE,1)<0?ABS(CLOSE-DELAY(CLOSE,1)):0),12)
"""
return abs(np.minimum(self.C.diff(), 0.0)).rolling(window=12, min_periods=12).sum()
def alpha130(self):
"""
(RANK(DECAYLINEAR(CORR((HIGH+LOW)/2,MEAN(VOLUME,40),9),10))/RANK(DECAYLINEAR(CORR(RANK(VWAP),RANK(VOLUME),7),3)))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w10 = np.arange(1, 11)
w3 = np.arange(1, 4)
vol_window = min(40, len(self.df) // 2)
corr_window1 = min(9, len(self.df) // 4)
decay_window1 = min(10, len(self.df) // 4)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//4)).mean().fillna(method='ffill').fillna(method='bfill')
hl_avg = self.H * 0.5 + self.L * 0.5
part1_corr = vol_ma.rolling(window=corr_window1, min_periods=max(5, corr_window1//2)).corr(hl_avg).fillna(method='ffill').fillna(method='bfill')
part1_decay = part1_corr.rolling(window=decay_window1, min_periods=max(5, decay_window1//2)).apply(
lambda x: np.dot(x, w10[:len(x)]) if len(x) >= 5 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
corr_window2 = min(7, len(self.df) // 5)
decay_window2 = min(3, len(self.df) // 10)
vwap_rank = vwap.rank(pct=True, method='min')
vol_rank = self.V.rank(pct=True, method='min')
part2_corr = vwap_rank.rolling(window=corr_window2, min_periods=max(4, corr_window2//2)).corr(vol_rank).fillna(method='ffill').fillna(method='bfill')
part2_decay = part2_corr.rolling(window=decay_window2, min_periods=2).apply(
lambda x: np.dot(x, w3[:len(x)]) if len(x) >= 2 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill').replace(0, 1e-10)
alpha = (part1 / part2).fillna(method='ffill').fillna(method='bfill')
alpha = alpha.clip(lower=alpha.quantile(0.01), upper=alpha.quantile(0.99))
return alpha
def alpha131(self):
"""
(RANK(DELTA(VWAP,1))^TSRANK(CORR(CLOSE,MEAN(VOLUME,50),18),18))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
vwap_diff = vwap.diff().fillna(0)
part1 = vwap_diff.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill').clip(0.001, 0.999)
vol_window = min(50, len(self.df) // 2)
corr_window = min(18, len(self.df) // 3)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//5)).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = vol_ma.rolling(window=corr_window, min_periods=max(5, corr_window//3)).corr(self.C).fillna(method='ffill').fillna(method='bfill')
part2 = self._tsrank_fixed(part2_corr, 18).fillna(method='ffill').fillna(method='bfill').clip(0.001, 0.999)
alpha = np.exp(np.log(part1 + 1e-10) * part2)
alpha = alpha.fillna(method='ffill').fillna(method='bfill').clip(0, 100)
return alpha
def alpha132(self):
"""
MEAN(AMOUNT,20)
"""
return self.AMOUNT.rolling(window=20, min_periods=20).mean()
def alpha133(self):
"""
((20-HIGHDAY(HIGH,20))/20)*100-((20-LOWDAY(LOW,20))/20)*100
"""
def highday(x):
return 19 - x.argmax() if len(x) == 20 else np.nan
def lowday(x):
return 19 - x.argmin() if len(x) == 20 else np.nan
part1 = (20 - self.H.rolling(window=20, min_periods=20).apply(highday)) / 20 * 100
part2 = (20 - self.L.rolling(window=20, min_periods=20).apply(lowday)) / 20 * 100
return part1 - part2
def alpha134(self):
"""
(CLOSE-DELAY(CLOSE,12))/DELAY(CLOSE,12)*VOLUME
"""
return self.C.pct_change(periods=12) * self.V
def alpha135(self):
"""
SMA(DELAY(CLOSE/DELAY(CLOSE,20),1),20,1)
"""
alpha = (self.C / self.C.shift(20)).shift(1)
return self._sma(alpha, 20, 1)
def alpha136(self):
"""
-1*RANK(DELTA(RET,3))*CORR(OPEN,VOLUME,10)
"""
ret = self.C.pct_change()
part1 = ret.diff(3).rank(pct=True)
part2 = self.O.rolling(window=10, min_periods=10).corr(self.V)
return -1 * part1 * part2
def alpha137(self):
"""
16*(CLOSE+(CLOSE-OPEN)/2-DELAY(OPEN,1))/
((ABS(HIGH-DELAY(CLOSE,1))>ABS(LOW-DELAY(CLOSE,1))&ABS(HIGH-DELAY(CLOSE,1))>ABS(HIGH-DELAY(LOW,1))?ABS(HIGH-DELAY(CLOSE,1))+ABS(LOW-DELAY(CLOSE,1))/2+ABS(DELAY(CLOSE,1)-DELAY(OPEN,1))/4:
(ABS(LOW-DELAY(CLOSE,1))>ABS(HIGH-DELAY(LOW,1)) & ABS(LOW-DELAY(CLOSE,1))>ABS(HIGH-DELAY(CLOSE,1))?ABS(LOW-DELAY(CLOSE,1))+ABS(HIGH-DELAY(CLOSE,1))/2+ABS(DELAY(CLOSE,1)-DELAY(OPEN,1))/4:ABS(HIGH-DELAY(LOW,1))+ABS(DELAY(CLOSE,1)-DELAY(OPEN,1))/4)))
*MAX(ABS(HIGH-DELAY(CLOSE,1)),ABS(LOW-DELAY(CLOSE,1)))
"""
part1 = self.C * 1.5 - self.O * 0.5 - self.O.shift(1)
part2 = abs(self.H - self.C.shift(1)) + abs(self.L - self.C.shift(1)) / 2.0 + abs(self.C - self.O).shift(1) / 4.0
condition1 = np.logical_and(
abs(self.H - self.C.shift(1)) > abs(self.L - self.C.shift(1)),
abs(self.H - self.C.shift(1)) > abs(self.H - self.L.shift(1))
)
condition2 = np.logical_and(
abs(self.L - self.C.shift(1)) > abs(self.H - self.L.shift(1)),
abs(self.L - self.C.shift(1)) > abs(self.H - self.C.shift(1))
)
part2[~condition1 & condition2] = abs(self.L - self.C.shift(1)) + abs(self.H - self.C.shift(1)) / 2.0 + abs(self.C - self.O).shift(1) / 4.0
part2[~condition1 & ~condition2] = abs(self.H - self.L.shift(1)) + abs(self.C - self.O).shift(1) / 4.0
part3 = np.maximum(abs(self.H - self.C.shift(1)), abs(self.L - self.C.shift(1)))
alpha = part1 / part2 * part3 * 16.0
return alpha
def alpha138(self):
"""
((RANK(DECAYLINEAR(DELTA(LOW*0.7+VWAP*0.3,3),20))
-TSRANK(DECAYLINEAR(TSRANK(
CORR(TSRANK(LOW,8),TSRANK(MEAN(VOLUME,60),17),5)
,19),16),7))* -1)
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w20 = np.arange(1, 21)
w16 = np.arange(1, 17)
decay_window1 = min(20, len(self.df) // 3)
weighted_price = self.L * 0.7 + vwap * 0.3
part1_diff = weighted_price.diff(3).fillna(0)
part1_decay = part1_diff.rolling(window=decay_window1, min_periods=max(5, decay_window1//4)).apply(
lambda x: np.dot(x, w20[:len(x)]) if len(x) >= 5 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
vol_window = min(60, len(self.df) // 2)
tsrank_window1 = min(17, len(self.df) // 3)
corr_window = min(5, len(self.df) // 6)
tsrank_window2 = min(19, len(self.df) // 3)
decay_window2 = min(16, len(self.df) // 3)
tsrank_window3 = min(7, len(self.df) // 5)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//6)).mean().fillna(method='ffill').fillna(method='bfill')
tsrank_vol = self._tsrank_fixed(vol_ma, tsrank_window1).fillna(method='ffill').fillna(method='bfill')
tsrank_low = self._tsrank_fixed(self.L, 8).fillna(method='ffill').fillna(method='bfill')
part2_corr = tsrank_low.rolling(window=corr_window, min_periods=max(3, corr_window//2)).corr(tsrank_vol).fillna(method='ffill').fillna(method='bfill')
part2_tsrank = self._tsrank_fixed(part2_corr, tsrank_window2).fillna(method='ffill').fillna(method='bfill')
part2_decay = part2_tsrank.rolling(window=decay_window2, min_periods=max(5, decay_window2//3)).apply(
lambda x: np.dot(x, w16[:len(x)]) if len(x) >= 5 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = self._tsrank_fixed(part2_decay, tsrank_window3).fillna(method='ffill').fillna(method='bfill')
alpha = -1 * (part1 - part2).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha139(self):
"""
(-1*CORR(OPEN,VOLUME,10))
"""
return -1 * self.O.rolling(window=10, min_periods=10).corr(self.V)
def alpha140(self):
"""
MIN(RANK(DECAYLINEAR(RANK(OPEN)+RANK(LOW)-RANK(HIGH)-RANK(CLOSE),8)),TSRANK(DECAYLINEAR(CORR(TSRANK(CLOSE,8),TSRANK(MEAN(VOLUME,60),20),8),7),3))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
w8 = np.arange(1, 9)
w7 = np.arange(1, 8)
decay_window1 = min(8, len(self.df) // 4)
open_rank = self.O.rank(pct=True, method='min')
low_rank = self.L.rank(pct=True, method='min')
high_rank = self.H.rank(pct=True, method='min')
close_rank = self.C.rank(pct=True, method='min')
part1_series = open_rank + low_rank - high_rank - close_rank
part1_series = part1_series.fillna(method='ffill').fillna(method='bfill')
part1_decay = part1_series.rolling(window=decay_window1, min_periods=max(4, decay_window1//2)).apply(
lambda x: np.dot(x, w8[:len(x)]) if len(x) >= 4 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
vol_window = min(60, len(self.df) // 2)
tsrank_window1 = min(20, len(self.df) // 3)
corr_window = min(8, len(self.df) // 4)
decay_window2 = min(7, len(self.df) // 5)
tsrank_window2 = min(3, len(self.df) // 10)
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//6)).mean().fillna(method='ffill').fillna(method='bfill')
tsrank_vol = self._tsrank_fixed(vol_ma, tsrank_window1).fillna(method='ffill').fillna(method='bfill')
tsrank_close = self._tsrank_fixed(self.C, 8).fillna(method='ffill').fillna(method='bfill')
part2_corr = tsrank_close.rolling(window=corr_window, min_periods=max(4, corr_window//2)).corr(tsrank_vol).fillna(method='ffill').fillna(method='bfill')
part2_decay = part2_corr.rolling(window=decay_window2, min_periods=max(3, decay_window2//2)).apply(
lambda x: np.dot(x, w7[:len(x)]) if len(x) >= 3 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = self._tsrank_fixed(part2_decay, tsrank_window2).fillna(method='ffill').fillna(method='bfill')
alpha = np.minimum(part1, part2).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha141(self):
"""
(RANK(CORR(RANK(HIGH),RANK(MEAN(VOLUME,15)),9))*-1)
"""
alpha = self.V.rolling(window=15, min_periods=15).mean().rank(pct=True)
alpha = alpha.rolling(window=9, min_periods=9).corr(self.H.rank(pct=True)).rank(pct=True)
return -1 * alpha
def alpha142(self):
"""
-1*RANK(TSRANK(CLOSE,10))*RANK(DELTA(DELTA(CLOSE,1),1))*RANK(TSRANK(VOLUME/MEAN(VOLUME,20),5))
"""
part1 = self._tsrank(self.C, 10).rank(pct=True)
part2 = self.C.diff().diff().rank(pct=True)
part3 = self._tsrank(self.V / self.V.rolling(window=20, min_periods=20).mean(), 5).rank(pct=True)
return -1 * part1 * part2 * part3
def alpha143(self):
"""
CLOSE>DELAY(CLOSE,1)?(CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1)*SELF:SELF
"""
condition = self.C > self.C.shift(1)
alpha = self.C.pct_change()
alpha[~condition] = alpha.shift(1)[~condition]
return alpha
def alpha144(self):
"""
SUMIF(ABS(CLOSE/DELAY(CLOSE,1)-1)/AMOUNT,20,CLOSE<DELAY(CLOSE,1))/COUNT(CLOSE<DELAY(CLOSE,1),20)
"""
part1 = abs(self.C.pct_change()) / self.AMOUNT
part1[self.C.diff() >= 0] = 0.0
part1 = part1.rolling(window=20, min_periods=20).sum()
part2 = (self.C.diff() < 0.0).rolling(window=20, min_periods=20).sum()
return part1 / part2
def alpha145(self):
"""
(MEAN(VOLUME,9)-MEAN(VOLUME,26))/MEAN(VOLUME,12)*100
"""
ma9 = self.V.rolling(window=9, min_periods=9).mean()
ma26 = self.V.rolling(window=26, min_periods=26).mean()
ma12 = self.V.rolling(window=12, min_periods=12).mean()
return (ma9 - ma26) / ma12 * 100.0
def alpha146(self):
"""
MEAN(RET-SMA(RET,61,2),20)*(RET-SMA(RET,61,2))/SMA(SMA(RET,61,2)^2,60)
"""
ret = self.C.pct_change()
sma = self._sma(ret, 61, 2)
ret_excess = ret - sma
part1 = ret_excess.rolling(window=20, min_periods=20).mean() * ret_excess
part2 = self._sma(sma ** 2, 60, 1)
return part1 / part2
def alpha147(self):
"""
REGBETA(MEAN(CLOSE,12),SEQUENCE(12))
"""
ma_price = self.C.rolling(window=12, min_periods=12).mean()
result = pd.Series(index=self.df.index, dtype=float)
for i in range(12, len(self.df)):
y = ma_price.iloc[i-12:i]
x = np.arange(1, 13)
result.iloc[i] = self._regbeta(y, x)
return result.fillna(0)
def alpha148(self):
"""
(RANK(CORR(OPEN,SUM(MEAN(VOLUME,60),9),6))<RANK(OPEN-TSMIN(OPEN,14)))*-1
"""
part1 = self.V.rolling(window=60, min_periods=60).mean().rolling(window=9, min_periods=9).sum()
part1 = part1.rolling(window=6, min_periods=6).corr(self.O).rank(pct=True)
part2 = (self.O - self.O.rolling(window=14, min_periods=14).min()).rank(pct=True)
return -1 * (part2 - part1)
def alpha149(self):
"""
REGBETA(FILTER(RET,BANCHMARK_INDEX_CLOSE<DELAY(BANCHMARK_INDEX_CLOSE,1)),
FILTER(BANCHMARK_INDEX_CLOSE/DELAY(BANCHMARK_INDEX_CLOSE,1)-1,BANCHMARK_INDEX_CLOSE<DELAY(BANCHMARK_INDEX_CLOSE,1)),252)
调整窗口期以适应数据量
"""
n_rows = len(self.df)
if n_rows < 252:
window = max(60, n_rows // 2)
else:
window = 252
if hasattr(self, 'index_df') and self.index_df is not None:
index_data = self.index_df.copy()
if 'close' in index_data.columns:
index_close = index_data['close']
elif 'closePrice' in index_data.columns:
index_close = index_data['closePrice']
else:
index_close = index_data.iloc[:, 0]
if 'date' in self.df.columns and 'date' in index_data.columns:
self.df['date'] = pd.to_datetime(self.df['date'])
index_data['date'] = pd.to_datetime(index_data['date'])
date_to_index = dict(zip(index_data['date'], index_close))
bm_close = self.df['date'].map(date_to_index).fillna(method='ffill').fillna(method='bfill')
else:
bm_close = index_close.reindex(self.df.index, method='ffill')
else:
bm_close = self.C.rolling(window=20, min_periods=5).mean()
bm_ret = bm_close.pct_change().fillna(0)
bm_down = bm_ret < 0.0
stock_ret = self.C.pct_change().fillna(0)
result = pd.Series(index=self.df.index, dtype=float)
if n_rows < window:
return pd.Series(0, index=self.df.index)
for i in range(window, n_rows):
start_idx = i - window
bm_down_window = bm_down.iloc[start_idx:i]
valid_indices = bm_down_window[bm_down_window].index
if len(valid_indices) < 5:
result.iloc[i] = np.nan
continue
y = stock_ret.loc[valid_indices]
x = bm_ret.loc[valid_indices]
valid_mask = ~(y.isna() | x.isna())
y_clean = y[valid_mask]
x_clean = x[valid_mask]
if len(y_clean) > 3:
try:
slope, intercept, r_value, p_value, std_err = stats.linregress(x_clean, y_clean)
result.iloc[i] = slope
except:
result.iloc[i] = np.nan
else:
result.iloc[i] = np.nan
result = result.fillna(method='ffill').fillna(method='bfill').fillna(0)
return result
def alpha150(self):
"""
(CLOSE+HIGH+LOW)/3*VOLUME
"""
return (self.C + self.H + self.L) / 3.0 * self.V
def alpha151(self):
"""
SMA(CLOSE-DELAY(CLOSE,20),20,1)
"""
return self._sma(self.C.diff(20), 20, 1)
def alpha152(self):
"""
A=DELAY(SMA(DELAY(CLOSE/DELAY(CLOSE,9),1),9,1),1)
SMA(MEAN(A,12)-MEAN(A,26),9,1)
"""
a = (self.C / self.C.shift(9)).shift(1)
a = self._sma(a, 9, 1).shift(1)
alpha = (a.rolling(window=12, min_periods=12).mean() - a.rolling(window=26, min_periods=26).mean())
alpha = self._sma(alpha, 9, 1)
return alpha
def alpha153(self):
"""
(MEAN(CLOSE,3)+MEAN(CLOSE,6)+MEAN(CLOSE,12)+MEAN(CLOSE,24))/4
"""
ma3 = self.C.rolling(window=3, min_periods=3).mean()
ma6 = self.C.rolling(window=6, min_periods=6).mean()
ma12 = self.C.rolling(window=12, min_periods=12).mean()
ma24 = self.C.rolling(window=24, min_periods=24).mean()
return (ma3 + ma6 + ma12 + ma24) / 4
def alpha154(self):
"""
VWAP-MIN(VWAP,16)<CORR(VWAP,MEAN(VOLUME,180),18)
"""
n_rows = len(self.df)
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
vwap_min = vwap.rolling(window=16, min_periods=8).min().fillna(method='ffill').fillna(method='bfill')
part1 = vwap - vwap_min
if n_rows < 180:
vol_window = max(60, n_rows // 2)
corr_window = max(10, min(18, n_rows // 5))
else:
vol_window = 180
corr_window = 18
vol_ma = self.V.rolling(window=vol_window, min_periods=max(10, vol_window//10)).mean().fillna(method='ffill').fillna(method='bfill')
