A Stock Data

A股数据查询技能。当用户询问中国A股股价、行情、K线、财务数据、实时行情、历史数据、涨停板、选股分析时使用。支持股票代码查询（如000001、600000）、股票名称查询。

Security Scan

The skill's code, instructions, and requirements are consistent with an A‑share data/analysis/charting tool that uses AkShare and TA‑Lib; nothing in the bundle appears to try to access unrelated secrets or exfiltrate data.

Purpose & Capability

The name/description (A股数据查询) match the SKILL.md and Python scripts: they call AkShare for market/historical data, compute indicators with TA‑Lib, and render charts. The included scripts are directly relevant to the stated purpose.

Instruction Scope

SKILL.md instructs use of AkShare and TA‑Lib and contains example API calls limited to publicly available market data. It does perform network fetches via AkShare (expected). The instructions do not ask the agent to read unrelated system files or credentials. Note: SKILL.md is missing an explicit 'pip install akshare/matplotlib/pandas' line (it only mentions pip install ta-lib).

Install Mechanism

There is no install specification (instruction-only), which minimizes installer risk. The README suggests installing ta‑lib via pip; AkShare and plotting libs are used but not explicitly listed for installation. TA‑Lib can be nontrivial to install on some systems (binary dependency), so the skill may fail to run without extra setup, but this is not a malicious install mechanism.

Credentials

The skill requests no environment variables or credentials (appropriate). It does require network access to fetch market data via AkShare — this is expected but not declared. Both scripts save output to a hardcoded Windows path (C:\Users\Administrator\.openclaw\workspace\601390_chart.png), which is odd for cross‑platform agents and may cause failures or overwrite files; it is not credential access but is a small privacy/operational concern.

Persistence & Privilege

always:false and no special persistence or modifications to other skills or system settings. The skill only writes chart files into a workspace path (its own output), which is normal behavior for a charting utility.

Assessment

This skill is coherent for fetching and analyzing A‑share data, but check a few things before installing: - Be prepared to allow network access (AkShare fetches data from the internet). - Ensure Python dependencies are installed (akshare, pandas, matplotlib, and TA‑Lib). TA‑Lib may require system packages or binary wheels. - The scripts save charts to a hardcoded Windows Administrator path — change it if you run on Linux/macOS or under a different user to avoid failures or accidental overwrites. - Review AkShare as a third‑party data source (it aggregates data from various endpoints). If you need strict provenance or an enterprise data source, confirm AkShare is acceptable. - If you need higher assurance, run the included scripts in a sandboxed environment first to observe network calls and file writes.

A股数据查询技能

基于 AkShare 提供完整的 A 股数据查询能力，支持实时行情、历史K线、财务数据、技术指标分析和选股策略。

使用场景

✅ 自动触发，当用户说：

• "股价"、"股票行情"、"涨停板"、"涨跌幅"
• "K线"、"历史数据"、"分时图"
• "财务数据"、"财报"、"市盈率"
• "A股"、"上证"、"深证"
• "茅台股价"、"腾讯控股"（股票名称）
• "000001"、"600000"（股票代码）
• "MACD金叉"、"RSI超卖"、"均线多头发"

