A Share Trader

A股核心交易框架，整合12种选股策略的自适应量化交易系统

Security Scan

The package appears to be a coherent A‑share quantitative trading framework, but it asks for 'data access and trading permissions' without declaring required credentials or showing how broker/API keys are handled — this mismatch and the presence of live-data interfaces warrants caution.

Purpose & Capability

Name/description match the included code: core engine, 12 strategies, data interfaces and quick-starts are present. Requiring stock data and trading permissions is consistent with a trading framework. However, the SKILL.md lists '需要股票数据访问和交易权限' but the registry metadata declares no required env vars or primary credential — that is disproportionate for a real trading skill which normally needs broker/API tokens and data-provider keys.

Instruction Scope

SKILL.md instructs an agent to 'run the trading framework' for simulation/backtest/real trading and lists data needs (real-time quotes, fundamentals, optional market sentiment). The instructions do not enumerate what secrets or endpoints will be used, or constrain the agent to paper mode. The included code contains a RealAShareDataInterface that tries to import xtquant/tushare (real data sources) and quick_start/optimized scripts that can initialize real-data connectors; running the skill in live mode could attempt network access and (potentially) order placement. The SKILL.md gives the agent broad permission to 'execute' without explicitly requiring or declaring the precise credentials it will use.

Install Mechanism

No install spec; code files are bundled in the skill. That means nothing is downloaded at install time beyond the shipped files (lower install-time risk). The code will import third‑party Python packages (numpy, pandas, xtquant, tushare, etc.) at runtime, but the skill does not perform external downloads during install.

Credentials

Registry shows no required env vars or primary credential, but the code uses real-data interfaces that typically require provider tokens (tushare token is explicitly mentioned in a comment) and may require brokerage API credentials for live trading. The omission of declared credential requirements is a coherence gap — a trading skill should list the exact credentials it will use and request only those.

Persistence & Privilege

always:false and disable-model-invocation:false (defaults). The skill does not request forced/always-on presence. The code writes local logs (a_share_trader.log) and persists runtime state to in‑memory structures; there is no evidence in the visible files that it modifies other skills or system-wide agent settings.

What to consider before installing

This package is a full trading framework and appears to run both simulated and (optionally) real-data workflows. Before installing or running: - Treat it as potentially network‑enabled: run only in 'paper' or isolated environment until you audit the code that executes trades. The quick_start and quick_start_optimized scripts initialize data connectors and may trigger network calls. - Do not provide real brokerage or data-provider credentials until you have reviewed the parts of core/a_share_trader.py and any execution/ordering modules (files truncated in the listing) to confirm where and how credentials are used. - Expect to need tokens such as tushare/xtquant or broker API keys; the skill currently does not declare these env vars — ask the author to document required env vars (e.g., TUSHARE_TOKEN, XTQUANT credentials, broker API keys) and why each is needed. - Run the system locally in paper/backtest mode first, inspect log file (a_share_trader.log), and run the provided tests before any live trading. - If you plan to use live trading, review all code paths that place orders or call external libs to ensure credentials are not accidentally exfiltrated and to confirm the skill only calls expected broker/data APIs. Confidence is limited by truncated/omitted files in the package listing; if the remaining files show explicit broker SDK calls or remote endpoints and a lack of explicit credential handling, that would raise the concern level. If instead the full code clearly confines itself to simulation and mocks external calls (and documents required credentials for optional connectors), the assessment could shift toward benign.

A股核心交易框架

概述

基于用户提供的12种选股策略，结合自适应交易引擎，构建的A股量化交易系统。系统具备市场状态识别、策略动态切换、风险自适应控制等高级功能。

整合策略列表

1. 基本面选股策略 - 质量+增长双因子
2. 防御选股策略 - 高股息价值投资
3. 震荡选股策略 - KDJ技术指标
4. 小市值策略 - 月度切换的小市值轮动
5. 基本面加小市值 - 质量小市值混合
6. 小市值成长 - 小市值成长股精选
7. 营收利润双增 - 增长质量策略
8. 价值成长 - 价值成长平衡
9. 控盘策略 - 筹码集中度选股
10. 社保重仓 - 机构跟随策略
11. 超跌反弹 - 均值回归策略
12. 时空共振策略 - 四维度综合模型

核心特性

• 自适应市场感知：识别牛熊市、震荡市等不同市场状态
• 策略动态权重：根据不同市场环境自动调整策略权重
• 智能参数调整：基于市场状态动态调整策略阈值
• 多维度风控：个股、行业、组合多层风控
• 学习优化机制：从交易绩效中持续学习优化

系统架构

A股核心交易框架/
├── core/                  # 核心引擎
│   ├── a_share_trader.py  # 主交易引擎
│   ├── adaptive_engine.py # 自适应引擎
│   └── data_interface.py  # A股数据接口
├── strategies/            # 策略模块
│   ├── __init__.py
│   ├── base_strategy.py   # 策略基类
│   ├── fundamental.py     # 基本面策略
│   ├── defensive.py       # 防御策略
│   ├── swing_trading.py   # 震荡策略
│   ├── small_cap.py       # 小市值策略
│   ├── quality_small_cap.py # 质量小市值
│   ├── small_cap_growth.py # 小市值成长
│   ├── revenue_profit.py  # 营收利润双增
│   ├── value_growth.py    # 价值成长
│   ├── chip_concentration.py # 控盘策略
│   ├── social_security.py # 社保重仓
│   ├── oversold_rebound.py # 超跌反弹
│   └── resonance.py       # 时空共振策略
├── risk_management/       # 风险管理
│   ├── core.py            # 风控核心
│   ├── position.py        # 仓位控制
│   └── stop_loss.py       # 止损策略
├── config/                # 配置
│   └── trading_config.py  # 交易配置
├── utils/                 # 工具函数
├── tests/                 # 测试
└── examples/              # 使用示例

调用方式

在智能体提示中直接请求执行此技能，例如：

"请运行A股交易框架进行模拟交易" "使用A股框架分析当前市场状态" "执行A股策略回测"

数据需求

• A股实时行情数据
• 基本面数据（财务指标）
• 技术指标数据
• 市场情绪数据（可选）

输出示例

{
  "status": "running",
  "market_state": "weak_bull",
  "state_confidence": 0.72,
  "active_strategies": 8,
  "portfolio_value": 10000000000,
  "total_return": 0.15,
  "max_drawdown": 0.08,
  "active_positions": 12
}

注意事项

1. 系统为量化交易框架，实盘交易需谨慎
2. 需要可靠的数据源支持
3. 建议先进行充分的回测和模拟交易
4. 注意市场风险，控制仓位和回撤