part2_corr = vol_ma.rolling(window=corr_window, min_periods=max(5, corr_window//3)).corr(vwap).fillna(method='ffill').fillna(method='bfill')
alpha = (part2_corr - part1).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha155(self):
"""
SMA(VOLUME,13,2)-SMA(VOLUME,27,2)-SMA(SMA(VOLUME,13,2)-SMA(VOLUME,27,2),10,2)
"""
sma13 = self._sma(self.V, 13, 2)
sma27 = self._sma(self.V, 27, 2)
diff = sma13 - sma27
return sma13 - sma27 - self._sma(diff, 10, 2)
def alpha156(self):
"""
MAX(RANK(DECAYLINEAR(DELTA(VWAP,5),3)),RANK(DECAYLINEAR((DELTA(OPEN*0.15+LOW*0.85,2)/(OPEN*0.15+LOW*0.85)) * -1,3))) * -1
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
w3 = np.arange(1, 4)
den = self.O * 0.15 + self.L * 0.85
vwap_diff = vwap.diff(5).fillna(0)
part1_decay = vwap_diff.rolling(window=3, min_periods=2).apply(
lambda x: np.dot(x, w3[:len(x)]) if len(x) >= 2 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part1 = part1_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
den = den.replace(0, 1e-10)
den_diff = den.diff(2).fillna(0)
den_ratio = (den_diff / den) * (-1)
den_ratio = den_ratio.replace([np.inf, -np.inf], np.nan).fillna(method='ffill').fillna(method='bfill').clip(-100, 100)
part2_decay = den_ratio.rolling(window=3, min_periods=2).apply(
lambda x: np.dot(x, w3[:len(x)]) if len(x) >= 2 else np.nan
).fillna(method='ffill').fillna(method='bfill')
part2 = part2_decay.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
alpha = -1 * np.maximum(part1, part2).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha157(self):
"""
MIN(PROD(RANK(LOG(SUM(TSMIN(RANK(-1*RANK(DELTA(CLOSE-1,5))),2),1))),1),5)+TSRANK(DELAY(-1*RET,6),5)
"""
part1 = (self.C - 1.0).diff(5).rank(pct=True) * (-1)
part1 = part1.rank(pct=True).rolling(window=2, min_periods=2).min()
part1 = np.log(part1.rolling(window=1, min_periods=1).sum()).rank(pct=True)
part1 = part1.rolling(window=5, min_periods=5).min()
part2 = self._tsrank((-1 * self.C.pct_change()).shift(6), 5)
return part1 + part2
def alpha158(self):
"""
(HIGH-LOW)/CLOSE
"""
return (self.H - self.L) / self.C
def alpha159(self):
"""
((CLOSE-SUM(MIN(LOW,DELAY(CLOSE,1)),6))/SUM(MAX(HIGH,DELAY(CLOSE,1))-MIN(LOW,DELAY(CLOSE,1)),6)*12*24
+(CLOSE-SUM(MIN(LOW,DELAY(CLOSE,1)),12))/SUM(MAX(HIGH,DELAY(CLOSE,1))-MIN(LOW,DELAY(CLOSE,1)),12)*6*24
+(CLOSE-SUM(MIN(LOW,DELAY(CLOSE,1)),24))/SUM(MAX(HIGH,DELAY(CLOSE,1))-MIN(LOW,DELAY(CLOSE,1)),24)*6*12)*100/(6*12+6*24+12*24)
"""
min_low_close = np.minimum(self.L, self.C.shift(1))
max_high_close = np.maximum(self.H, self.C.shift(1))
diff = max_high_close - min_low_close
part1 = (self.C - min_low_close.rolling(window=6, min_periods=6).sum()) / diff.rolling(window=6, min_periods=6).sum() * 12 * 24
part2 = (self.C - min_low_close.rolling(window=12, min_periods=12).sum()) / diff.rolling(window=12, min_periods=12).sum() * 6 * 24
part3 = (self.C - min_low_close.rolling(window=24, min_periods=24).sum()) / diff.rolling(window=24, min_periods=24).sum() * 6 * 12
return (part1 + part2 + part3) * 100.0 / (12 * 6 + 6 * 24 + 12 * 24)
def alpha160(self):
"""
SMA((CLOSE<=DELAY(CLOSE,1)?STD(CLOSE,20):0),20,1)
"""
part1 = self.C.rolling(window=20, min_periods=20).std()
part1[self.C.diff() > 0] = 0.0
return self._sma(part1, 20, 1)
def alpha161(self):
"""
MEAN(MAX(MAX(HIGH-LOW,ABS(DELAY(CLOSE,1)-HIGH)),ABS(DELAY(CLOSE,1)-LOW)),12)
"""
part1 = np.maximum(self.H - self.L, abs(self.C.shift(1) - self.H))
part1 = np.maximum(part1, abs(self.C.shift(1) - self.L))
return part1.rolling(window=12, min_periods=12).mean()
def alpha162(self):
"""
(SMA(MAX(CLOSE-DELAY(CLOSE,1),0),12,1)/SMA(ABS(CLOSE-DELAY(CLOSE,1)),12,1)*100
-MIN(SMA(MAX(CLOSE-DELAY(CLOSE,1),0),12,1)/SMA(ABS(CLOSE-DELAY(CLOSE,1)),12,1)*100,12))
/(MAX(SMA(MAX(CLOSE-DELAY(CLOSE,1),0),12,1)/SMA(ABS(CLOSE-DELAY(CLOSE,1)),12,1)*100,12)
-MIN(SMA(MAX(CLOSE-DELAY(CLOSE,1),0),12,1)/SMA(ABS(CLOSE-DELAY(CLOSE,1)),12,1)*100,12))
"""
diff = self.C.diff()
den = np.maximum(diff, 0.0).ewm(adjust=False, alpha=1/12, min_periods=0).mean() / abs(diff).ewm(adjust=False, alpha=1/12, min_periods=0).mean() * 100.0
alpha = (den - den.rolling(window=12, min_periods=12).min()) / (den.rolling(window=12, min_periods=12).max() - den.rolling(window=12, min_periods=12).min())
return alpha
def alpha163(self):
"""
RANK((-1*RET)*MEAN(VOLUME,20)*VWAP*(HIGH-CLOSE))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
ret = self.C.pct_change().fillna(0)
vol_ma = self.V.rolling(window=20, min_periods=10).mean().fillna(method='ffill').fillna(method='bfill')
high_minus_close = self.H - self.C
alpha = (-1 * ret) * vol_ma * vwap * high_minus_close
alpha = alpha.fillna(method='ffill').fillna(method='bfill')
alpha_rank = alpha.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
return alpha_rank
def alpha164(self):
"""
SMA(((CLOSE>DELAY(CLOSE,1)?1/(CLOSE-DELAY(CLOSE,1)):1)-MIN(CLOSE>DELAY(CLOSE,1)?1/(CLOSE-DELAY(CLOSE,1)):1,12))/(HIGH-LOW)*100,13,2)
"""
diff = self.C.diff()
part1 = 1.0 / diff
part1[diff <= 0] = 1.0
part2 = part1.rolling(window=12, min_periods=12).min()
alpha = (part1 - part2) / (self.H - self.L) * 100.0
return self._sma(alpha, 13, 2)
def alpha165(self):
"""
MAX(SUMAC(CLOSE-MEAN(CLOSE,48)))-MIN(SUMAC(CLOSE-MEAN(CLOSE,48)))/STD(CLOSE,48)
"""
part = self.C - self.C.rolling(window=48, min_periods=48).mean()
part = part.rolling(window=48, min_periods=48).sum()
part1 = part.rolling(window=48, min_periods=48).max()
part2 = part.rolling(window=48, min_periods=48).min()
part3 = self.C.rolling(window=48, min_periods=48).std()
return part1 - part2 / part3
def alpha166(self):
"""
-20*(20-1)^1.5*SUM(CLOSE/DELAY(CLOSE,1)-1-MEAN(CLOSE/DELAY(CLOSE,1)-1,20),20)/((20-1)*(20-2)*(SUM((CLOSE/DELAY(CLOSE,1))^2,20))^1.5)
"""
ret = self.C.pct_change()
ret_mean = ret.rolling(window=20, min_periods=20).mean()
part1 = (ret - ret_mean).rolling(window=20, min_periods=20).sum() * (-20 * 19 ** 1.5)
part2 = ((self.C / self.C.shift(1)) ** 2).rolling(window=20, min_periods=20).sum() ** 1.5 * 19 * 18
return part1 / part2
def alpha167(self):
"""
SUM(CLOSE-DELAY(CLOSE,1)>0?CLOSE-DELAY(CLOSE,1):0,12)
"""
return np.maximum(self.C.diff(), 0.0).rolling(window=12, min_periods=12).sum()
def alpha168(self):
"""
-1*VOLUME/MEAN(VOLUME,20)
"""
return -1 * self.V / self.V.rolling(window=20, min_periods=20).mean()
def alpha169(self):
"""
SMA(MEAN(DELAY(SMA(CLOSE-DELAY(CLOSE,1),9,1),1),12)-MEAN(DELAY(SMA(CLOSE-DELAY(CLOSE,1),9,1),1),26),10,1)
"""
part1 = self._sma(self.C.diff(), 9, 1).shift(1)
part2 = part1.rolling(window=12, min_periods=12).mean() - part1.rolling(window=26, min_periods=26).mean()
return self._sma(part2, 10, 1)
def alpha170(self):
"""
((RANK(1/CLOSE)*VOLUME)/MEAN(VOLUME,20))*(HIGH*RANK(HIGH-CLOSE)/(SUM(HIGH,5)/5))-RANK(VWAP-DELAY(VWAP,5))
"""
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
inv_close_rank = (1.0 / self.C).rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
vol_ma = self.V.rolling(window=20, min_periods=10).mean().fillna(method='ffill').fillna(method='bfill').replace(0, 1e-10)
part1 = (inv_close_rank * self.V) / vol_ma
high_minus_close_rank = (self.H - self.C).rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
high_ma5 = self.H.rolling(window=5, min_periods=3).sum() / 5.0
high_ma5 = high_ma5.fillna(method='ffill').fillna(method='bfill').replace(0, 1e-10)
part2 = (self.H * high_minus_close_rank) / high_ma5
vwap_diff = vwap.diff(5).fillna(0)
part3 = vwap_diff.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
alpha = (part1 * part2 - part3).fillna(method='ffill').fillna(method='bfill').clip(-10, 10)
return alpha
def alpha171(self):
"""
(-1*(LOW-CLOSE)*(OPEN^5))/((CLOSE-HIGH)*(CLOSE^5))
"""
self.C = self.C.clip(lower=1e-10)
self.O = self.O.clip(lower=1e-10)
self.H = self.H.clip(lower=1e-10)
self.L = self.L.clip(lower=1e-10)
part1 = (self.C - self.L) * (self.O ** 5)
part2 = (self.C - self.H) * (self.C ** 5)
part2 = part2.replace(0, 1e-10)
mask_small = abs(part2) < 1e-10
part2[mask_small] = 1e-10 * np.sign(part2[mask_small])
alpha = part1 / part2
alpha = alpha.replace([np.inf, -np.inf], np.nan).fillna(method='ffill').fillna(method='bfill').clip(-100, 100)
return alpha
def alpha172(self):
"""
ADX指标
"""
hd = self.H.diff()
ld = -self.L.diff()
tr = np.maximum(
np.maximum(self.H - self.L, abs(self.H - self.C.shift(1))),
abs(self.L - self.C.shift(1))
)
plus_dm = ((hd > 0) & (hd > ld)) * hd
minus_dm = ((ld > 0) & (ld > hd)) * ld
plus_di = plus_dm.rolling(window=14, min_periods=14).sum() * 100 / tr.rolling(window=14, min_periods=14).sum()
minus_di = minus_dm.rolling(window=14, min_periods=14).sum() * 100 / tr.rolling(window=14, min_periods=14).sum()
dx = abs(plus_di - minus_di) / (plus_di + minus_di) * 100
return dx.rolling(window=6, min_periods=6).mean()
def alpha173(self):
"""
3*SMA(CLOSE,13,2)-2*SMA(SMA(CLOSE,13,2),13,2)+SMA(SMA(SMA(LOG(CLOSE),13,2),13,2),13,2)
"""
sma = self._sma(self.C, 13, 2)
sma2 = self._sma(sma, 13, 2)
log_sma = self._sma(np.log(self.C), 13, 2)
log_sma2 = self._sma(log_sma, 13, 2)
log_sma3 = self._sma(log_sma2, 13, 2)
return 3 * sma - 2 * sma2 + log_sma3
def alpha174(self):
"""
SMA((CLOSE>DELAY(CLOSE,1)?STD(CLOSE,20):0),20,1)
"""
part1 = self.C.rolling(window=20, min_periods=20).std()
part1[self.C.diff() <= 0] = 0.0
return self._sma(part1, 20, 1)
def alpha175(self):
"""
MEAN(MAX(MAX(HIGH-LOW,ABS(DELAY(CLOSE,1)-HIGH)),ABS(DELAY(CLOSE,1)-LOW)),6)
"""
part1 = np.maximum(self.H - self.L, abs(self.C.shift(1) - self.H))
part1 = np.maximum(part1, abs(self.C.shift(1) - self.L))
return part1.rolling(window=6, min_periods=6).mean()
def alpha176(self):
"""
CORR(RANK((CLOSE-TSMIN(LOW,12))/(TSMAX(HIGH,12)-TSMIN(LOW,12))),RANK(VOLUME),6)
"""
high_max = self.H.rolling(window=12, min_periods=12).max()
low_min = self.L.rolling(window=12, min_periods=12).min()
part1 = (self.C - low_min) / (high_max - low_min)
part1 = part1.rank(pct=True)
part2 = self.V.rank(pct=True)
return part1.rolling(window=6, min_periods=6).corr(part2)
def alpha177(self):
"""
((20-HIGHDAY(HIGH,20))/20)*100
"""
def highday(x):
return 19 - x.argmax() if len(x) == 20 else np.nan
return (20 - self.H.rolling(window=20, min_periods=20).apply(highday)) / 20 * 100
def alpha178(self):
"""
(CLOSE-DELAY(CLOSE,1))/DELAY(CLOSE,1)*VOLUME
"""
return self.C.pct_change() * self.V
def alpha179(self):
"""
RANK(CORR(VWAP,VOLUME,4))*RANK(CORR(RANK(LOW),RANK(MEAN(VOLUME,50)),12))
"""
n_rows = len(self.df)
self.V = self.V.replace(0, np.nan)
self.AMOUNT = self.AMOUNT.replace(0, np.nan)
vwap = (self.AMOUNT / self.V).fillna(method='ffill').fillna(method='bfill')
self.V = self.V.fillna(method='ffill').fillna(method='bfill')
part1_corr = vwap.rolling(window=4, min_periods=3).corr(self.V).fillna(method='ffill').fillna(method='bfill')
part1 = part1_corr.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
if n_rows < 50:
vol_window = max(20, n_rows // 2)
corr_window = max(5, min(12, n_rows // 4))
else:
vol_window = 50
corr_window = 12
vol_ma = self.V.rolling(window=vol_window, min_periods=max(5, vol_window//5)).mean().fillna(method='ffill').fillna(method='bfill')
low_rank = self.L.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
vol_ma_rank = vol_ma.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
part2_corr = low_rank.rolling(window=corr_window, min_periods=max(3, corr_window//2)).corr(vol_ma_rank).fillna(method='ffill').fillna(method='bfill')
part2 = part2_corr.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
alpha = (part1 * part2).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha180(self):
"""
(MEAN(VOLUME,20)<VOLUME)?((-1*TSRANK(ABS(DELTA(CLOSE,7)),60))*SIGN(DELTA(CLOSE,7)):(-1*VOLUME))
"""
condition = self.V.rolling(window=20, min_periods=20).mean() < self.V
alpha = pd.Series(index=self.df.index, dtype=float)
alpha[condition] = self._tsrank(abs(self.C.diff(7)), 60) * np.sign(self.C.diff(7)) * (-1)
alpha[~condition] = -1 * self.V
return alpha
def alpha181(self):
"""
SUM(RET-MEAN(RET,20)-(BANCHMARK_INDEX_CLOSE-MEAN(BANCHMARK_INDEX_CLOSE,20))^2,20)/SUM((BANCHMARK_INDEX_CLOSE-MEAN(BANCHMARK_INDEX_CLOSE,20))^3)
"""
n_rows = len(self.df)
if hasattr(self, 'index_df') and self.index_df is not None:
index_data = self.index_df.copy()
if 'close' in index_data.columns:
index_close = index_data['close']
elif 'closePrice' in index_data.columns:
index_close = index_data['closePrice']
else:
index_close = index_data.iloc[:, 0]
if 'date' in self.df.columns and 'date' in index_data.columns:
self.df['date'] = pd.to_datetime(self.df['date'])
index_data['date'] = pd.to_datetime(index_data['date'])
date_to_index = dict(zip(index_data['date'], index_close))
bm_close = self.df['date'].map(date_to_index).fillna(method='ffill').fillna(method='bfill')
else:
bm_close = index_close.reindex(self.df.index, method='ffill')
else:
bm_close = self.C.rolling(window=20, min_periods=10).mean()
bm_mean = bm_close - bm_close.rolling(window=20, min_periods=10).mean().fillna(0)
ret = self.C.pct_change().fillna(0)
ret_mean = ret.rolling(window=20, min_periods=10).mean().fillna(0)
part1 = (ret - ret_mean - bm_mean ** 2).rolling(window=20, min_periods=10).sum().fillna(method='ffill').fillna(method='bfill')
part2 = (bm_mean ** 3).rolling(window=20, min_periods=10).sum().fillna(method='ffill').fillna(method='bfill').replace(0, 1e-10)
alpha = (part1 / part2).fillna(method='ffill').fillna(method='bfill').clip(-100, 100)
return alpha
def alpha182(self):
"""
COUNT((CLOSE>OPEN & BANCHMARK_INDEX_CLOSE>BANCHMARK_INDEX_OPEN) OR (CLOSE<OPEN &BANCHMARK_INDEX_CLOSE<BANCHMARK_INDEX_OPEN),20)/20
"""
n_rows = len(self.df)
if hasattr(self, 'index_df') and self.index_df is not None:
index_data = self.index_df.copy()
if 'close' in index_data.columns:
index_close = index_data['close']
elif 'closePrice' in index_data.columns:
index_close = index_data['closePrice']
else:
index_close = index_data.iloc[:, 0]
if 'open' in index_data.columns:
index_open = index_data['open']
elif 'openPrice' in index_data.columns:
index_open = index_data['openPrice']
else:
index_open = index_close.shift(1).fillna(index_close)
if 'date' in self.df.columns and 'date' in index_data.columns:
self.df['date'] = pd.to_datetime(self.df['date'])
index_data['date'] = pd.to_datetime(index_data['date'])
date_to_close = dict(zip(index_data['date'], index_close))
date_to_open = dict(zip(index_data['date'], index_open))
bm_close = self.df['date'].map(date_to_close).fillna(method='ffill').fillna(method='bfill')
bm_open = self.df['date'].map(date_to_open).fillna(method='ffill').fillna(method='bfill')
else:
bm_close = index_close.reindex(self.df.index, method='ffill')
bm_open = index_open.reindex(self.df.index, method='ffill')
else:
bm_close = self.C.rolling(window=20, min_periods=5).mean()
bm_open = self.O.rolling(window=20, min_periods=5).mean()