数据源

• AkShare: 免费开源财经数据接口，覆盖 A股、港股、美股、基金、期货、宏观经济

快速命令

实时行情数据

沪深京 A 股全部

import akshare as ak

# 方式1：东方财富实时行情（推荐，更快）
stock_zh_a_spot_em_df = ak.stock_zh_a_spot_em()

# 方式2：新浪财经实时行情
stock_zh_a_spot_df = ak.stock_zh_a_spot()

沪 A 股

# 沪 A 股实时行情
stock_sh_a_spot_em_df = ak.stock_sh_a_spot_em()

深 A

# 深 A 股实时行情
stock_sz_a_spot_em_df = ak.stock_sz_a_spot_em()

北交所

# 北交所实时行情
stock_bj_a_spot_em_df = ak.stock_bj_a_spot_em()

新股

# 新股实时行情
stock_new_a_spot_em_df = ak.stock_new_a_spot_em()

创业板

# 创业板实时行情
stock_cy_a_spot_em_df = ak.stock_cy_a_spot_em()

科创板

# 科创板实时行情
stock_kc_a_spot_em_df = ak.stock_kc_a_spot_em()

历史 K线

日 K线

# 东方财富接口（推荐）
stock_zh_a_hist_df = ak.stock_zh_a_hist(
    symbol="000001",
    period="daily",  # daily/weekly/monthly
    start_date="20240101",
    end_date="20240331",
    adjust="qfq"     # qfq前复权/hfq后复权/""不复权
)

# 新浪财经接口
stock_zh_a_hist_df = ak.stock_zh_a_hist(
    symbol="sz000001",
    start_date="19910403",
    end_date="20210327"
)

周 K线

stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol="000001", period="weekly")

月 K线

stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol="000001", period="monthly")

股票信息

获取所有股票代码和名称

stock_info_a_code_name_df = ak.stock_info_a_code_name()

个股详细信息

# 东方财富
stock_individual_info_em_df = ak.stock_individual_info_em(symbol="茅台")

# 雪球
stock_individual_basic_info_xq_df = ak.stock_individual_basic_info_xq(symbol="SH600519")

# 雪球历史K线
stock_individual_spot_xq_df = ak.stock_individual_spot_xq(symbol="SH600519")

财务数据

财务指标

stock_financial_analysis_indicator_df = ak.stock_financial_analysis_indicator(
    stock="600519", 
    symbol="财务指标"
)

资产负债表

stock_balance_sheet_by_yearly_em_df = ak.stock_balance_sheet_by_yearly_em(symbol="600519")

利润表

stock_profit_sheet_by_reportly_em_df = ak.stock_profit_sheet_by_reportly_em(symbol="600519")

现金流量表

stock_cash_flow_sheet_by_reportly_em_df = ak.stock_cash_flow_sheet_by_reportly_em(symbol="600519")

市场数据

指数历史

# 上证指数
index_zh_a_hist_df = ak.index_zh_a_hist(
    symbol="sh000001", 
    period="daily"
)

# 深证指数
index_sz_a_hist_df = ak.index_sz_a_hist(
    symbol="sz399001", 
    period="daily"
)

资金流向与龙虎榜

龙虎榜-营业部

stock_individual_em_xq_df = ak.stock_individual_em_xq(symbol="SH600519")

龙虎榜-统计

# 需要东财账号
# stock_user_individual_info_em()
# stock_user_statistics_em()

板块数据

强势股池

stock_pool_em_df = ak.stock_pool_em()

涨停股池

stock_pool_em_df = ak.stock_pool_em()

股票市场总貌

上交所

stock_sse_summary_df = ak.stock_sse_summary()

深交所

# 证券类别统计
stock_szse_summary_df = ak.stock_szse_summary(date="20250619")

# 地区交易排序
stock_szse_area_summary_df = ak.stock_szse_area_summary(date="20250619")

返回格式

实时行情字段

字段	说明
代码	股票代码
名称	股票名称
最新价	当前价格
涨跌幅	百分比
涨跌额	绝对值
成交量(手)	成交量
成交额	成交金额
昨收	昨日收盘价
今开	今日开盘价
最高	今日最高价
最低	今日最低价
振幅	波动幅度
换手率	换手率
市盈率-动态	动态市盈率
总市值	总市值
流通市值	流通市值

K线字段

字段	说明
日期	交易日期
开盘	开盘价
收盘	收盘价
最高	最高价
最低	最低价
成交量	成交量
成交额	成交金额
振幅	振幅
涨跌幅	涨跌幅度
涨跌额	涨跌额
换手率	换手率