bm_up = bm_close > bm_open
stock_up = self.C > self.O
stock_down = self.C < self.O
condition1 = stock_up & bm_up
condition2 = stock_down & ~bm_up
condition = condition1 | condition2
min_periods = max(5, min(10, n_rows // 4))
alpha = condition.rolling(window=20, min_periods=min_periods).mean().fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha183(self):
"""
MAX(SUMAC(CLOSE-MEAN(CLOSE,24)))-MIN(SUMAC(CLOSE-MEAN(CLOSE,24)))/STD(CLOSE,24)
"""
part = self.C - self.C.rolling(window=24, min_periods=24).mean()
part = part.rolling(window=24, min_periods=24).sum()
part1 = part.rolling(window=24, min_periods=24).max()
part2 = part.rolling(window=24, min_periods=24).min()
part3 = self.C.rolling(window=24, min_periods=24).std()
return part1 - part2 / part3
def alpha184(self):
"""
RANK(CORR(DELAY(OPEN-CLOSE,1),CLOSE,200))+RANK(OPEN-CLOSE)
"""
n_rows = len(self.df)
if n_rows < 200:
window = max(20, int(n_rows * 0.6))
else:
window = 200
oc = self.O - self.C
min_periods = max(10, min(50, window // 4))
part1_corr = oc.shift(1).rolling(window=window, min_periods=min_periods).corr(self.C).fillna(method='ffill').fillna(method='bfill')
part1 = part1_corr.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
part2 = oc.rank(pct=True, method='min').fillna(method='ffill').fillna(method='bfill')
alpha = ((part1 + part2) / 2.0).fillna(method='ffill').fillna(method='bfill')
return alpha
def alpha185(self):
"""
RANK(-1*(1-OPEN/CLOSE)^2)
"""
return -1 * (1.0 - self.O / self.C) ** 2
def alpha186(self):
"""
ADXR指标
"""
hd = self.H.diff()
ld = -self.L.diff()
tr = np.maximum(
np.maximum(self.H - self.L, abs(self.H - self.C.shift(1))),
abs(self.L - self.C.shift(1))
)
plus_dm = ((hd > 0) & (hd > ld)) * hd
minus_dm = ((ld > 0) & (ld > hd)) * ld
plus_di = plus_dm.rolling(window=14, min_periods=14).sum() * 100 / tr.rolling(window=14, min_periods=14).sum()
minus_di = minus_dm.rolling(window=14, min_periods=14).sum() * 100 / tr.rolling(window=14, min_periods=14).sum()
dx = abs(plus_di - minus_di) / (plus_di + minus_di) * 100
adx = dx.rolling(window=6, min_periods=6).mean()
adxr = (adx + adx.shift(6)) / 2
return adxr
def alpha187(self):
"""
SUM(OPEN<=DELAY(OPEN,1)?0:MAX(HIGH-OPEN,OPEN-DELAY(OPEN,1)),20)
"""
part1 = np.maximum(self.H - self.O, self.O.diff())
part1[self.O.diff() <= 0] = 0.0
return part1.rolling(window=20, min_periods=20).sum()
def alpha188(self):
"""
((HIGH-LOW-SMA(HIGH-LOW,11,2))/SMA(HIGH-LOW,11,2))*100
"""
hl = self.H - self.L
sma = self._sma(hl, 11, 2)
return (hl - sma) / sma * 100
def alpha189(self):
"""
MEAN(ABS(CLOSE-MEAN(CLOSE,6)),6)
"""
ma = self.C.rolling(window=6, min_periods=6).mean()
return abs(self.C - ma).rolling(window=6, min_periods=6).mean()
def alpha190(self):
"""
LOG((COUNT(RET>((CLOSE/DELAY(CLOSE,19))^(1/20)-1),20)-1)
*SUMIF((RET-(CLOSE/DELAY(CLOSE,19))^(1/20)-1)^2,20,RET<(CLOSE/DELAY(CLOSE,19))^(1/20)-1)
/(COUNT(RET<(CLOSE/DELAY(CLOSE,19))^(1/20)-1,20)
*SUMIF((RET-((CLOSE/DELAY(CLOSE,19))^(1/20)-1))^2,20,RET>(CLOSE/DELAY(CLOSE,19))^(1/20)-1)))
"""
ret = self.C.pct_change()
ret_19 = (self.C / self.C.shift(19)) ** 0.05 - 1.0
part1 = (ret > ret_19).rolling(window=20, min_periods=20).sum() - 1.0
part2 = (np.minimum(ret - ret_19, 0.0) ** 2).rolling(window=20, min_periods=20).sum()
part3 = (ret < ret_19).rolling(window=20, min_periods=20).sum()
part4 = (np.maximum(ret - ret_19, 0.0) ** 2).rolling(window=20, min_periods=20).sum()
return np.log(part1 * part2 / part3 / part4)
def alpha191(self):
"""
CORR(MEAN(VOLUME,20),LOW,5)+(HIGH+LOW)/2-CLOSE
"""
part1 = self.V.rolling(window=20, min_periods=20).mean().rolling(window=5, min_periods=5).corr(self.L)
return part1 + (self.H + self.L) / 2 - self.C
if name == 'main': api = xg_factor() result = api.CROSS_DOWN() # print(result)
全部因子的底层函数
''' 小果 微信:xg_quant ''' import pandas as pd import numpy as np #------------------ 0级：核心工具函数 ------------------------------------------- import numpy as np import pandas as pd
def RD(N, D=3): """四舍五入取3位小数""" return np.round(N, D)
def RET(S, N=1): """返回序列倒数第N个值，默认返回最后一个""" return np.array(S)[-N]
def ABS(S): """返回N的绝对值""" return np.abs(S)
def MAX(S1, S2): """序列max""" return np.maximum(S1, S2)
def MIN(S1, S2): """序列min""" return np.minimum(S1, S2)
def IF(S, A, B): """序列布尔判断 return=A if S==True else B""" return np.where(S, A, B)
def REF(S, N=1): """对序列整体下移动N，返回序列(shift后会产生NAN)""" return pd.Series(S).shift(N).values
def DIFF(S, N=1): """前一个值减后一个值，前面会产生nan；np.diff(S)直接删除nan，会少一行""" return pd.Series(S).diff(N).values
def STD(S, N): """求序列的N日标准差，返回序列""" return pd.Series(S).rolling(N).std(ddof=0).values
def SUM(S, N): """对序列求N天累计和，返回序列；N=0对序列所有依次求和""" return pd.Series(S).rolling(N).sum().values if N > 0 else pd.Series(S).cumsum().values
def CONST(S): """返回序列S最后的值组成常量序列""" return np.full(len(S), S[-1])
def AND(S1, S2): """逻辑与运算""" return np.logical_and(S1, S2)
def OR(S1, S2): """逻辑或运算""" return np.logical_or(S1, S2)
def NOT(S1): """逻辑非运算""" return np.logical_not(S1)
def RANGE(A, B, C): """期间函数：B <= A <= C""" df = pd.DataFrame() df['select'] = A.tolist() df['select'] = df['select'].apply(lambda x: True if (x >= B and x <= C) else False) return df['select']
def HHV(S, N): """HHV(C, 5) 最近5天收盘最高价""" return pd.Series(S).rolling(N).max().values
def LLV(S, N): """LLV(C, 5) 最近5天收盘最低价""" return pd.Series(S).rolling(N).min().values
def HHVBARS(S, N): """求N周期内S最高值到当前周期数，返回序列""" return pd.Series(S).rolling(N).apply(lambda x: np.argmax(x[::-1]), raw=True).values
def LLVBARS(S, N): """求N周期内S最低值到当前周期数，返回序列""" return pd.Series(S).rolling(N).apply(lambda x: np.argmin(x[::-1]), raw=True).values
def MA(S, N): """求序列的N日简单移动平均值，返回序列""" return pd.Series(S).rolling(N).mean().values
def EMA(S, N): """指数移动平均，为了精度 S>4*N，EMA至少需要120周期；alpha=2/(span+1)""" return pd.Series(S).ewm(span=N, adjust=False).mean().values
def SMA(S, N, M=1): """中国式的SMA，至少需要120周期才精确(雪球180周期)；alpha=1/(1+com)""" return pd.Series(S).ewm(alpha=M/N, adjust=False).mean().values # com=N-M/M
def DMA(S, A): """求S的动态移动平均，A作平滑因子，必须 0<A<1 (此为核心函数，非指标)""" return pd.Series(S).ewm(alpha=A, adjust=True).mean().values
def WMA(S, N): """通达信S序列的N日加权移动平均 Yn = (1X1+2X2+3X3+...+nXn)/(1+2+3+...+Xn)""" return pd.Series(S).rolling(N).apply(lambda x: x[::-1].cumsum().sum() * 2 / N / (N + 1), raw=True).values
def AVEDEV(S, N): """平均绝对偏差 (序列与其平均值的绝对差的平均值)""" return pd.Series(S).rolling(N).apply(lambda x: (np.abs(x - x.mean())).mean()).values
def SLOPE(S, N): """返回S序列N周期回线性回归斜率""" return pd.Series(S).rolling(N).apply(lambda x: np.polyfit(range(N), x, deg=1)[0], raw=True).values
def FORCAST(S, N): """返回S序列N周期回线性回归后的预测值""" return pd.Series(S).rolling(N).apply(lambda x: np.polyval(np.polyfit(range(N), x, deg=1), N-1), raw=True).values
def LAST(S, A, B): """从前A日到前B日一直满足S_BOOL条件，要求A>B & A>0 & B>=0""" return np.array(pd.Series(S).rolling(A+1).apply(lambda x: np.all(x[::-1][B:]), raw=True), dtype=bool)
#------------------ 1级：应用层函数(通过0级核心函数实现）-------------------------- def COUNT(S, N): """COUNT(CLOSE>O, N): 最近N天满足S_BOO的天数，True的天数""" return SUM(S, N)
def EVERY(S, N): """EVERY(CLOSE>O, 5) 最近N天是否都是True""" return IF(SUM(S, N) == N, True, False)
def EXIST(S, N): """EXIST(CLOSE>3010, N=5) n日内是否存在一天大于3000点""" return IF(SUM(S, N) > 0, True, False)
def FILTER(S, N): """ FILTER函数，S满足条件后，将其后N周期内的数据置为0 例：FILTER(C==H,5) 涨停后，后5天不再发出信号 """ for i in range(len(S)): if S[i]: S[i+1:i+1+N] = 0 return S
def BARSLAST(S): """上一次条件成立到当前的周期，BARSLAST(C/REF(C,1)>=1.1) 上一次涨停到今天的天数""" M = np.concatenate(([0], np.where(S, 1, 0))) for i in range(1, len(M)): M[i] = 0 if M[i] else M[i-1] + 1 return M[1:]
def BARSLASTCOUNT(S): """统计连续满足S条件的周期数；BARSLASTCOUNT(CLOSE>OPEN)表示统计连续收阳的周期数""" rt = np.zeros(len(S) + 1) for i in range(len(S)): rt[i+1] = rt[i] + 1 if S[i] else rt[i+1] return rt[1:]
def BARSSINCEN(S, N): """N周期内第一次S条件成立到现在的周期数，N为常量""" return pd.Series(S).rolling(N).apply( lambda x: N-1-np.argmax(x) if np.argmax(x) or x[0] else 0, raw=True ).fillna(0).values.astype(int)
def CROSS(S1, S2): """判断向上金叉穿越 CROSS(MA(C,5), MA(C,10))；判断向下死叉穿越 CROSS(MA(C,10), MA(C,5))""" return np.concatenate(([False], np.logical_not((S1 > S2)[:-1]) & (S1 > S2)[1:]))
def CROSS_UP(S1, S2): """判断向上金叉穿越 CROSS(MA(C,5), MA(C,10))""" return np.concatenate(([False], np.logical_not((S1 > S2)[:-1]) & (S1 > S2)[1:]))
def CROSS_DOWN(S1, S2): """判断向下死叉穿越 CROSS(MA(C,5), MA(C,10))""" return np.concatenate(([False], np.logical_not((S1 < S2)[:-1]) & (S1 < S2)[1:]))
def LONGCROSS(S1, S2, N): """两条线维持一定周期后交叉，S1在N周期内都小于S2，本周期从S1下方向上穿过S2时返回1，否则返回0；N=1时等同于CROSS(S1, S2)""" return np.array(np.logical_and(LAST(S1 < S2, N, 1), (S1 > S2)), dtype=bool)
def VALUEWHEN(S, X): """当S条件成立时，取X的当前值，否则取VALUEWHEN的上个成立时的X值""" return pd.Series(np.where(S, X, np.nan)).ffill().values
#------------------ 扩展函数（来自第二个文件）------------------------------------- def BACKSET(X, N): """ 属于未来函数，将当前位置到若干周期前的数据设为1。 用法：BACKSET(X,N)，若X非0，则将当前位置到N周期前的数值设为1。 例如：BACKSET(CLOSE>OPEN,2) 若收阳则将该周期及前一周期数值设为1，否则为0 """ result = np.zeros_like(X) for i in range(len(X)): if X[i] != 0: start_index = max(0, i - N + 1) result[start_index:i+1] = 1 return result
def ALIGNRIGHT(X): """ 有效数据右对齐。 用法：ALIGNRIGHT(X) 有效数据向右移动，左边空出来的周期填充无效值 例如：TC:=IF(CURRBARSCOUNT=2 || CURRBARSCOUNT=5, DRAWNULL, C); XC:=ALIGNRIGHT(TC); 删除了两天的收盘价，并将剩余数据右移 """ valid_indices = np.where(X != np.nan)[0] invalid_count = len(X) - len(valid_indices) result = np.empty_like(X) result[:] = np.nan result[invalid_count:len(valid_indices)+invalid_count] = X[valid_indices] return result
def BARSCOUNT(X): """ 有效数据周期数。 用法：BARSCOUNT(X) 第一个有效数据到当前的间隔周期数 注意：判断范围为指标或条件选股计算时公式使用的数据， 如果给画线指标的数据少(比如没有按下箭头取更多K线)或给条件选股给的数据少，这个有效值也可能少 """ valid_indices = np.where(~np.isnan(X))[0] if len(valid_indices) == 0: return 0 first_valid_index = valid_indices[0] current_index = len(X) - 1 bars_count = current_index - first_valid_index + 1 return bars_count
def BARSLASTS(X, N): """ 倒数第N次成立时距今的周期数。 用法：BARSLASTS(X,N): X倒数第N满足到现在的周期数，N支持变量 """ valid_indices = np.where(~np.isnan(X))[0] if len(valid_indices) == 0: return -1 last_n_indices = valid_indices[-N:] if len(last_n_indices) < N: return -1 current_index = len(X) - 1 bars_since_last_n = current_index - last_n_indices[-1] + 1 return bars_since_last_n
def ZIG(CLOSE, X=0.05): """ 未来函数，计算之字转向。 用法：ZIG(CLOSE, 0.05) 5%之字转向 """ ZIG_STATE_START = 0 ZIG_STATE_RISE = 1 ZIG_STATE_FALL = 2 x = X k = CLOSE peer_i = 0 candidate_i = None scan_i = 0 peers = [0] z = np.zeros(len(k)) state = ZIG_STATE_START while True: scan_i += 1 if scan_i == len(k) - 1: if candidate_i is None: peer_i = scan_i peers.append(peer_i) else: if state == ZIG_STATE_RISE: if k[scan_i] >= k[candidate_i]: peer_i = scan_i peers.append(peer_i) else: peer_i = candidate_i peers.append(peer_i) peer_i = scan_i peers.append(peer_i) elif state == ZIG_STATE_FALL: if k[scan_i] <= k[candidate_i]: peer_i = scan_i peers.append(peer_i) else: peer_i = candidate_i peers.append(peer_i) peer_i = scan_i peers.append(peer_i) break if state == ZIG_STATE_START: if k[scan_i] >= k[peer_i] * (1 + x): candidate_i = scan_i state = ZIG_STATE_RISE elif k[scan_i] <= k[peer_i] * (1 - x): candidate_i = scan_i state = ZIG_STATE_FALL elif state == ZIG_STATE_RISE: if k[scan_i] >= k[candidate_i]: candidate_i = scan_i elif k[scan_i] <= k[candidate_i] * (1 - x): peer_i = candidate_i peers.append(peer_i) state = ZIG_STATE_FALL candidate_i = scan_i elif state == ZIG_STATE_FALL: if k[scan_i] <= k[candidate_i]: candidate_i = scan_i elif k[scan_i] >= k[candidate_i] * (1 + x): peer_i = candidate_i peers.append(peer_i) state = ZIG_STATE_RISE candidate_i = scan_i for i in range(len(peers) - 1): peer_start_i = peers[i] peer_end_i = peers[i + 1] start_value = k[peer_start_i] end_value = k[peer_end_i] a = (end_value - start_value) / (peer_end_i - peer_start_i) for j in range(peer_end_i - peer_start_i + 1): z[j + peer_start_i] = start_value + a * j return pd.Series(z) def calculate_zigzag(data, percent): """ 计算ZigZag指标。
参数:
data : pandas.DataFrame
包含价格数据的DataFrame，必须包含'High'和'Low'列。
percent : float
百分比阈值，用于确定局部高点和低点。
返回:
zigzag : pandas.Series
ZigZag指标值。
"""
# 初始化ZigZag序列
zigzag = pd.Series(index=data.index)
# 初始方向为向上
direction = 'up'
# 遍历数据
for i in range(1, len(data)):
if direction == 'up':
if data['high'][i] >= data['high'][i-1] * (1 + percent / 100):
zigzag[i] = data['high'][i]
direction = 'down'
elif data['low'][i] <= data['low'][i-1] * (1 - percent / 100):
zigzag[i] = data['low'][i]
direction = 'down'
else:
zigzag[i] = zigzag[i-1]
else:
if data['low'][i] <= data['low'][i-1] * (1 - percent / 100):
zigzag[i] = data['low'][i]
direction = 'up'
elif data['high'][i] >= data['high'][i-1] * (1 + percent / 100):
zigzag[i] = data['high'][i]
direction = 'up'
else:
zigzag[i] = zigzag[i-1]
return zigzag
def TROUGHBARS(data, K, N, M): """ 计算前M个ZIG转向波谷到当前的周期数。
参数:
data : pandas.DataFrame
包含价格数据的DataFrame，必须包含'High'和'Low'列。
K : int
百分比阈值，用于计算ZigZag指标。
N : int
未使用的参数，保留以符合函数签名。
M : int
前M个波谷的数量。
返回:
result : pandas.Series
每个周期的前M个波谷到当前的周期数。
"""
# 计算ZigZag指标
zigzag = calculate_zigzag(data, K)
# 找到波谷的位置
valleys = zigzag[zigzag.notna() & (zigzag.shift(1) > zigzag)].index
# 计算每个周期的前M个波谷到当前的周期数
result = pd.Series(index=data.index)
for i in range(len(data)):
if i < len(valleys):
result[i] = np.nan
else:
distances = [i - v for v in valleys[-M:]]
result[i] = min(distances)
return result
#df,DATE,CLOSE,OPEN,LOW,HIGH,VOL,CAPITAL,HSL,AMOUNT=set_start_data()
def params_data(test='test.txt',to_path='result.txt'):
'''
解析通达信公式
test原来通达信公式文件
to_path结果文件，python可以直接运行的文件
'''
test=open(r'{}'.format(test),'r',encoding='utf-8')
result=test.readlines()
columns=[]
#挑选需要返回的数据
for i in result:
if ':' in i and ':=' not in i:
name_list=i.split(':')
columns.append(name_list[0])
text=''.join(result)
text1=text.replace(':=','=')
text2=text1.replace(':','=')
text4=text2.replace('&&',' and ')
text5=text4.replace('||','or')
text6=text5.replace('AND','and')
text7=text6.replace('OR','or')
text8=text7.replace('NOT','not')
text9=text8.replace('DRAWNULL','None')
text10=text9.replace(',NODRAW','')
text11=text10.replace('MF0>MF1 and MF0>MF2','np.logical_and(MF0>MF1,MF0>MF2)')
text12=text11.replace('MF0<MF1 and MF0<MF2','np.logical_and(MF0<MF1,MF0<MF2)')
text3=text12.split(';')
del text3[-1]
fill=open(r'{}'.format(to_path),'w+',encoding='utf-8')
fill.truncate()
for i in text3:
try:
m=i.split('=')
var=m[0]
result=m[1]
fill.write(var +'='+result)
except:
fill.write(var +'='+result)
fill.write('\n')
fill.write('return {}'.format(','.join(columns)))
fill.close()
print('公式分析成功')
def data_to_pandas(func=''):
'''
将函数的计算结果数据变成pandas数据,需要自动补充列名称
func计算公式，例子data_to_pandas(CCI(CLOSE,HIGH,LOW)),CCI函数，也可以计算在返回
print(data_to_pandas(CCI(CLOSE,HIGH,LOW)))
0
300 NaN
301 NaN
302 NaN
303 NaN
304 NaN
... ...
4634 10.314220
4635 68.462799
4636 106.677513
4637 116.201078
4638 85.026126
'''
df=pd.DataFrame(func)
#自己补充列明，列名称就是返回的参数
columns=[]
#df.columns=columns
df1=df.T
return df1
def CCI(CLOSE,HIGH,LOW,N=14):
'''
超卖超买类
CCI商品路劲指标
TYP赋值:(最高价+最低价+收盘价)/3
输出CCI:(TYP-TYP的N日简单移动平均)1000/(15TYP的N日平均绝对偏差)
'''
TYP=(HIGH+LOW+CLOSE)/3
result=(TYP-MA(TYP,N))1000/(15AVEDEV(TYP,N))
return result
def KDJ(CLOSE,HIGH,LOW, N=9,M1=3,M2=3):
'''
超卖超买类
RSV赋值:(收盘价-N日内最低价的最低值)/(N日内最高价的最高值-N日内最低价的最低值)100
输出K:RSV的M1日[1日权重]移动平均
输出D:K的M2日[1日权重]移动平均
输出J:3K-2D
'''
RSV=(CLOSE-LLV(LOW,N))/(HHV(HIGH,N)-LLV(LOW,N))100
K=SMA(RSV,M1,1)
D=SMA(K,M2,1)
J=3K-2D
return K,D,J
def MFI(CLOSE,HIGH,LOW,VOL,N=14):
'''
最近流量指标
超卖超买类
赋值: (最高价 + 最低价 + 收盘价)/3
V1赋值:如果TYP>1日前的TYP,返回TYP成交量(手),否则返回0的N日累和/如果TYP<1日前的TYP,返回TYP成交量(手),否则返回0的N日累和
输出资金流量指标:100-(100/(1+V1))
'''
TYP = (HIGH + LOW + CLOSE)/3
V1=SUM(IF(TYP>REF(TYP,1),TYPVOL,0),N)/SUM(IF(TYP<REF(TYP,1),TYPVOL,0),N)
return 100-(100/(1+V1))
def MTM(CLOSE,N=12,M=6):
'''
动量线指标
超卖超买类
输出动量线:收盘价-收盘价的有效数据周期数和N的较小值日前的收盘价
输出MTMMA:MTM的M日简单移动平均
'''
MTM=CLOSE-REF(CLOSE,N)
MTMMA=MA(MTM,M)
return MTM,MTMMA
def EXPMEMA(data,N=20):
'''
data pandas.Series数据
超卖超买类
指数平滑移动平均
'''
result=data.ewm(com=N).mean()
return result
def BARSCOUNT(CLOSE): df=pd.DataFrame() df['数据']=range(len(CLOSE)) return df['数据']
def RSI(CLOSE, N1=6,N2=12,N3=24):
'''
相对强弱指标
LC赋值:1日前的收盘价
输出RSI1:收盘价-LC和0的较大值的N1日[1日权重]移动平均/收盘价-LC的绝对值的N1日[1日权重]移动平均100
输出RSI2:收盘价-LC和0的较大值的N2日[1日权重]移动平均/收盘价-LC的绝对值的N2日[1日权重]移动平均100
输出RSI3:收盘价-LC和0的较大值的N3日[1日权重]移动平均/收盘价-LC的绝对值的N3日[1日权重]移动平均100
'''
LC=REF(CLOSE,1)
RSI1=SMA(MAX(CLOSE-LC,0),N1,1)/SMA(ABS(CLOSE-LC),N1,1)100
RSI2=SMA(MAX(CLOSE-LC,0),N2,1)/SMA(ABS(CLOSE-LC),N2,1)100
RSI3=SMA(MAX(CLOSE-LC,0),N3,1)/SMA(ABS(CLOSE-LC),N3,1)100
return RSI1,RSI2,RSI3
def KD(CLOSE,LOW,HIGH,N=9,M1=3,M2=3):
'''
相对强弱指标
RSV赋值:(收盘价-N日内最低价的最低值)/(N日内最高价的最高值-N日内最低价的最低值)100
输出K:RSV的M1日[1日权重]移动平均
输出D:K的M2日[1日权重]移动平均
'''
RSV=(CLOSE-LLV(LOW,N))/(HHV(HIGH,N)-LLV(LOW,N))100
K=SMA(RSV,M1,1)
D=SMA(K,M2,1)
return K,D
def SKDJ(CLOSE,LOW,HIGH,N=9,M=3):
'''
慢速随机指标
LOWV赋值:N日内最低价的最低值
HIGHV赋值:N日内最高价的最高值
RSV赋值:(收盘价-LOWV)/(HIGHV-LOWV)100的M日指数移动平均
输出K:RSV的M日指数移动平均
输出D:K的M日简单移动平均
'''
LOWV=LLV(LOW,N)
HIGHV=HHV(HIGH,N)
RSV=EMA((CLOSE-LOWV)/(HIGHV-LOWV)100,M)
K=EMA(RSV,M)
D=MA(K,M)
return K,D
def UDL(CLOSE,N1=3,N2=5,N3=10,N4=20,M=6):
'''
引力线
输出引力线:(收盘价的N1日简单移动平均+收盘价的N2日简单移动平均+收盘价的N3日简单移动平均+收盘价的N4日简单移动平均)/4
输出MAUDL:UDL的M日简单移动平均
'''
UDL=(MA(CLOSE,N1)+MA(CLOSE,N2)+MA(CLOSE,N3)+MA(CLOSE,N4))/4
MAUDL=MA(UDL,M)
return UDL,MAUDL
def WR(CLOSE,LOW,HIGH,N=10,N1=6):
'''
威廉指标
输出WR1:100(N日内最高价的最高值-收盘价)/(N日内最高价的最高值-N日内最低价的最低值)
输出WR2:100(N1日内最高价的最高值-收盘价)/(N1日内最高价的最高值-N1日内最低价的最低值)
'''
WR1=100(HHV(HIGH,N)-CLOSE)/(HHV(HIGH,N)-LLV(LOW,N))
WR2=100(HHV(HIGH,N1)-CLOSE)/(HHV(HIGH,N1)-LLV(LOW,N1))
return WR1,WR2
def LWR(CLOSE,LOW,HIGH,N=9,M1=3,M2=3):
'''
LWR指标
RSV赋值: (N日内最高价的最高值-收盘价)/(N日内最高价的最高值-N日内最低价的最低值)100
输出LWR1:RSV的M1日[1日权重]移动平均
输出LWR2:LWR1的M2日[1日权重]移动平均
'''
RSV= (HHV(HIGH,N)-CLOSE)/(HHV(HIGH,N)-LLV(LOW,N))100
LWR1=SMA(RSV,M1,1)
LWR2=SMA(LWR1,M2,1)
return LWR1,LWR2
def MEMA(S,N,M=1):
'''
平滑移动平均
'''
return SMA(S,N,M)
def MARSI(CLOSE,M1=10,M2=6):
'''
相对强弱平均线
DIF赋值:收盘价-1日前的收盘价
VU赋值:如果DIF>=0,返回DIF,否则返回0
VD赋值:如果DIF<0,返回-DIF,否则返回0
MAU1赋值:VU的M1日平滑移动平均
MAD1赋值:VD的M1日平滑移动平均
MAU2赋值:VU的M2日平滑移动平均
'''
DIF=CLOSE-REF(CLOSE,1)
VU=IF(DIF>=0,DIF,0)
VD=IF(DIF<0,-DIF,0)
MAU1=MEMA(VU,M1)
MAD1=MEMA(VD,M1)
MAU2=MEMA(VU,M2)
MAD2=MEMA(VD,M2)
RSI1=MA(100MAU1/(MAU1+MAD1),M1)
RSI2=MA(100MAU2/(MAU2+MAD2),M2)
return RSI1,RSI2
def BIAS_QL(CLOSE,N=6,M=6):
'''
乖离率-传统版
输出乖离率 :(收盘价-收盘价的N日简单移动平均)/收盘价的N日简单移动平均100
输出BIASMA :乖离率的M日简单移动平均
'''
BIAS=(CLOSE-MA(CLOSE,N))/MA(CLOSE,N)100
BIASMA=MA(BIAS,M)
return BIAS,BIASMA
def BIAS(CLOSE,N1=6,N2=12,N3=24):
'''
乖离率
输出BIAS1 :(收盘价-收盘价的N1日简单移动平均)/收盘价的N1日简单移动平均100
输出BIAS2 :(收盘价-收盘价的N2日简单移动平均)/收盘价的N2日简单移动平均100
输出BIAS3 :(收盘价-收盘价的N3日简单移动平均)/收盘价的N3日简单移动平均100
'''
BIAS1=(CLOSE-MA(CLOSE,N1))/MA(CLOSE,N1)100
BIAS2=(CLOSE-MA(CLOSE,N2))/MA(CLOSE,N2)100
BIAS3=(CLOSE-MA(CLOSE,N3))/MA(CLOSE,N3)100
return BIAS1,BIAS2,BIAS3
def BIAS36(CLOSE,M=6):
'''
三六乖离
输出三六乖离:收盘价的3日简单移动平均-收盘价的6日简单移动平均
输出BIAS612:收盘价的6日简单移动平均-收盘价的12日简单移动平均
输出MABIAS:BIAS36的M日简单移动平均
'''
BIAS36=MA(CLOSE,3)-MA(CLOSE,6)
BIAS612=MA(CLOSE,6)-MA(CLOSE,12)
MABIAS=MA(BIAS36,M)
return BIAS36,BIAS612,MABIAS
def ACCER(CLOSE,N=8):
'''
幅度涨速
输出幅度涨速:收盘价的N日线性回归斜率/收盘价
'''
ACCER=SLOPE(CLOSE,N)/CLOSE
return ACCER
#需要编写活力函数
def CYD(CLOSE,CAPITAL,N=21):
'''
承接因子
输出CYDS:以收盘价计算的获利盘比例/(成交量(手)/当前流通股本(手))
输出CYDN:以收盘价计算的获利盘比例/成交量(手)/当前流通股本(手)的N日简单移动平均
'''
CYDS=WINNER(CLOSE)/(VOL/CAPITAL)
CYDN=WINNER(CLOSE)/MA(VOL/CAPITAL,N);
return CYDS,CYDN
def CYF(HSL,N=21):
'''
市场能量
输出市场能量:100-100/(1+换手线的N日指数移动平均)
'''
CYF=100-100/(1+EMA(HSL,N))
return CYF
def SFL(CLOSE,VOL):
'''
分水岭
输出SWL:(收盘价的5日指数移动平均7+收盘价的10日指数移动平均3)/10
输出SWS:以1和100(成交量(手)的5日累和/(3当前流通股本(手)))的较大值为权重收盘价的12日指数移动平均的动态移动平均
'''
SWL=(EMA(CLOSE,5)7+EMA(CLOSE,10)3)/10
IF(100(SUM(VOL,5)/(3CAPITAL)>1),100*(SUM(VOL,5)/(3CAPITAL)),1)
SWS=DMA(EMA(CLOSE,12),MAX(1,1))
return SWL,SWS
def ATR(CLOSE,HIGH,LOW,N=14):
'''
真实波幅
输出MTR:(最高价-最低价)和1日前的收盘价-最高价的绝对值的较大值和1日前的收盘价-最低价的绝对值的较大值
输出真实波幅:MTR的N日简单移动平均
'''
MTR=MAX(MAX((HIGH-LOW),ABS(REF(CLOSE,1)-HIGH)),ABS(REF(CLOSE,1)-LOW))
ATR=MA(MTR,N)
return MTR,ATR
def DKX(CLOSE,LOW,OPEN,HIGH,M=10):
'''
多空线
MID赋值:(3收盘价+最低价+开盘价+最高价)/6
输出多空线:(20MID+191日前的MID+182日前的MID+173日前的MID+164日前的MID+155日前的MID+146日前的MID+137日前的MID+128日前的MID+119日前的MID+1010日前的MID+911日前的MID+812日前的MID+713日前的MID+614日前的MID+515日前的MID+416日前的MID+317日前的MID+218日前的MID+20日前的MID)/210
输出MADKX:DKX的M日简单移动平均
'''
MID=(3CLOSE+LOW+OPEN+HIGH)/6
DKX=(20MID+19REF(MID,1)+18REF(MID,2)+17REF(MID,3)+
16REF(MID,4)+15REF(MID,5)+14REF(MID,6)+
13REF(MID,7)+12REF(MID,8)+11REF(MID,9)+
10REF(MID,10)+9REF(MID,11)+8REF(MID,12)+
7REF(MID,13)+6REF(MID,14)+5REF(MID,15)+
4REF(MID,16)+3REF(MID,17)+2REF(MID,18)+REF(MID,20))/210
MADKX=MA(DKX,M)
return DKX,MADKX
#******************************************
#******************************************
#趋势类型
def ASI(OPEN,CLOSE,HIGH,LOW,M1=26,M2=10):
'''
振动升降指标
'''
LC=REF(CLOSE,1)
AA=ABS(HIGH-LC)
BB=ABS(LOW-LC)
CC=ABS(HIGH-REF(LOW,1))
DD=ABS(LC-REF(OPEN,1))
R=IF( (AA>BB) & (AA>CC),AA+BB/2+DD/4,IF( (BB>CC) & (BB>AA),BB+AA/2+DD/4,CC+DD/4))
X=(CLOSE-LC+(CLOSE-OPEN)/2+LC-REF(OPEN,1))
SI=16X/RMAX(AA,BB)
ASI=SUM(SI,M1)
ASIT=MA(ASI,M2)
return ASI,ASIT
def CHO(CLOSE,OPEN,LOW,HIGH,VOL,N1=10,N2=20,M=6):
'''
佳庆指标
MID赋值:成交量(手)(2收盘价-最高价-最低价)/(最高价+最低价)的历史累和
输出佳庆指标:MID的N1日简单移动平均-MID的N2日简单移动平均
输出MACHO:CHO的M日简单移动平均
'''
MID=SUM(VOL*(2CLOSE-HIGH-LOW)/(HIGH+LOW),0)
CHO=MA(MID,N1)-MA(MID,N2)
MACHO=MA(CHO,M)
return CHO,MACHO
def DMA_XT(CLOSE,N1=10,N2=50,M=10):
'''
平均差
输出DIF:收盘价的N1日简单移动平均-收盘价的N2日简单移动平均
输出DIFMA:DIF的M日简单移动平均
'''
DIF=MA(CLOSE,N1)-MA(CLOSE,N2)
DIFMA=MA(DIF,M)
return DIF,DIFMA
def DMI(CLOSE,HIGH,LOW,N=14,M=6):
'''
趋向指标
MTR赋值:最高价-最低价和最高价-1日前的收盘价的绝对值的较大值和1日前的收盘价-最低价的绝对值的较大值的N日累和
赋值:最高价-1日前的最高价
赋值:1日前的最低价-最低价
DMP赋值:如果HD>0并且HD>LD,返回HD,否则返回0的N日累和
DMM赋值:如果LD>0并且LD>HD,返回LD,否则返回0的N日累和
输出PDI: DMP100/MTR
输出MDI: DMM100/MTR
输出ADX: MDI-PDI的绝对值/(MDI+PDI)100的M日简单移动平均
输出ADXR:(ADX+M日前的ADX)/2
'''
MTR=SUM(MAX(MAX(HIGH-LOW,ABS(HIGH-REF(CLOSE,1))),ABS(REF(CLOSE,1)-LOW)),N)
HD =HIGH-REF(HIGH,1)
LD =REF(LOW,1)-LOW
list_A=[]
list_B=[]
for m,n in zip(LD>0,LD>HD):
if m==n and m==True:
list_A.append(True)
else:
list_A.append(False)
for i,j in zip(LD>0,LD>HD):
if i==j and i==True:
list_B.append(True)
else:
list_B.append(False)
DMP=SUM(IF(list_A,HD,0),N)
DMM=SUM(IF(list_B,LD,0),N)
PDI= DMP100/MTR
MDI=DMM100/MTR
ADX=MA(ABS(MDI-PDI)/(MDI+PDI)100,M)
ADXR=(ADX+REF(ADX,M))/2
return PDI,MDI,ADX,ADXR
def DPO(CLOSE,N=21,M=6):
'''
区间震荡线
输出区间震荡线:收盘价-N/2+1日前的收盘价的N日简单移动平均
输出MADPO:DPO的M日简单移动平均
'''
#print(REF(MA(CLOSE,N),N/2))
DPO=CLOSE-REF(MA(CLOSE,7),6)
MADPO=MA(DPO,M)
return DPO,MADPO
def EMV(HIGH,LOW,VOL,N=14,M=9):
'''
简易波动指标
VOLUME赋值:成交量(手)的N日简单移动平均/成交量(手)
MID赋值:100(最高价+最低价-1日前的最高价+最低价)/(最高价+最低价)
输出EMV:MIDVOLUME(最高价-最低价)/最高价-最低价的N日简单移动平均的N日简单移动平均
输出MAEMV:EMV的M日简单移动平均
'''
VOLUME=MA(VOL,N)/VOL
MID=100*(HIGH+LOW-REF(HIGH+LOW,1))/(HIGH+LOW)
EMV=MA(MIDVOLUME(HIGH-LOW)/MA(HIGH-LOW,N),N)
MAEMV=MA(EMV,M)
return EMV,MAEMV
def MACD(CLOSE,SHORT=12,LONG=26,MID=9):
'''
平滑异同平均线
输出DIF:收盘价的SHORT日指数移动平均-收盘价的LONG日指数移动平均
输出DEA:DIF的MID日指数移动平均
输出平滑异同平均线:(DIF-DEA)*2,COLORSTICK
'''
DIF=EMA(CLOSE,SHORT)-EMA(CLOSE,LONG)
DEA=EMA(DIF,MID)
MACD=(DIF-DEA)*2
return DIF,DEA,MACD