技术指标分析

安装依赖

pip install ta-lib

常用指标

均线系统（MA）

import talib
import numpy as np

close = np.array(df['收盘'], dtype=float)
df['MA5'] = talib.MA(close, timeperiod=5)
df['MA10'] = talib.MA(close, timeperiod=10)
df['MA20'] = talib.MA(close, timeperiod=20)
df['MA60'] = talib.MA(close, timeperiod=60)

MACD 指标

macd, signal, hist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
df['MACD'] = macd
df['MACD_SIGNAL'] = signal
df['MACD_HIST'] = hist

RSI 指标

df['RSI_6'] = talib.RSI(close, timeperiod=6)
df['RSI_12'] = talib.RSI(close, timeperiod=12)
df['RSI_24'] = talib.RSI(close, timeperiod=24)

KDJ 指标

high = np.array(df['最高'], dtype=float)
low = np.array(df['最低'], dtype=float)

k, d = talib.STOCH(high, low, close, fastk_period=9, slowk_period=3, slowd_period=3)
df['KDJ_K'] = k
df['KDJ_D'] = d
df['KDJ_J'] = 3 * k - 2 * d

布林带（BOLL）

upper, middle, lower = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2)
df['BOLL_UPPER'] = upper
df['BOLL_MIDDLE'] = middle
df['BOLL_LOWER'] = lower

成交量指标

volume = np.array(df['成交量'], dtype=float)

df['VOL_MA5'] = talib.MA(volume, timeperiod=5)
df['VOL_MA10'] = talib.MA(volume, timeperiod=10)

df['VOL_RATIO'] = df['成交量'] / df['VOL_MA5']

选股策略

策略 1：均线金叉

# MA5 上穿 MA20
df['SIGNAL_MA_GOLD'] = (df['MA5'] > df['MA20']) & (df['MA5'].shift(1) <= df['MA20'].shift(1))

signals = df[df['SIGNAL_MA_GOLD'] == True]

策略 2：MACD 金叉

# MACD 上穿 Signal
df['SIGNAL_MACD_GOLD'] = (df['MACD'] > df['MACD_SIGNAL']) & \
                              (df['MACD'].shift(1) <= df['MACD_SIGNAL'].shift(1))

signals = df[df['SIGNAL_MACD_GOLD'] == True]

策略 3：RSI 超卖

# RSI < 30 超卖
df['SIGNAL_RSI_OVERSOLD'] = df['RSI_6'] < 30

signals = df[df['SIGNAL_RSI_OVERSOLD'] == True]

策略 4：布林带突破

# 价格突破上轨
df['SIGNAL_BOLL_BREAK'] = df['收盘'] > df['BOLL_UPPER']

signals = df[df['SIGNAL_BOLL_BREAK'] == True]

策略 5：综合多因子

# 多条件选股
df['SIGNAL_MULTI'] = (
    (df['MA5'] > df['MA20']) &  # 趋势向上
    (df['RSI_6'] > 50) &           # 不超卖
    (df['MACD'] > df['MACD_SIGNAL']) &  # MACD 金叉
    (df['VOL_RATIO'] > 1.5)         # 放量
)

signals = df[df['SIGNAL_MULTI'] == True]

批量选股流程

import akshare as ak
import talib
import pandas as pd

# 1. 获取所有 A 股列表
stock_list = ak.stock_zh_a_spot_em()

# 2. 遍历计算指标
results = []
for index, row in stock_list.iterrows():
    code = row['代码']
    name = row['名称']
    
    # 获取历史数据
    df = ak.stock_zh_a_hist(symbol=code, period="daily")
    
    if df.empty:
        continue
    
    # 计算技术指标
    close = np.array(df['收盘'], dtype=float)
    
    # 均线
    ma5 = talib.MA(close, timeperiod=5)[-1]
    ma20 = talib.MA(close, timeperiod=20)[-1]
    
    # RSI
    rsi6 = talib.RSI(close, timeperiod=6)[-1]
    
    # MACD
    macd, signal, _ = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
    macd_val = macd[-1]
    signal_val = signal[-1]
    