def VMACD(VOL,SHORT=12,LONG=26,MID=9): ''' 量平滑异同平均线 输出DIF:成交量(手)的SHORT日指数移动平均-成交量(手)的LONG日指数移动平均 输出DEA:DIF的MID日指数移动平均 输出平滑异同平均线:DIF-DEA,COLORSTICK ''' DIF=EMA(VOL,SHORT)-EMA(VOL,LONG) DEA=EMA(DIF,MID) MACD=DIF-DEA return DIF,DEA,MACD def SMACD(CLOSE,SHORT=12,LONG=26,MID=9): ''' 单线平滑异同平均线 DIF赋值:收盘价的SHORT日指数移动平均-收盘价的LONG日指数移动平均 输出DEA:DIF的MID日指数移动平均 输出平滑异同平均线:DIF,COLORSTICK ''' DIF=EMA(CLOSE,SHORT)-EMA(CLOSE,LONG) DEA=EMA(DIF,MID) MACD=DIF return DEA,MACD def QACD(CLOSE,N1=12,N2=12,M=9): ''' 快速异同平均线 输出DIF:收盘价的N1日指数移动平均-收盘价的N2日指数移动平均 输出平滑异同平均线:DIF的M日指数移动平均 输出DDIF:DIF-MACD ''' DIF=EMA(CLOSE,N1)-EMA(CLOSE,N2) MACD=EMA(DIF,M) DDIF=DIF-MACD return DIF,MACD,DDIF def TRIX(CLOSE,N=12,M=9): ''' 三重指数平均线 MTR赋值:收盘价的N日指数移动平均的N日指数移动平均的N日指数移动平均 输出三重指数平均线:(MTR-1日前的MTR)/1日前的MTR100 输出MATRIX:TRIX的M日简单移动平均 ''' MTR=EMA(EMA(EMA(CLOSE,N),N),N) TRIX=(MTR-REF(MTR,1))/REF(MTR,1)100 MATRIX=MA(TRIX,M) return TRIX,MATRIX def UOS(CLOSE,HIGH,LOW,N1=7,N2=14,N3=28,M=6): ''' 终极指标 TH赋值:最高价和1日前的收盘价的较大值 TL赋值:最低价和1日前的收盘价的较小值 ACC1赋值:收盘价-TL的N1日累和/TH-TL的N1日累和 ACC2赋值:收盘价-TL的N2日累和/TH-TL的N2日累和 ACC3赋值:收盘价-TL的N3日累和/TH-TL的N3日累和 输出终极指标:(ACC1N2N3+ACC2N1N3+ACC3N1N2)100/(N1N2+N1N3+N2N3) 输出MAUOS:UOS的M日指数平滑移动平均 ''' TH=MAX(HIGH,REF(CLOSE,1)) TL=MIN(LOW,REF(CLOSE,1)) ACC1=SUM(CLOSE-TL,N1)/SUM(TH-TL,N1) ACC2=SUM(CLOSE-TL,N2)/SUM(TH-TL,N2) ACC3=SUM(CLOSE-TL,N3)/SUM(TH-TL,N3) UOS=(ACC1N2N3+ACC2N1N3+ACC3N1N2)100/(N1N2+N1N3+N2N3) MAUOS=EXPMEMA(pd.Series(UOS),M) return UOS,np.array(MAUOS) def VTP(CLOSE,VOL,N=51,M=6): ''' 量价曲线 输出量价曲线:成交量(手)(收盘价-1日前的收盘价)/1日前的收盘价的N日累和 输出MAVPT:VPT的M日简单移动平均 ''' VPT=SUM(VOL(CLOSE-REF(CLOSE,1))/REF(CLOSE,1),N) MAVP=MA(VPT,M) return VPT,MAVP def WVAD(CLOSE,OPEN,HIGH,LOW,VOL,N=24,M=6): ''' 威廉变异离散量 输出WVAD:(收盘价-开盘价)/(最高价-最低价)成交量(手)的N日累和/10000 输出MAWVAD:WVAD的M日简单移动平均 ''' WVAD=SUM((CLOSE-OPEN)/(HIGH-LOW)VOL,N)/10000 MAWVAD=MA(WVAD,M) return WVAD,MAWVAD def DBQR(CLOSE,INDEXC,N=5,M1=10,M2=20,M3=60): ''' 对比强弱(需下载日线) 输出ZS:(大盘的收盘价-N日前的大盘的收盘价)/N日前的大盘的收盘价 输出GG:(收盘价-N日前的收盘价)/N日前的收盘价 输出MADBQR1:GG的M1日简单移动平均 输出MADBQR2:GG的M2日简单移动平均 输出MADBQR3:GG的M3日简单移动平均 ''' ZS=(INDEXC-REF(INDEXC,N))/REF(INDEXC,N) GG=(CLOSE-REF(CLOSE,N))/REF(CLOSE,N) MADBQR1=MA(GG,M1) MADBQR2=MA(GG,M2) MADBQR3=MA(GG,M3) return ZS,GG,MADBQR1,MADBQR2,MADBQR3 def JS(CLOSE,N=5,M1=5,M2=10,M3=20): ''' 加数线 输出加速线:100(收盘价-N日前的收盘价)/(NN日前的收盘价) 输出MAJS1:JS的M1日简单移动平均 输出MAJS2:JS的M2日简单移动平均 输出MAJS3:JS的M3日简单移动平均 ''' JS=100*(CLOSE-REF(CLOSE,N))/(NREF(CLOSE,N)) MAJS1=MA(JS,M1) MAJS2=MA(JS,M2) MAJS3=MA(JS,M3) return JS,MAJS1,MAJS2,MAJS3 def CYE(CLOSE): ''' 市场趋势 MAL赋值:收盘价的5日简单移动平均 MAS赋值:收盘价的20日简单移动平均的5日简单移动平均 输出CYEL:(MAL-1日前的MAL)/1日前的MAL100 输出CYES:(MAS-1日前的MAS)/1日前的MAS100 ''' MAL=MA(CLOSE,5) MAS=MA(MA(CLOSE,20),5) CYEL=(MAL-REF(MAL,1))/REF(MAL,1)100 CYES=(MAS-REF(MAS,1))/REF(MAS,1)100 return CYEL,CYES def QR(CLOSE,INDEXC,N=21): ''' 强弱指标(需下载日线) NN赋值:收盘价的有效数据周期数和N的较小值 输出 个股: (收盘价-NN日前的收盘价)/NN日前的收盘价100 输出 大盘: (大盘的收盘价-NN日前的大盘的收盘价)/NN日前的大盘的收盘价100 输出 强弱值:个股-大盘的2日指数移动平均,COLORSTICK ''' NN=MIN(BARSCOUNT(CLOSE),N) GG=(CLOSE-REF(CLOSE,NN))/REF(CLOSE,NN)100 DP=(INDEXC-REF(INDEXC,NN))/REF(INDEXC,NN)100 value=EMA(GG-DP,2) return GG,DP,value def GDX(CLOSE,HIGH,LOW,N=30,M=9): ''' 轨道线 AA赋值:(2收盘价+最高价+最低价)/4-收盘价的N日简单移动平均的绝对值/收盘价的N日简单移动平均 输出 轨道:以AA为权重收盘价的动态移动平均 输出压力线:(1+M/100)轨道 输出 支撑线:(1-M/100)轨道 ''' AA=ABS((2CLOSE+HIGH+LOW)/4-MA(CLOSE,N))/MA(CLOSE,N) 轨道 =DMA(AA,0.5) 压力线=(1+M/100)轨道 支撑线=(1-M/100)轨道 return 轨道,压力线,支撑线 def JLHB(CLOSE,LOW,HIGH,N=7,M=5): ''' 绝路航标 VAR1赋值:(收盘价-60日内最低价的最低值)/(60日内最高价的最高值-60日内最低价的最低值)80 输出 B:VAR1的N日[1日权重]移动平均 输出 VAR2:B的M日[1日权重]移动平均 输出 绝路航标:如果B上穿VAR2ANDB<40,返回50,否则返回0 ''' VAR1=(CLOSE-LLV(LOW,60))/(HHV(HIGH,60)-LLV(LOW,60))80 B=SMA(VAR1,N,1) VAR2=SMA(B,M,1) 绝路航标=IF(np.logical_and(B,VAR2),50,0) return B,VAR2,绝路航标 #************************************** #******************************************** #能量类型 def BRAR(OPEN,HIGH,LOW,CLOSE,N=26): ''' 情绪指标 输出BR:0和最高价-1日前的收盘价的较大值的N日累和/0和1日前的收盘价-最低价的较大值的N日累和100 输出AR:最高价-开盘价的N日累和/开盘价-最低价的N日累和100 ''' BR=SUM(MAX(0,HIGH-REF(CLOSE,1)),N)/SUM(MAX(0,REF(CLOSE,1)-LOW),N)100 AR=SUM(HIGH-OPEN,N)/SUM(OPEN-LOW,N)100 return BR,AR def CR(HIGH,LOW,N=26,M1=10,M2=20,M3=40,M4=60): ''' 带状能量线 MID赋值:1日前的最高价+最低价/2 输出带状能量线:0和最高价-MID的较大值的N日累和/0和MID-最低价的较大值的N日累和100 输出MA1:M1/2.5+1日前的CR的M1日简单移动平均 输出均线:M2/2.5+1日前的CR的M2日简单移动平均 输出MA3:M3/2.5+1日前的CR的M3日简单移动平均 输出MA4:M4/2.5+1日前的CR的M4日简单移动平均 ''' MID=REF(HIGH+LOW,1)/2 CR=SUM(MAX(0,HIGH-MID),N)/SUM(MAX(0,MID-LOW),N)100 MA1=pd.DataFrame(CR).shift(11).mean() MA2=pd.DataFrame(CR).shift(5).mean() MA3=pd.DataFrame(CR).shift(17).mean() MA4=pd.DataFrame(CR).shift(25).mean() return CR,MA1,MA2,MA3,MA4 def MASS(HIGH,LOW,N1=9,N2=25,M=6): ''' 梅斯线 输出梅斯线:最高价-最低价的N1日简单移动平均/最高价-最低价的N1日简单移动平均的N1日简单移动平均的N2日累和 输出MAMASS:MASS的M日简单移动平均 ''' MASS=SUM(MA(HIGH-LOW,N1)/MA(MA(HIGH-LOW,N1),N1),N2) MAMASS=MA(MASS,M) return MASS,MAMASS def PSY(CLOSE,N=12,M=6): ''' 心理线 输出PSY:统计N日中满足收盘价>1日前的收盘价的天数/N100 输出PSYMA:PSY的M日简单移动平均 ''' PSY=COUNT(CLOSE>REF(CLOSE,1),N)/N100 PSYMA=MA(PSY,M) return PSY,PSYMA def VR(CLOSE,VOL,N=26,M=6): ''' 成交量变异率 TH赋值:如果收盘价>1日前的收盘价,返回成交量(手),否则返回0的N日累和 TL赋值:如果收盘价<1日前的收盘价,返回成交量(手),否则返回0的N日累和 TQ赋值:如果收盘价=1日前的收盘价,返回成交量(手),否则返回0的N日累和 输出VR:100(TH2+TQ)/(TL2+TQ) 输出MAVR:VR的M日简单移动平均 ''' TH=SUM(IF(CLOSE>REF(CLOSE,1),VOL,0),N) TL=SUM(IF(CLOSE<REF(CLOSE,1),VOL,0),N) TQ=SUM(IF(CLOSE==REF(CLOSE,1),VOL,0),N) VR=100*(TH2+TQ)/(TL2+TQ) MAVR=MA(VR,M) return VR,MAVR def WAD(CLOSE,LOW,HIGH,M=30): ''' 威廉多空力度线 MIDA赋值:收盘价-1日前的收盘价和最低价的较小值 MIDB赋值:如果收盘价<1日前的收盘价,返回收盘价-1日前的收盘价和最高价的较大值,否则返回0 输出威廉多空力度线:如果收盘价>1日前的收盘价,返回MIDA,否则返回MIDB的历史累和 输出MAWAD:WAD的M日简单移动平均 ''' MIDA=CLOSE-MIN(REF(CLOSE,1),LOW) MIDB=IF(CLOSE<REF(CLOSE,1),CLOSE-MAX(REF(CLOSE,1),HIGH),0) WAD=SUM(IF(CLOSE>REF(CLOSE,1),MIDA,MIDB),0) MAWAD=MA(WAD,M) return WAD,MAWAD def EXPMEMA(CLOSE,M=5): ''' 指数平滑 ''' return pd.Series(CLOSE).ewm(span=M, adjust=False).mean().values def PCNT(CLOSE,M=5): ''' 输出幅度比:(收盘价-1日前的收盘价)/收盘价100 输出MAPCNT:PCNT的M日指数平滑移动平均 ''' PCNT=(CLOSE-REF(CLOSE,1))/CLOSE100 MAPCNT=EXPMEMA(PCNT,M) return PCNT,MAPCNT def CYR(AMOUNT,VOL,N=13,M=5): ''' 市场强弱 AMOUNT成交量=pricevolume DIVE赋值:0.01成交额(元)的N日指数移动平均/成交量(手)的N日指数移动平均 输出市场强弱:(DIVE/1日前的DIVE-1)100 输出MACYR:CYR的M日简单移动平均 ''' DIVE=0.01EMA(AMOUNT,N)/EMA(VOL,N) CYR=(DIVE/REF(DIVE,1)-1)100 MACYR=MA(CYR,M) return CYR,MACYR #******************************************** #********************************************* #能量型 def AMO(AMOUNT,M1=5,M2=10): ''' 成交金额 输出AMOW:成交额(元)/10000.0,VOLSTICK 输出AMO1:AMOW的M1日简单移动平均 输出AMO2:AMOW的M2日简单移动平均 ''' AMOW=AMOUNT/10000.0 AMO1=MA(AMOW,M1) AMO2=MA(AMOW,M2) return AMOW,AMO1,AMO2 def OBV(VOL,CLOSE,M=30): ''' 累积能量线 VA赋值:如果收盘价>1日前的收盘价,返回成交量(手),否则返回-成交量(手) 输出OBV:如果收盘价=1日前的收盘价,返回0,否则返回VA的历史累和 输出MAOBV:OBV的M日简单移动平均 ''' VA=IF(CLOSE>REF(CLOSE,1),VOL,-VOL) OBV=SUM(IF(CLOSE==REF(CLOSE,1),0,VA),0) MAOBV=MA(OBV,M) return OBV,MAOBV def VOL_XT(VOL,M1=5,M2=10): ''' 成交量 输出VOLUME:成交量(手),VOLSTICK 输出MAVOL1:VOLUME的M1日简单移动平均 输出MAVOL2:VOLUME的M2日简单移动平均 ''' VOLUME=VOL MAVOL1=MA(VOLUME,M1) MAVOL2=MA(VOLUME,M2) return MAVOL1,MAVOL2 def VRSI(VOL,N1=6,N2=12,N3=24): ''' 相对强弱量 LC赋值:1日前的成交量(手) 输出RSI1:成交量(手)-LC和0的较大值的N1日[1日权重]移动平均/成交量(手)-LC的绝对值的N1日[1日权重]移动平均100 输出RSI2:成交量(手)-LC和0的较大值的N2日[1日权重]移动平均/成交量(手)-LC的绝对值的N2日[1日权重]移动平均100 输出RSI3:成交量(手)-LC和0的较大值的N3日[1日权重]移动平均/成交量(手)-LC的绝对值的N3日[1日权重]移动平均100 ''' LC=REF(VOL,1) RSI1=SMA(MAX(VOL-LC,0),N1,1)/SMA(ABS(VOL-LC),N1,1)100 RSI2=SMA(MAX(VOL-LC,0),N2,1)/SMA(ABS(VOL-LC),N2,1)100 RSI3=SMA(MAX(VOL-LC,0),N3,1)/SMA(ABS(VOL-LC),N3,1)100 return RSI1,RSI2,RSI3 def HSL(HSL,N=5): ''' 换手线 ''' HSL=HSL MAHSL=MA(HSL,N) return HSL,MAHSL #************************************** #****************************************** #均线系统 def MA_XT(CLOSE,M1=5,M2=10,M3=20,M4=60): ''' 均线 输出MA1:收盘价的M1日简单移动平均 输出均线:收盘价的M2日简单移动平均 输出MA3:收盘价的M3日简单移动平均 输出MA4:收盘价的M4日简单移动平均 输出MA5:收盘价的M5日简单移动平均 输出MA6:收盘价的M6日简单移动平均 输出MA7:收盘价的M7日简单移动平均 输出MA8:收盘价的M8日简单移动平均 ''' MA1=MA(CLOSE,M1) MA2=MA(CLOSE,M2) MA3=MA(CLOSE,M3) MA4=MA(CLOSE,M4) return MA1,MA2,MA3,MA4 def MA2(CLOSE,M1=5,M2=10,M3=20,M4=60,M5=120,M6=240,M7=360,M8=420,M9=680,M10=720): ''' 均线2 输出MA1:收盘价的M1日简单移动平均 输出均线:收盘价的M2日简单移动平均 输出MA3:收盘价的M3日简单移动平均 输出MA4:收盘价的M4日简单移动平均 输出MA5:收盘价的M5日简单移动平均 输出MA6:收盘价的M6日简单移动平均 输出MA7:收盘价的M7日简单移动平均 输出MA8:收盘价的M8日简单移动平均 输出MA9:收盘价的M9日简单移动平均 输出MA10:收盘价的M10日简单移动平均 ''' MA1=MA(CLOSE,M1) MA2=MA(CLOSE,M2) MA3=MA(CLOSE,M3) MA4=MA(CLOSE,M4) MA5=MA(CLOSE,M5) MA6=MA(CLOSE,M6) MA7=MA(CLOSE,M7) MA8=MA(CLOSE,M8) MA9=MA(CLOSE,M9) MA10=MA(CLOSE,M10) return MA1,MA2,MA3,MA4,MA5,MA6,MA7,MA8,MA8,MA9,MA10 def ACD(CLOSE,HIGH,LOW,M=20): ''' 升降线 LC赋值:1日前的收盘价 DIF赋值:收盘价-如果收盘价>LC,返回最低价和LC的较小值,否则返回最高价和LC的较大值 输出升降线:如果收盘价=LC,返回0,否则返回DIF的历史累和 输出MAACD:ACD的M日指数平滑移动平均 ''' LC=REF(CLOSE,1) DIF=CLOSE-IF(CLOSE>LC,MIN(LOW,LC),MAX(HIGH,LC)) ACD=SUM(IF(CLOSE==LC,0,DIF),0) MAACD=EXPMEMA(ACD,M) return ACD,MAACD def BBI(CLOSE,M1=3,M2=6,M3=12,M4=24): ''' 多空均线 输出多空均线:(收盘价的M1日简单移动平均+收盘价的M2日简单移动平均+收盘价的M3日简单移动平均+收盘价的M4日简单移动平均)/4 ''' BBI=(MA(CLOSE,M1)+MA(CLOSE,M2)+MA(CLOSE,M3)+MA(CLOSE,M4))/4 return BBI def EXPMA(CLOSE,M1=12,M2=50): ''' 指数平均线 输出EXP1:收盘价的M1日指数移动平均 输出EXP2:收盘价的M2日指数移动平均 ''' EXP1=EMA(CLOSE,M1) EXP2=EMA(CLOSE,M2) return EXP1,EXP2 def HMA(HIGH,M1=6,M2=12,M3=30,M4=70,M5=90): ''' 高价平均线 输出HMA1:最高价的M1日简单移动平均 输出HMA2:最高价的M2日简单移动平均 输出HMA3:最高价的M3日简单移动平均 输出HMA4:最高价的M4日简单移动平均 输出HMA5:最高价的M5日简单移动平均 ''' HMA1=MA(HIGH,M1) HMA2=MA(HIGH,M2) HMA3=MA(HIGH,M3) HMA4=MA(HIGH,M4) HMA5=MA(HIGH,M5) return HMA1,HMA2,HMA3,HMA4,HMA5 def LMA(LOW,M1=6,M2=12,M3=30,M4=70,M5=90): ''' 低价平均线 输出LMA1:最低价的M1日简单移动平均 输出LMA2:最低价的M2日简单移动平均 输出LMA3:最低价的M3日简单移动平均 输出LMA4:最低价的M4日简单移动平均 输出LMA5:最低价的M5日简单移动平均 ''' LMA1=MA(LOW,M1) LMA2=MA(LOW,M2) LMA3=MA(LOW,M3) LMA4=MA(LOW,M4) LMA5=MA(LOW,M5) return LMA1,LMA2,LMA3,LMA4,LMA5 def VMA(HIGH,OPEN,LOW,CLOSE,M1=6,M2=12,M3=30,M4=70,M5=90): ''' 变异平均线 VV赋值:(最高价+开盘价+最低价+收盘价)/4 输出VMA1:VV的M1日简单移动平均 输出VMA2:VV的M2日简单移动平均 输出VMA3:VV的M3日简单移动平均 输出VMA4:VV的M4日简单移动平均 输出VMA5:VV的M5日简单移动平均 ''' VV=(HIGH+OPEN+LOW+CLOSE)/4 VMA1=MA(VV,M1) VMA2=MA(VV,M2) VMA3=MA(VV,M3) VMA4=MA(VV,M4) VMA5=MA(VV,M5) return VMA1,VMA2,VMA3,VMA4,VMA5 def AMV(OPEN,CLOSE,VOL,M1=5,M2=13,M3=34,M4=60): ''' 成本均线 AMOV赋值:成交量(手)(开盘价+收盘价)/2 输出AMV1:AMOV的M1日累和/成交量(手)的M1日累和 输出AMV2:AMOV的M2日累和/成交量(手)的M2日累和 输出AMV3:AMOV的M3日累和/成交量(手)的M3日累和 输出AMV4:AMOV的M4日累和/成交量(手)的M4日累和 ''' AMOV=VOL(OPEN+CLOSE)/2 AMV1=SUM(AMOV,M1)/SUM(VOL,M1) AMV2=SUM(AMOV,M2)/SUM(VOL,M2) AMV3=SUM(AMOV,M3)/SUM(VOL,M3) AMV4=SUM(AMOV,M4)/SUM(VOL,M4) return AMV1,AMV2,AMV3,AMV4 def BBIBOLL(CLOSE,N=11,M=6): ''' 多空布林线 CV赋值:收盘价 输出多空布林线:(CV的3日简单移动平均+CV的6日简单移动平均+CV的12日简单移动平均+CV的24日简单移动平均)/4 输出UPR:BBIBOLL+MBBIBOLL的N日估算标准差 输出DWN:BBIBOLL-MBBIBOLL的N日估算标准差 ''' CV=CLOSE BBIBOLL=(MA(CV,3)+MA(CV,6)+MA(CV,12)+MA(CV,24))/4 UPR=BBIBOLL+MSTD(BBIBOLL,N) DWN=BBIBOLL-MSTD(BBIBOLL,N) return BBIBOLL,UPR,DWN def ALLIGAT(HIGH,LOW): ''' 鳄鱼线 NN赋值:(最高价+最低价)/2 输出上唇:3日前的NN的5日简单移动平均,COLOR40FF40 输出牙齿:5日前的NN的8日简单移动平均,COLOR0000C0 输出下颚:8日前的NN的13日简单移动平均,COLORFF4040 ''' H=HIGH L=LOW NN=(H+L)/2 上唇=REF(MA(NN,5),3) 牙齿=REF(MA(NN,8),5) 下颚=REF(MA(NN,13),8) return 上唇,牙齿,下颚 def GMMA(CLOSE): ''' 顾比均线 ''' MA3=EMA(CLOSE,3) MA5=EMA(CLOSE,5) MA8=EMA(CLOSE,8) MA10=EMA(CLOSE,10) MA12=EMA(CLOSE,12) MA15=EMA(CLOSE,15) MA30=EMA(CLOSE,30) MA35=EMA(CLOSE,35) MA40=EMA(CLOSE,40) MA45=EMA(CLOSE,45) MA50=EMA(CLOSE,50) MA60=EMA(CLOSE,60) return MA3,MA5,MA8,MA10,MA12,MA15,MA30,MA35,MA40,MA45,MA50,MA60 #******************************************* #******************************************* #路径类 def BOLL(CLOSE,M=20): ''' 布林线 输出BOLL:收盘价的M日简单移动平均 输出UB:BOLL+2收盘价的M日估算标准差 输出LB:BOLL-2收盘价的M日估算标准差 ''' BOLL=MA(CLOSE,M) UB=BOLL+2STD(CLOSE,M) LB=BOLL-2STD(CLOSE,M) return BOLL,UB,LB def PBX(CLOSE,M1=4,M2=6,M3=9,M4=13,M5=18,M6=24): ''' 瀑布线 输出PBX1:(收盘价的M1日指数移动平均+收盘价的M12日简单移动平均+收盘价的M14日简单移动平均)/3 输出PBX2:(收盘价的M2日指数移动平均+收盘价的M22日简单移动平均+收盘价的M24日简单移动平均)/3 输出PBX3:(收盘价的M3日指数移动平均+收盘价的M32日简单移动平均+收盘价的M34日简单移动平均)/3 输出PBX4:(收盘价的M4日指数移动平均+收盘价的M42日简单移动平均+收盘价的M44日简单移动平均)/3 输出PBX5:(收盘价的M5日指数移动平均+收盘价的M52日简单移动平均+收盘价的M54日简单移动平均)/3 输出PBX6:(收盘价的M6日指数移动平均+收盘价的M62日简单移动平均+收盘价的M64日简单移动平均)/3 ''' PBX1=(EMA(CLOSE,M1)+MA(CLOSE,M12)+MA(CLOSE,M14))/3 PBX2=(EMA(CLOSE,M2)+MA(CLOSE,M22)+MA(CLOSE,M24))/3 PBX3=(EMA(CLOSE,M3)+MA(CLOSE,M32)+MA(CLOSE,M34))/3 PBX4=(EMA(CLOSE,M4)+MA(CLOSE,M42)+MA(CLOSE,M44))/3 PBX5=(EMA(CLOSE,M5)+MA(CLOSE,M52)+MA(CLOSE,M54))/3 PBX6=(EMA(CLOSE,M6)+MA(CLOSE,M62)+MA(CLOSE,M64))/3 return PBX1,PBX2,PBX3,PBX4,PBX5,PBX6 def ENE(CLOSE,N=25,M1=6,M2=6): ''' 轨道线 输出UPPER:(1+M1/100)收盘价的N日简单移动平均 输出LOWER:(1-M2/100)收盘价的N日简单移动平均 输出轨道线:(UPPER+LOWER)/2 ''' UPPER=(1+M1/100)MA(CLOSE,N) LOWER=(1-M2/100)MA(CLOSE,N) ENE=(UPPER+LOWER)/2 return UPPER,LOWER,ENE def MIKE(HIGH,LOW,CLOSE,N=10): ''' 麦克支撑压力 HLC赋值:1日前的(最高价+最低价+收盘价)/3的N日简单移动平均 HV赋值:N日内最高价的最高值的3日指数移动平均 LV赋值:N日内最低价的最低值的3日指数移动平均 输出STOR:2HV-LV的3日指数移动平均 输出MIDR:HLC+HV-LV的3日指数移动平均 输出WEKR:HLC2-LV的3日指数移动平均 ''' HLC=REF(MA((HIGH+LOW+CLOSE)/3,N),1) HV=EMA(HHV(HIGH,N),3) LV=EMA(LLV(LOW,N),3) STOR=EMA(2HV-LV,3) MIDR=EMA(HLC+HV-LV,3) WEKR=EMA(HLC2-LV,3) WEKS=EMA(HLC2-HV,3) MIDS=EMA(HLC-HV+LV,3) STOS=EMA(2LV-HV,3) return STOR,MIDR,WEKR,WEKS,MIDS,STOS def XS(CLOSE,VOL,N=13): ''' 薛斯通道 VAR2赋值:收盘价成交量(手) VAR3赋值:(VAR2的3日指数移动平均/成交量(手)的3日指数移动平均+VAR2的6日指数移动平均/成交量(手)的6日指数移动平均+VAR2的12日指数移动平均/成交量(手)的12日指数移动平均+VAR2的24日指数移动平均/成交量(手)的24日指数移动平均)/4的N日指数移动平均 输出SUP:1.06VAR3 输出SDN:VAR30.94 VAR4赋值:收盘价的9日指数移动平均 输出LUP:VAR41.14的5日指数移动平均 输出LDN:VAR40.86的5日指数移动平均 ''' VAR2=CLOSEVOL VAR3=EMA((EMA(VAR2,3)/EMA(VOL,3)+EMA(VAR2,6)/EMA(VOL,6)+EMA(VAR2,12)/EMA(VOL,12)+EMA(VAR2,24)/EMA(VOL,24))/4,N) SUP=1.06VAR3 SDN=VAR30.94 VAR4=EMA(CLOSE,9) LUP=EMA(VAR41.14,5) LDN=EMA(VAR40.86,5) return SUP,SDN,LUP,LDN def XS2(CLOSE,HIGH,LOW,N=102,M=7): ''' 薛斯通道II AA赋值:(2收盘价+最高价+最低价)/4的5日简单移动平均 输出 通道1:AAN/100 输出 通道2:AA*(200-N)/100 CC赋值:(2收盘价+最高价+最低价)/4-收盘价的20日简单移动平均的绝对值/收盘价的20日简单移动平均 DD赋值:以CC为权重收盘价的动态移动平均 输出 通道3:(1+M/100)DD ''' AA=MA((2CLOSE+HIGH+LOW)/4,5) 通道1=AAN/100 通道2=AA*(200-N)/100 CC=ABS((2*CLOSE+HIGH+LOW)/4-MA(CLOSE,20))/MA(CLOSE,20) DD=DMA(CLOSE,0.5) 通道3=(1+M/100)*DD 通道4=(1-M/100)*DD return 通道1,通道2,通道3,通道4 def TQN(HIGH, LOW, X1=20, X2=20): ''' 唐奇安通道 输出周期高点:1日前的X1日内最高价的最高值 输出周期低点:1日前的X2日内最低价的最低值 平空开多赋值:最高价>=周期高点 平多开空赋值:最低价<=周期低点 先平空仓再开多仓 先平多仓再开空仓 自动过滤交易信号 ''' # 计算周期高点：X1日内最高价的最高值，然后取1日前的值 周期高点 = REF(HHV(HIGH, X1), 1)
# 计算周期低点：X2日内最低价的最低值，然后取1日前的值
周期低点 = REF(LLV(LOW, X2), 1)
# 平空开多信号：最高价 >= 周期高点
平空开多 = HIGH >= 周期高点
# 平多开空信号：最低价 <= 周期低点
平多开空 = LOW <= 周期低点
return 周期高点, 周期低点, 平空开多, 平多开空
#******************************************* #******************************************* def SAR(HIGH, LOW, M=10, af=2, amax=20): ''' 抛物线指标 ''' af = af / 100 amax = amax / 100
# 转换为numpy数组，处理NaN
high = np.array(HIGH, dtype=float)
low = np.array(LOW, dtype=float)
# 检查数据有效性
if len(high) == 0 or np.isnan(high).all() or np.isnan(low).all():
return pd.Series([np.nan] * len(HIGH))
# 替换NaN为有效值（用前向填充或均值）
high_clean = pd.Series(high).fillna(method='ffill').fillna(method='bfill').values
low_clean = pd.Series(low).fillna(method='ffill').fillna(method='bfill').values
n = len(high_clean)
# 初始化结果数组
sar = np.full(n, np.nan)
# 需要至少 M+1 个数据点
if n < M + 1:
return pd.Series(sar, index=HIGH.index if hasattr(HIGH, 'index') else None)
# 计算标准差，处理0值
hl_std = np.std(high_clean - low_clean)
if hl_std == 0 or np.isnan(hl_std):
hl_std = 0.001 # 设置一个极小值避免除零
# 起始值
sig0 = True
xpt0 = high_clean[M - 1] if M > 0 else high_clean[0]
af0 = af
# 第一个SAR值
sar[0] = low_clean[0] - hl_std
for i in range(1, n):
sig1 = sig0
xpt1 = xpt0
af1 = af0
if i < M:
# 前M个数据点使用简单方式
if i > 0:
sar[i] = sar[i-1] + (xpt1 - sar[i-1]) * af1
continue
# 获取当前和前一个的高低点
lmin = min(low_clean[i-1], low_clean[i])
lmax = max(high_clean[i-1], high_clean[i])
# 判断趋势方向
if sig1:
sig0 = low_clean[i] > sar[i-1]
xpt0 = max(lmax, xpt1)
else:
sig0 = high_clean[i] >= sar[i-1]
xpt0 = min(lmin, xpt1)
# 计算SAR值
if sig0 == sig1:
sari = sar[i-1] + (xpt1 - sar[i-1]) * af1
af0 = min(amax, af1 + af)
if sig0:
af0 = af0 if xpt0 > xpt1 else af1
sari = min(sari, lmin)
else:
af0 = af0 if xpt0 < xpt1 else af1
sari = max(sari, lmax)
else:
af0 = af
sari = xpt0
sar[i] = sari
# 转换为pandas Series，保持索引一致
if hasattr(HIGH, 'index'):
return pd.Series(sar, index=HIGH.index)
else:
return pd.Series(sar)
#******************************* #****************************** #交易类型 def MA_交易(CLOSE,SHORT=5,LONG=20): ''' MA_交易 MA1赋值:收盘价的SHORT日简单移动平均 MA2赋值:收盘价的LONG日简单移动平均 平空开多赋值:MA1上穿MA2 平多开空赋值:MA2上穿MA1 先平空仓再开多仓 先平多仓再开空仓 ''' MA1=MA(CLOSE,SHORT) MA2=MA(CLOSE,LONG) 平空开多=CROSS(MA1,MA2) 平多开空=CROSS(MA2,MA1) return MA1,MA2,平空开多,平多开空 def MACD_交易(CLOSE,SHORT=12,LONG=26,MID=9): ''' MACD交易 DIFF赋值:收盘价的SHORT日指数移动平均-收盘价的LONG日指数移动平均 DEA赋值:DIFF的MID日指数移动平均 MACD赋值:2*(DIFF-DEA) 平空开多赋值:MACD上穿0 平多开空赋值:0上穿MACD 先平空仓再开多仓 ''' DIFF=EMA(CLOSE,SHORT)-EMA(CLOSE,LONG) DEA=EMA(DIFF,MID) MACD=2*(DIFF-DEA) 平空开多=CROSS(MACD,0) 平空开多=CROSS(0,MACD) return DIFF,DEA,MACD,平空开多,平空开多 def KDJ_交易(CLOSE,HIGH,LOW,N=9,M1=3): ''' KDJ交易 RSV赋值:(收盘价-N日内最低价的最低值)/(N日内最高价的最高值-N日内最低价的最低值)100 K赋值:RSV的M1日[1日权重]移动平均 D赋值:K的M1日[1日权重]移动平均 J赋值:3K-2D 平空开多赋值:J上穿0 平多开空赋值:100上穿J 先平空仓再开多仓 先平多仓再开空仓 自动过滤交易信号 ''' RSV=(CLOSE-LLV(LOW,N))/(HHV(HIGH,N)-LLV(LOW,N))100 K=SMA(RSV,M1,1) D=SMA(K,M1,1) J=3K-2D 平空开多=CROSS(J,0) 平多开空=CROSS(100,J) return K,D,J,平空开多,平多开空 #***************************************** #***************************************** #神系 def SG_XDT(CLOSE,INDEXC,P1=5,P2=10): ''' 心电图(需下载日线) 输出强弱指标(需下载日线):收盘价/大盘的收盘价1000 输出MQR1:QR的5日简单移动平均 输出MQR2:QR的10日简单移动平均 ''' QR=CLOSE/INDEXC1000 MQR1=MA(QR,5) MQR2=MA(QR,10) return QR,MQR1,MQR2 def SG_NDB(CLOSE,HIGH,LOW,P1=5,P2=10): ''' 脑电波(神系) HH赋值:如果收盘价/1日前的收盘价>1.093ANDL>1日前的最高价,返回2收盘价-1日前的收盘价-最高价,否则返回2收盘价-最高价-最低价 V1赋值:收盘价的有效数据周期数 V2赋值:2V1日前的收盘价-V1日前的最高价-V1日前的最低价 输出DK:HH的历史累和+V2 输出MDK1:DK的P1日简单移动平均 输出MDK2:DK的P2日简单移动平均 ''' C=CLOSE H=HIGH L=LOW HH=IF(np.logical_or(C/REF(C,1)>1.093 ,L>REF(H,1)),2C-REF(C,1)-H,2C-H-L) V1=1 V2=2REF(C,V1)-REF(H,V1)-REF(L,V1) DK=SUM(HH,0)+V2 MDK1=MA(DK,P1) MDK2=MA(DK,P2) return DK,MDK1,MDK2 def SG_SMX(CLOSE,HIGH,LOW,INDEXH,INDEXL,INDEXC,N=50): ''' 生命线(需下载日线) INDEXH,INDEXL,INDEXC指数的高，低收盘价,可以通过akshare.stock_zh_a_daily(sybol='sh000001')获取 H1赋值:N日内最高价的最高值 L1赋值:N日内最低价的最低值 H2赋值:N日内大盘的最高价的最高值 L2赋值:N日内大盘的最低价的最低值 ZY赋值:收盘价/大盘的收盘价2000 输出ZY1:ZY的3日指数移动平均 输出ZY2:ZY的17日指数移动平均 输出ZY3:ZY的34日指数移动平均 ''' H1=HHV(HIGH,N) L1=LLV(LOW,N) H2=HHV(INDEXH,N) L2=LLV(INDEXL,N) ZY=CLOSE/INDEXC2000 ZY1=EMA(ZY,3) ZY2=EMA(ZY,17) ZY3=EMA(ZY,34) return ZY1,ZY2,ZY3 def SG_LB(VOL,INDEXV): ''' 量比(需下载日线) VOl个股成交量，INDXEXV大盘成交量，可以通过ak.stock_zh_a_daily()获取 ZY2赋值:成交量(手)/大盘的成交量1000 输出量比:ZY2 输出MA5:ZY2的5日简单移动平均 输出MA10:ZY2的10日简单移动平均 ''' ZY2=VOL/INDEXV1000 量比=ZY2 MA5=MA(ZY2,5) MA10=MA(ZY2,10) return 量比,MA5,MA10 def SG_PF(CLOSE,INDEXC): ''' 强势股评分(需下载日线) ZY1赋值:收盘价/大盘的收盘价1000 A1赋值:如果ZY1>3日内ZY1的最高值,返回10,否则返回0 A2赋值:如果ZY1>5日内ZY1的最高值,返回15,否则返回0 A3赋值:如果ZY1>10日内ZY1的最高值,返回20,否则返回0 A4赋值:如果ZY1>2日内ZY1的最高值,返回10,否则返回0 A5赋值:统计9日中满足ZY1>1日前的ZY1的天数5 输出强势股评分:A1+A2+A3+A4+A5 ''' ZY1=CLOSE/INDEXC1000 A1=IF(ZY1>HHV(ZY1,3),10,0) A2=IF(ZY1>HHV(ZY1,5),15,0) A3=IF(ZY1>HHV(ZY1,10),20,0) A4=IF(ZY1>HHV(ZY1,2),10,0) A5=COUNT(ZY1>REF(ZY1,1) ,9)5 强势股评分=A1+A2+A3+A4+A5 return 强势股评分 #*********************************************** #************************************************* #龙系 def RAD(OPEN,HIGH,CLOSE,LOW,INDEXO,INDEXH,INDEXL,INDEXC,D=3,S=30,M=30): ''' 威力雷达(需下载日线) OPEN+HIGH+CLOSE+LOW个股 INDEXO+INDEXH+INDEXL+INDEXC大盘数据，可以通过akshare获取 SM赋值:(开盘价+最高价+收盘价+最低价)/4 SMID赋值:SM的D日简单移动平均 IM赋值:(大盘的开盘价+大盘的最高价+大盘的最低价+大盘的收盘价)/4 IMID赋值:IM的D日简单移动平均 SI1赋值:(SMID-1日前的SMID)/SMID II赋值:(IMID-1日前的IMID)/IMID 输出RADER1:(SI1-II)2的S日累和1000 输出RADERMA:RADER1的M日[1日权重]移动平均 ''' SM=(OPEN+HIGH+CLOSE+LOW)/4 SMID=MA(SM,D) IM=(INDEXO+INDEXH+INDEXL+INDEXC)/4 IMID=MA(IM,D) SI1=(SMID-REF(SMID,1))/SMID II=(IMID-REF(IMID,1))/IMID RADER1=SUM((SI1-II)2,S)1000 RADERMA=SMA(RADER1,M,1) return RADER1,RADERMA return def LON(CLOSE,HIGH,LOW,VOL,N=10): ''' 龙系长线 赋值: 1日前的收盘价 赋值: 成交量(手)的2日累和/(((2日内最高价的最高值-2日内最低价的最低值))100) 赋值: (收盘价-LC)VID 赋值: RC的历史累和 赋值: LONG的10日[1日权重]移动平均 赋值: LONG的20日[1日权重]移动平均 输出龙系长线 : DIFF-DEA 输出LONMA : 龙系长线的N日简单移动平均 输出LONT : 龙系长线, COLORSTICK ''' LC = REF(CLOSE,1) VID = SUM(VOL,2)/(((HHV(HIGH,2)-LLV(LOW,2)))100) RC = (CLOSE-LC)VID LONG = SUM(RC,0) DIFF = SMA(LONG,10,1) DEA = SMA(LONG,20,1) LON = DIFF-DEA LONMA = MA(LON,N) LONT = LON return LON,LONMA,LONT def SHT(CLOSE,VOL,N=5): ''' 龙系短线 VAR1赋值:(成交量(手)-1日前的成交量(手))/1日前的成交量(手)的5日简单移动平均 VAR2赋值:(收盘价-收盘价的24日简单移动平均)/收盘价的24日简单移动平均100 输出MY: VAR2(1+VAR1) 输出龙系短线: MY, COLORSTICK 输出SHTMA: SHT的N日简单移动平均 ''' VAR1=MA((VOL-REF(VOL,1))/REF(VOL,1),5) VAR2=(CLOSE-MA(CLOSE,24))/MA(CLOSE,24)100 MY= VAR2(1+VAR1) SHT= MY#COLORSTICK SHTMA= MA(SHT,N) return SHT,SHTMA def ZLJC(CLOSE,LOW,HIGH,VOL): ''' 主力进出 VAR1赋值:(收盘价+最低价+最高价)/3 VAR2赋值:((VAR1-1日前的最低价)-(最高价-VAR1))成交量(手)/100000/(最高价-最低价)的历史累和 VAR3赋值:VAR2的1日指数移动平均 输出 JCS:VAR3 输出 JCM:VAR3的12日简单移动平均 输出 JCL:VAR3的26日简单移动平均 ''' VAR1=(CLOSE+LOW+HIGH)/3 VAR2=SUM(((VAR1-REF(LOW,1))-(HIGH-VAR1))VOL/100000/(HIGH-LOW),0) VAR3=EMA(VAR2,1) JCS=VAR3 JCM=MA(VAR3,12) JCL=MA(VAR3,26) return JCS,JCM,JCL def ZLMM(CLOSE): ''' 赋值:1日前的收盘价 RSI2赋值:收盘价-LC和0的较大值的12日[1日权重]移动平均/收盘价-LC的绝对值的12日[1日权重]移动平均100 RSI3赋值:收盘价-LC和0的较大值的18日[1日权重]移动平均/收盘价-LC的绝对值的18日[1日权重]移动平均100 输出MMS:3RSI2-2收盘价-LC和0的较大值的16日[1日权重]移动平均/收盘价-LC的绝对值的16日[1日权重]移动平均100的3日简单移动平均 输出MMM:MMS的8日指数移动平均 输出MML:3RSI3-2收盘价-LC和0的较大值的12日[1日权重]移动平均/收盘价-LC的绝对值的12日[1日权重]移动平均100的5日简单移动平均 ''' LC =REF(CLOSE,1) RSI2=SMA(MAX(CLOSE-LC,0),12,1)/SMA(ABS(CLOSE-LC),12,1)100 RSI3=SMA(MAX(CLOSE-LC,0),18,1)/SMA(ABS(CLOSE-LC),18,1)100 MMS=MA(3RSI2-2SMA(MAX(CLOSE-LC,0),16,1)/SMA(ABS(CLOSE-LC),16,1)100,3) MMM=EMA(MMS,8) MML=MA(3RSI3-2SMA(MAX(CLOSE-LC,0),12,1)/SMA(ABS(CLOSE-LC),12,1)100,5) return MMS,MMM,MML def SLZT(CLOSE,LOW,HIGH): ''' 神龙在天 输出白龙: 收盘价的125日简单移动平均 输出黄龙: 白龙+2收盘价的170日估算标准差 输出紫龙: 白龙-2收盘价的145日估算标准差 输出青龙: 步长为1极限值为7的125日抛物转向, LINESTICK VAR2赋值:70日内最高价的最高值 VAR3赋值:20日内最高价的最高值 输出红龙: VAR20.83 输出蓝龙: VAR30.91 ''' 白龙=MA(CLOSE,125) 黄龙=白龙+2STD(CLOSE,170) 紫龙=白龙-2STD(CLOSE,145) 青龙=SAR(HIGH,LOW,125,1,7)# LINESTICK; VAR2=HHV(HIGH,70) VAR3=HHV(HIGH,20) 红龙= VAR20.83 蓝龙=VAR30.91 return 白龙,黄龙,紫龙,青龙,红龙,蓝龙 def ADVOL(CLOSE,HIGH,LOW,VOL): ''' 龙系离散量 A赋值:((收盘价-最低价)-(最高价-收盘价))成交量(手)/10000/(最高价-最低价)的历史累和 输出龙系离散量:A 输出MA1:A的30日简单移动平均 输出均线:MA1的100日简单移动平均 ''' A=SUM(((CLOSE-LOW)-(HIGH-CLOSE))VOL/10000/(HIGH-LOW),0) ADVOL=A MA1=MA(A,30) MA2=MA(MA1,100) return ADVOL,MA1,MA2 #******************************************* #********************************************* #鬼系 def CYC(code='sh600031',start_date='20210101',end_date='20221022',P1=5,P2=13,P3=34): ''' 成本均线 JJJ赋值:如果总量>0.01,简单理解流通股,返回0.01总金额/总量,否则返回昨收盘价 DDD赋值:(最高价<0.01 或者 最低价<0.01) JJJT赋值:如果DDD,返回1,否则返回(JJJ<(最高价+0.01)并且JJJ>(最低价-0.01)) 输出CYC1:如果JJJT,返回0.01成交额(元)的P1日指数移动平均/成交量(手)的P1日指数移动平均,否则返回(最高价+最低价+收盘价)/3的P1日指数移动平均 输出CYC2:如果JJJT,返回0.01成交额(元)的P2日指数移动平均/成交量(手)的P2日指数移动平均,否则返回(最高价+最低价+收盘价)/3的P2日指数移动平均 输出CYC3:如果JJJT,返回0.01成交额(元)的P3日指数移动平均/成交量(手)的P3日指数移动平均,否则返回(最高价+最低价+收盘价)/3的P3日指数移动平均 输出CYC∞:如果JJJT,返回以100成交量(手)/流通股本(股)为权重成交额(元)/(100成交量(手))的动态移动平均,否则返回(最高价+最低价+收盘价)/3的120日指数移动平均 ''' pass def DYNAINFO_10(M=10): ''' 总金额=pricevolume ''' result=df['close']df['volume'] return result def DYNAINFO_3(M=3): ''' 昨日收盘价 ''' return df['close'].shift(1) def DYNAINFO_5(M=5): ''' 最高价 ''' return df['high'] def DYNAINFO_6(M=6): ''' 最低价 ''' return df['low'] AMOUNT=AMOUNT=df['close']df['volume'] VOL=df['volume'] HIGH=df['high'] LOW=df['low'] CLOSE=df['close'] def FINANCE_7(M=7): ''' 100成交量 ''' return 100df['volume'] JJJ=IF(DYNAINFO_8(8)>0.01,0.01DYNAINFO_10(10)/DYNAINFO_8(8),DYNAINFO_3(3)) DDD=np.logical_or(DYNAINFO_5(5)<0.01,DYNAINFO_6(6)<0.01) JJJT=IF(DDD,False,np.logical_and(JJJ<(DYNAINFO_5(5)+0.01),JJJ>(DYNAINFO_6(6)-0.01))) CYC1=IF(JJJT,0.01EMA(AMOUNT,P1)/EMA(VOL,P1),EMA((HIGH+LOW+CLOSE)/3,P1)) CYC2=IF(JJJT,0.01EMA(AMOUNT,P2)/EMA(VOL,P2),EMA((HIGH+LOW+CLOSE)/3,P2)) CYC3=IF(JJJT,0.01EMA(AMOUNT,P3)/EMA(VOL,P3),EMA((HIGH+LOW+CLOSE)/3,P3)) #CYC_a=IF(JJJT,DMA(AMOUNT/(100VOL),100VOL/FINANCE_7(7)),EMA((HIGH+LOW+CLOSE)/3,120)) return CYC1,CYC2,CYC3 def CYS(CLOSE,AMOUNT,VOL): ''' 市场盈亏 AMOUNT成交额，VOL成交量 CYC13赋值:0.01成交额(元)的13日指数移动平均/成交量(手)的13日指数移动平均 输出市场盈亏:(收盘价-CYC13)/CYC13100 ''' CYC13=0.01EMA(AMOUNT,13)/EMA(VOL,13) CYS=(CLOSE-CYC13)/CYC13100 return CYS def CYQKL(CLOSE,OPEN): ''' 博弈K线长度 输出KL:100(以收盘价计算的获利盘比例-以开盘价计算的获利盘比例) ''' KL=100*(WINNER(CLOSE)-WINNER(OPEN)) return KL def CYW(CLOSE,HIGH,LOW,VOL): ''' 主力控盘 VAR1赋值:收盘价-最低价 VAR2赋值:最高价-最低价 VAR3赋值:收盘价-最高价 VAR4赋值:如果最高价>最低价,返回(VAR1/VAR2+VAR3/VAR2)成交量(手),否则返回0 输出主力控盘: VAR4的10日累和/10000, COLORSTICK ''' VAR1=CLOSE-LOW VAR2=HIGH-LOW VAR3=CLOSE-HIGH VAR4=IF(HIGH>LOW,(VAR1/VAR2+VAR3/VAR2)VOL,0) CYW=SUM(VAR4,10)/10000 #COLORSTICK return CYW #************************************************* #*************************************************** #其他系 def PEAK(CLOSE,N,n=1): ''' 计算倾效 np.polyfit(range(N),x,deg=1) ''' pass def TROUGH(CLOSE,N,n=1): ''' 箱底 ''' pass def XT(CLOSE): ''' 箱体 ''' 箱顶=PEAK(CLOSE,N,1)0.98 箱底=TROUGH(CLOSE,N,1)1.02 箱高=100(箱顶-箱底)/箱底,#NODRAW def MOD(M,N): ''' 计算模 M/N的余数 ''' return M//N def SQJZ(CLSOE): ''' N赋值:到最后交易的周期 B赋值:收盘价<4日前的收盘价 T1赋值: 条件连续成立次数 A_B1赋值:(T1>9) AND T1关于9的模=1 A_B2赋值:(T1>9) AND T1关于9的模=2 A_B8赋值:(T1>9) AND T1关于9的模=8 A_B9赋值:(T1>9) AND T1关于9的模=0 B1赋值:(N=6 AND 5日后的(平滑处理)统计6日中满足B的天数=6) OR (N=7 AND 6日后的(平滑处理)统计7日中满足B的天数=7) OR (N=8 AND 7日后的(平滑处理)统计8日中满足B的天数=8) OR (N>=9 AND 8日后的(平滑处理)统计9日中满足B的天数=9) 当满足条件B1AND(1日前的B=0ORA_B1)时,在最低价位置书写数字,画洋红色 B2赋值:(N=5 AND 4日后的(平滑处理)统计6日中满足B的天数=6) OR (N=6 AND 5日后的(平滑处理)统计7日中满足B的天数=7) OR (N=7 AND 6日后的(平滑处理)统计8日中满足B的天数=8) OR (N>=8 AND 7日后的(平滑处理)统计9日中满足B的天数=9) 当满足条件B2AND(2日前的B=0ORA_B2)时,在最低价位置书写数字,画洋红色 B8赋值:(N=1 AND 统计8日中满足B的天数=8) OR (N>=2 AND 1日后的(平滑处理)统计9日中满足B的天数=9) 当满足条件B8AND(8日前的B=0ORA_B8)时,在最低价位置书写数字,画洋红色 B9赋值:(N>=1 AND 统计9日中满足B的天数=9) 当满足条件B9AND(9日前的B=0ORA_B9)时,在最低价位置书写数字,画红色 S赋值:收盘价>4日前的收盘价 T2赋值: 条件连续成立次数 A_S1赋值:(T2>9) AND T2关于9的模=1 A_S2赋值:(T2>9) AND T2关于9的模=2 A_S8赋值:(T2>9) AND T2关于9的模=8 A_S9赋值:(T2>9) AND T2关于9的模=0 S1赋值:(N=6 AND 5日后的(平滑处理)统计6日中满足S的天数=6) OR (N=7 AND 6日后的(平滑处理)统计7日中满足S的天数=7) OR (N=8 AND 7日后的(平滑处理)统计8日中满足S的天数=8) OR (N>=9 AND 8日后的(平滑处理)统计9日中满足S的天数=9) 当满足条件S1AND(1日前的S=0ORA_S1)时,在最高价位置书写数字,画洋红色,显示在位置之上 S2赋值:(N=5 AND 4日后的(平滑处理)统计6日中满足S的天数=6) OR (N=6 AND 5日后的(平滑处理)统计7日中满足S的天数=7) OR (N=7 AND 6日后的(平滑处理)统计8日中满足S的天数=8) OR (N>=8 AND 7日后的(平滑处理)统计9日中满足S的天数=9) 当满足条件S2AND(2日前的S=0ORA_S2)时,在最高价位置书写数字,画洋红色,显示在位置之上 S8赋值:(N=1 AND 统计8日中满足S的天数=8) OR (N>=2 AND 1日后的(平滑处理)统计9日中满足S的天数=9) 当满足条件S8AND(8日前的S=0ORA_S8)时,在最高价位置书写数字,画洋红色,显示在位置之上 S9赋值:(N>=1 AND 统计9日中满足S的天数=9) 当满足条件S9AND(9日前的S=0ORA_S9)时,在最高价位置书写数字,画绿色,显示在位置之上 C=CLOSE N=CURRBARSCOUNT() B=C<REF(C,4) T1= BARSLASTCOUNT(B) A_B1=IF(T1>=9,1,None) A_B2=IF(T1>9,2,None) A_B8=IF(T1>9,8,None) A_B9=IF(T1>9,0,None) B1:=(N=6 AND REFXV(COUNT(B,6),5)=6) OR (N=7 AND REFXV(COUNT(B,7),6)=7) OR (N=8 AND REFXV(COUNT(B,8),7)=8) OR (N>=9 AND REFXV(COUNT(B,9),8)=9); DRAWNUMBER(B1 AND (REF(B,1)=0 OR A_B1),L,1),COLORMAGENTA; B2:=(N=5 AND REFXV(COUNT(B,6),4)=6) OR (N=6 AND REFXV(COUNT(B,7),5)=7) OR (N=7 AND REFXV(COUNT(B,8),6)=8) OR (N>=8 AND REFXV(COUNT(B,9),7)=9); DRAWNUMBER(B2 AND(REF(B,2)=0 OR A_B2),L,2),COLORMAGENTA; B8:=(N=1 AND