    # 判断选股条件（示例：均线多头 + RSI 不超卖 + MACD 金叉）
    if ma5 > ma20 and rsi6 > 50 and macd_val > signal_val:
        results.append({
            '代码': code,
            '名称': name,
            '现价': row['最新价'],
            'MA5': ma5,
            'MA20': ma20,
            'RSI': rsi6,
            'MACD': macd_val,
            '涨跌幅': row['涨跌幅']
        })

# 3. 输出结果
result_df = pd.DataFrame(results)
print(result_df.sort_values('涨跌幅', ascending=False))

涨停板查询

import akshare as ak

# 获取实时行情
df = ak.stock_zh_a_spot_em()

# 筛选涨停板（涨跌幅 >= 9.9%）
df_zt = df[df['涨跌幅'] >= 9.9].sort_values('涨跌幅', ascending=False)

# 筛选跌停板（涨跌幅 <= -9.9%）
df_dt = df[df['涨跌幅'] <= -9.9].sort_values('涨跌幅', ascending=True)

print(f"涨停板数量: {len(df_zt)}")
print(f"跌停板数量: {len(df_dt)}")
print(df_zt[['代码','名称','最新价','涨跌幅','成交额']].head(20))

复权说明

股票数据复权类型：

类型	说明	适用场景
不复权（""）	原始价格	查看历史走势
前复权（qfq）	历史价格调整，当前价格不变	看盘、技术分析
后复权（hfq）	当前价格不变，历史价格调整	收益率计算

股票代码规则

市场	代码格式	示例
上交所	6xxxxx	600000, 601318
深交所	0xxxxx	000001, 300059
北交所	8xxxxx	8xxxxx

使用示例

查询个股实时行情

用户：查询贵州茅台的股价
响应：使用 stock_zh_a_spot_em() 查询 600519

查询历史 K线

用户：获取平安银行最近 30 天的 K线
响应：使用 stock_zh_a_hist() 查询 000001，指定日期范围

查询涨停板

用户：今天有哪些股票涨停
响应：使用 stock_zh_a_spot_em() 筛选涨跌幅 >= 9.9%

查询财务数据

用户：腾讯的市盈率是多少
响应：使用 stock_financial_analysis_indicator() 查询

技术分析选股

用户：帮我找出 MA5 上穿 MA20 的股票
响应：计算均线指标，筛选金叉信号

用户：RSI 超卖的有哪些
响应：计算 RSI，筛选 RSI < 30

用户：MACD 金叉且放量的股票
响应：计算 MACD 和成交量，综合筛选

用户：多因子选股：趋势向上 + MACD 金叉 + RSI>50
响应：多条件综合筛选

错误处理

常见错误及处理：

错误类型	可能原因	解决方法
KeyError	股票代码不存在或输入错误	检查代码并重试
TimeoutError	网络超时	重试或检查连接
EmptyDataError	当天无数据（非交易日）	确认是否交易日

注意事项

1. 频率限制: 避免频繁请求，建议缓存结果
2. 数据延迟: 实时数据可能有 1-5 分钟延迟
3. 复权处理: 查询历史数据时注意复权方式选择
4. 代码规范: 6 位数字代码，补齐前导 0（如 1 → 000001）

支持的数据范围

• ✅ A 股实时行情（沪深京、沪深北、沪深深）
• ✅ B 股实时行情
• ✅ 港股实时行情
• ✅ 美股实时行情
• ✅ 创业板、科创板、新股
• ✅ 历史 K线数据（日、周、月）
• ✅ 财务数据（资产负债表、利润表、现金流量表）
• ✅ 技术指标分析（MA、MACD、RSI、KDJ、BOLL）
• ✅ 龙虎榜、资金流向
• ✅ 板块数据、概念股
• ✅ 指数数据
• ✅ 市场总貌统计

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