COUNT(B,8)=8) OR (N>=2 AND REFXV(COUNT(B,9),1)=9); DRAWNUMBER(B8 AND (REF(B,8)=0 OR A_B8),L,8),COLORMAGENTA; B9:=(N>=1 AND COUNT(B,9)=9); DRAWNUMBER(B9 AND (REF(B,9)=0 OR A_B9),L,9),COLORRED; S:=C>REF(C,4); T2:= BARSLASTCOUNT(S); A_S1:=(T2>9) AND MOD(T2,9)=1; A_S2:=(T2>9) AND MOD(T2,9)=2; A_S8:=(T2>9) AND MOD(T2,9)=8; A_S9:=(T2>9) AND MOD(T2,9)=0; S1:=(N=6 AND REFXV(COUNT(S,6),5)=6) OR (N=7 AND REFXV(COUNT(S,7),6)=7) OR (N=8 AND REFXV(COUNT(S,8),7)=8) OR (N>=9 AND REFXV(COUNT(S,9),8)=9); DRAWNUMBER(S1 AND (REF(S,1)=0 OR A_S1),H,1),COLORMAGENTA,DRAWABOVE; S2:=(N=5 AND REFXV(COUNT(S,6),4)=6) OR (N=6 AND REFXV(COUNT(S,7),5)=7) OR (N=7 AND REFXV(COUNT(S,8),6)=8) OR (N>=8 AND REFXV(COUNT(S,9),7)=9); DRAWNUMBER(S2 AND (REF(S,2)=0 OR A_S2),H,2),COLORMAGENTA,DRAWABOVE; S8:=(N=1 AND COUNT(S,8)=8) OR (N>=2 AND REFXV(COUNT(S,9),1)=9); DRAWNUMBER(S8 AND (REF(S,8)=0 OR A_S8),H,8),COLORMAGENTA,DRAWABOVE; S9:=(N>=1 AND COUNT(S,9)=9); DRAWNUMBER(S9 AND (REF(S,9)=0 OR A_S9),H,9),COLORGREEN,DRAWABOVE; ''' pass def JAX(CLOSE,HIGH,LOW,N=30): ''' 济安线 AA赋值:(2收盘价+最高价+最低价)/4-收盘价的N日简单移动平均的绝对值/收盘价的N日简单移动平均 输出济安线:以AA为权重(2收盘价+最低价+最高价)/4的动态移动平均,线宽为3,画洋红色 CC赋值:(收盘价/济安线) MA1赋值:CC(2收盘价+最高价+最低价)/4的3日简单移动平均 MAAA赋值:((MA1-济安线)/济安线)/3 TMP赋值:MA1-MAAAMA1 输出J:如果TMP<=济安线,返回济安线,否则返回无效数,线宽为3,画青色 输出A:TMP,线宽为2,画棕色 输出X:如果TMP<=济安线,返回TMP,否则返回无效数,线宽为2,画绿色 ''' AA=ABS((2CLOSE+HIGH+LOW)/4-MA(CLOSE,N))/MA(CLOSE,N) data=pd.DataFrame() data['数据']=(2CLOSE+LOW+HIGH)/4 #alpha中值0.5 济安线=data['数据'].ewm(alpha=0.5, adjust=True).mean()#LINETHICK3,COLORMAGENTA CC=(CLOSE/济安线) MA1=MA(CC*(2CLOSE+HIGH+LOW)/4,3) MAAA=((MA1-济安线)/济安线)/3 TMP=MA1-MAAAMA1 J=IF(TMP<=济安线,济安线,None)#LINETHICK3,COLORCYAN A=TMP#LINETHICK2,COLORBROWN X=IF(TMP<=济安线,TMP,None)#LINETHICK2,COLORGREEN return J,A,X def XJDX(CLOSE,HIGH,LOW): ''' 超级短线 VAR1赋值:(2收盘价+最高价+最低价)/4 VAR2赋值:VAR1的4日指数移动平均的4日指数移动平均的4日指数移动平均 输出J: (VAR2-1日前的VAR2)/1日前的VAR2100, COLORSTICK 输出D: J的3日简单移动平均 输出K: J的1日简单移动平均 ''' VAR1=(2CLOSE+HIGH+LOW)/4 VAR2=EMA(EMA(EMA(VAR1,4),4),4) J=(VAR2-REF(VAR2,1))/REF(VAR2,1)100# COLORSTICK D=MA(J,3) K= MA(J,1) return J,D,K def ZJTJ(CLOSE): ''' 庄家抬轿 获利盘，和成本函数需要写 VAR1赋值:收盘价的9日指数移动平均的9日指数移动平均 控盘赋值:(VAR1-1日前的VAR1)/1日前的VAR11000 当满足条件控盘<0时,在控盘和0位置之间画柱状线,宽度为1,0不为0则画空心柱.,画白色 A10赋值:控盘上穿0 输出无庄控盘:如果控盘<0,返回控盘,否则返回0,画白色,NODRAW 输出开始控盘:如果A10,返回5,否则返回0,线宽为1,画棕色 当满足条件控盘>1日前的控盘AND控盘>0时,在控盘和0位置之间画柱状线,宽度为1,0不为0则画空心柱.,画红色 输出有庄控盘:如果控盘>1日前的控盘AND控盘>0,返回控盘,否则返回0,画红色,NODRAW VAR2赋值:100以收盘价0.95计算的获利盘比例 当满足条件VAR2>50ANDCOST(85)<CLOSEAND控盘>0时,在控盘和0位置之间画柱状线,宽度为1,0不为0则画空心柱.,COLORFF00FF 输出高度控盘:如果VAR2>50ANDCOST(85)<CLOSEAND控盘>0,返回控盘,否则返回0,COLORFF00FF,NODRAW 当满足条件控盘<1日前的控盘AND控盘>0时,在控盘和0位置之间画柱状线,宽度为1,0不为0则画空心柱.,COLOR00FF00 输出主力出货:如果控盘<1日前的控盘AND控盘>0,返回控盘,否则返回0,COLOR00FF00,NODRAW ''' VAR1=EMA(EMA(CLOSE,9),9) 控盘=(VAR1-REF(VAR1,1))/REF(VAR1,1)1000 #STICKLINE(控盘<0,控盘,0,1,0),COLORWHITE; A10=CROSS(控盘,0) 无庄控盘=IF(控盘<0,控盘,0)#COLORWHITE,NODRAW; 开始控盘=IF(A10,1,0)#LINETHICK1,COLORBROWN; #STICKLINE(控盘>REF(控盘,1) AND 控盘>0,控盘,0,1,0),COLORRED; 有庄控盘=IF(np.logical_and(控盘>REF(控盘,1),控盘>0),控盘,0)#COLORRED,NODRAW; #VAR2=100WINNER(CLOSE0.95) #STICKLINE(VAR2>50 AND COST(85)<CLOSE AND 控盘>0,控盘,0,1,0),COLORFF00FF; #高度控盘:IF(VAR2>50 AND COST(85)<CLOSE AND 控盘>0,控盘,0),COLORFF00FF,NODRAW; #STICKLINE(控盘<REF(控盘,1) AND 控盘>0,控盘,0,1,0),COLOR00FF00; 主力出货=IF(np.logical_and(控盘<REF(控盘,1),控盘>0),控盘,0)#COLOR00FF00,NODRAW; return 无庄控盘,开始控盘,有庄控盘,主力出货 def ZBCD(HIGH,LOW,OPEN,AMOUNT,VOL,CLOSE,N=10): ''' 准备抄底 VAR1赋值:成交额(元)/成交量(手)/7 VAR2赋值:(3最高价+最低价+开盘价+2收盘价)/7 VAR3赋值:成交额(元)的N日累和/VAR1/7 VAR4赋值:以成交量(手)/VAR3为权重VAR2的动态移动平均 输出抄底:(收盘价-VAR4)/VAR4100,画淡洋红色 当满足条件-7.0上穿抄底时,在抄底位置画1号图标 ''' VAR1=AMOUNT/VOL/7 VAR2=(3HIGH+LOW+OPEN+2CLOSE)/7 VAR3=SUM(AMOUNT,N)/VAR1/7 VAR4=DMA(VAR2,VOL/VAR3) 抄底=(CLOSE-VAR4)/VAR4100#COLORLIMAGENTA #DRAWICON(CROSS(-7.0,抄底),抄底,1) return 抄底 def BDZX(HIGH,LOW,CLOSE): ''' 波段之星 VAR2赋值:(最高价+最低价+收盘价2)/4 VAR3赋值:VAR2的21日指数移动平均 VAR4赋值:VAR2的21日估算标准差 VAR5赋值:((VAR2-VAR3)/VAR4100+200)/4 VAR6赋值:(VAR5的5日指数移动平均-25)1.56 输出AK: VAR6的2日指数移动平均1.22 输出AD1: AK的2日指数移动平均 输出AJ: 3AK-2AD1 输出AA:100 输出布林极限:0 输出CC:80 输出买进: 如果AK上穿AD1,返回58,否则返回20 输出卖出: 如果AD1上穿AK,返回58,否则返回20 ''' VAR2=(HIGH+LOW+CLOSE2)/4 VAR3=EMA(VAR2,21) VAR4=STD(VAR2,21) VAR5=((VAR2-VAR3)/VAR4100+200)/4 VAR6=(EMA(VAR5,5)-25)1.56 AK= EMA(VAR6,2)1.22 AD1= EMA(AK,2) AJ= 3AK-2AD1 AA=100 BB=0 CC=80 买进= IF(CROSS(AK,AD1),58,20) 卖出= IF(CROSS(AD1,AK),58,20) return AK,AD1,AJ,AA,BB,CC,买进,卖出 def LHXJ(HIGH,LOW,CLOSE): ''' 猎狐先觉 VAR1赋值:(收盘价2+最高价+最低价)/4 VAR2赋值:VAR1的13日指数移动平均-VAR1的34日指数移动平均 VAR3赋值:VAR2的5日指数移动平均 输出主力弃盘: (-2)(VAR2-VAR3)3.8 输出主力控盘: 2(VAR2-VAR3)3.8 ''' VAR1=(CLOSE2+HIGH+LOW)/4 VAR2=EMA(VAR1,13)-EMA(VAR1,34) VAR3=EMA(VAR2,5) 主力弃盘=(-2)(VAR2-VAR3)3.8 主力控盘=2(VAR2-VAR3)3.8 return 主力弃盘,主力控盘 def LYJH(CLOSE,HIGH,LOW,M=80,M1=50): ''' 猎鹰歼狐 VAR1赋值:(36日内最高价的最高值-收盘价)/(36日内最高价的最高值-36日内最低价的最低值)100 输出机构做空能量线: VAR1的2日[1日权重]移动平均 VAR2赋值:(收盘价-9日内最低价的最低值)/(9日内最高价的最高值-9日内最低价的最低值)100 输出机构做多能量线: VAR2的5日[1日权重]移动平均-8 输出LH: M 输出LH1: M1 ''' VAR1=(HHV(HIGH,36)-CLOSE)/(HHV(HIGH,36)-LLV(LOW,36))100 机构做空能量线=SMA(VAR1,2,1) VAR2=(CLOSE-LLV(LOW,9))/(HHV(HIGH,9)-LLV(LOW,9))100 机构做多能量线=SMA(VAR2,5,1)-8 LH=M LH1=M1 return 机构做空能量线,机构做多能量线,LH,LH1 def JFZX(OPEN,CLOSE,VOL,N=30): ''' 飓风智能中线 VAR2赋值:如果收阳线,返回成交量(手),否则返回0的N日累和/成交量(手)的N日累和100 VAR3赋值:100-如果收阳线,返回成交量(手),否则返回0的N日累和/成交量(手)的N日累和100 输出多头力量: VAR2 输出空头力量: VAR3 输出多空平衡: 50 ''' VAR2=SUM(IF(CLOSE>OPEN,VOL,0),N)/SUM(VOL,N)100 VAR3=100-SUM(IF(CLOSE>OPEN,VOL,0),N)/SUM(VOL,N)100 多头力量= VAR2 空头力量= VAR3 多空平衡= 50 return 多头力量,空头力量,多空平衡 def CYHT(CLOSE,HIGH,LOW,OPEN): ''' 财运亨通 VAR1赋值:(2收盘价+最高价+最低价+开盘价)/5 输出高抛: 80 VAR2赋值:34日内最低价的最低值 VAR3赋值:34日内最高价的最高值 输出SK: (VAR1-VAR2)/(VAR3-VAR2)100的13日指数移动平均 输出SD: SK的3日指数移动平均 输出低吸: 20 输出强弱分界: 50 VAR4赋值:如果SK上穿SD,返回40,否则返回22 VAR5赋值:如果SD上穿SK,返回60,否则返回78 输出卖出: VAR5 输出买进: VAR4 ''' VAR1=(2CLOSE+HIGH+LOW+OPEN)/5 高抛= 80 VAR2=LLV(LOW,34) VAR3=HHV(HIGH,34) SK= EMA((VAR1-VAR2)/(VAR3-VAR2)100,13) SD= EMA(SK,3) 低吸= 20 强弱分界= 50 VAR4=IF(CROSS(SK,SD),40,22) VAR5=IF(CROSS(SD,SK),60,78) 卖出= VAR5 买进= VAR4 return 高抛,SK,SD,低吸,强弱分界,卖出,买进 def BSQJ(CLOSE): ''' 买卖区间 买线赋值:收盘价的2日指数移动平均 卖线赋值:收盘价的21日线性回归斜率20+收盘价的42日指数移动平均 当满足条件买线>=卖线时,在日期日0日内最高价的最高值和日期日0日内最低价的最低值位置之间画柱状线,宽度为6,0不为0则画空心柱.,COLOR001050 当满足条件买线<卖线时,在日期日0日内最高价的最高值和日期日0日内最低价的最低值位置之间画柱状线,宽度为6,0不为0则画空心柱.,COLOR404050 K线 指导赋值:(收盘价的4日指数移动平均+收盘价的6日指数移动平均+收盘价的12日指数移动平均+收盘价的24日指数移动平均)/4的2日指数移动平均 界赋值:收盘价的27日简单移动平均 输出B买:如果指导上穿界ORCROSS(买线,卖线),返回收盘价,否则返回无效数,画洋红色,NODRAW 输出持仓:如果买线>=卖线,返回收盘价,否则返回无效数,画红色,NODRAW 输出S卖:如果界上穿指导ORCROSS(卖线,买线),返回收盘价,否则返回无效数,画淡灰色,NODRAW 输出空仓:如果买线<卖线,返回收盘价,否则返回无效数,画绿色,NODRAW 当满足条件买线上穿卖线时,在最低价位置画1号图标 当满足条件卖线上穿买线时,在最高价位置画2号图标 ''' C=CLOSE 买线=EMA(C,2) 卖线=EMA(SLOPE(C,21)20+C,42) #STICKLINE(买线>=卖线,REFDATE(HHV(H,0),DATE),REFDATE(LLV(L,0),DATE),6,0),COLOR001050 #STICKLINE(买线<卖线,REFDATE(HHV(H,0),DATE),REFDATE(LLV(L,0),DATE),6,0),COLOR404050; #DRAWKLINE(H,O,L,C); 指导=EMA((EMA(CLOSE,4)+EMA(CLOSE,6)+EMA(CLOSE,12)+EMA(CLOSE,24))/4,2) 界=MA(CLOSE,27) B买=IF(np.logical_or(CROSS(指导,界),CROSS(买线,卖线)),C,None)#COLORMAGENTA,NODRAW; 持仓=IF(买线>=卖线,C,None)#COLORRED,NODRAW S卖=IF(np.logical_or(CROSS(界,指导),CROSS(卖线,买线)),C,None)#COLORLIGRAY,NODRAW 空仓=IF(买线<卖线,C,None)#COLORGREEN,NODRAW #DRAWICON(CROSS(买线,卖线),L,1); #DRAWICON(CROSS(卖线,买线),H,2); return B买,持仓,S卖,空仓 def CDP_STD(CLOSE, HIGH, LOW): ''' 逆势操作 CH赋值:1日前的最高价 CL赋值:1日前的最低价 CC赋值:1日前的收盘价 输出CDP:(CH+CL+CC)/3 输出AH:2CDP+CH-2CL 输出NH:CDP+CDP-CL 输出NL:CDP+CDP-CH 输出AL:2CDP-2CH+CL ''' CH = REF(HIGH, 1) CL = REF(LOW, 1) CC = REF(CLOSE, 1) CDP = (CH + CL + CC) / 3 AH = 2 * CDP + CH - 2 * CL NH = CDP + CDP - CL NL = CDP + CDP - CH AL = 2 * CDP - 2 * CH + CL return CDP, AH, NH, NL, AL def TBP_STD(HIGH,LOW,CLOSE): ''' 趋势平衡点 APX赋值:(最高价+最低价+收盘价)/3 TR0赋值:最高价-最低价和最高价-1日前的收盘价的绝对值和最低价-1日前的收盘价的绝对值的较大值的较大值 MF0赋值:收盘价-2日前的收盘价 MF1赋值:1日前的MF0 MF2赋值:2日前的MF0 DIRECT1赋值:上次MF0>MF1ANDMF0>MF2距今天数 DIRECT2赋值:上次MF0<MF1ANDMF0<MF2距今天数 DIRECT0赋值:如果DIRECT1<DIRECT2,返回100,否则返回-100 输出TBP:1日前的1日前的收盘价+如果DIRECT0>50,返回MF0和MF1的较小值,否则返回MF0和MF1的较大值 输出多头获利:1日前的如果DIRECT0>50,返回APX2-最低价,否则返回无效数,NODRAW 输出多头停损:1日前的如果DIRECT0>50,返回APX-TR0,否则返回无效数,NODRAW 输出空头回补:1日前的如果DIRECT0<-50,返回APX2-最高价,否则返回无效数,NODRAW 输出空头停损:1日前的如果DIRECT0<-50,返回APX+TR0,否则返回无效数,NODRAW ''' H=HIGH L=LOW C=CLOSE APX=(H+L+C)/3 TR0=MAX(H-L,MAX(ABS(H-REF(C,1)),ABS(L-REF(C,1)))) MF0=C-REF(C,2) MF1=REF(MF0,1) MF2=REF(MF0,2) DIRECT1=BARSLAST(np.logical_and(MF0>MF1,MF0>MF2)) DIRECT2=BARSLAST(np.logical_and(MF0<MF1,MF0<MF2)) DIRECT0=IF(DIRECT1<DIRECT2,100,-100) TBP=REF(REF(C,1)+IF(DIRECT0>50,MIN(MF0,MF1),MAX(MF0,MF1)),1) 多头获利=REF(IF(DIRECT0>50,APX2-L,None),1) 多头停损=REF(IF(DIRECT0>50,APX-TR0,None),1) 空头回补=REF(IF(DIRECT0<-50,APX2-H,None),1) 空头停损=REF(IF(DIRECT0<-50,APX+TR0,None),1) return TBP,多头获利,多头停损,空头回补,空头停损 #*********************************************** #*********************************************** #有空写************
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