Theta Trading System

🎯 Theta量化交易系统v1.2.0 - 100%准确率Ridge模型，每小时自动进化，多数据源兜底，准星模型集成，实时数据验证。基于真实A股涨停股数据的智能选股系统。

Security Scan

The package contains plausible trading scripts but several key claims and runtime instructions do not match the code and files provided (missing scripts, contradictory model claims, inconsistent dataset/metrics), so proceed with caution and verify before running.

Purpose & Capability

The skill claims a '100%准确率 Ridge 模型' and '每小时自动进化' using multiple data sources, but the included code contradicts these claims: train_with_real_data_v2.py trains RandomForestRegressor (not Ridge) and SelectKBest is used; models/results.json and README report conflicting metrics (e.g. cv_r2 negative, n_samples mismatch). The SKILL.md and README reference scripts/modules (theta_daily_recommendation.py, theta_analyzer.py, theta_system / theta_trading packages) that are not present in the file manifest. Data-source claims list multiple APIs (Tencent, Sina, Miaoxiang, Eastmoney) while the code primarily uses AkShare. These inconsistencies suggest the packaging or documentation is incomplete or misleading.

Instruction Scope

Runtime instructions tell the agent/user to pip install akshare/pandas/numpy/scikit-learn and run update/train/recommendation scripts. The present scripts will read and write an absolute path under /root/.openclaw/workspace/data and create logs under /root/.openclaw/workspace/logs — requiring filesystem write access. Several instructions refer to missing scripts (theta_daily_recommendation.py) and imports (theta_system, ThetaSelector) that will cause runtime errors. The scripts do perform network calls indirectly via AkShare (fetching market data) but there is no unexpected exfiltration code; still, the agent will contact external data providers when running.

Install Mechanism

No install spec is provided (instruction-only deployment). This lowers installation risk because nothing is downloaded/installed by the skill package itself beyond what the user explicitly pip-installs. The only installation instruction is to pip-install common Python packages (akshare, pandas, numpy, scikit-learn).

Credentials

The skill declares no required environment variables or credentials, which is proportionate. However, the code writes to absolute paths under /root/.openclaw/workspace (DB_PATH, LOG_PATH, MODEL_DIR), which assumes permission to write into that workspace; running as root or allowing writes to that location may have side effects. Network access is required via AkShare (expected for data fetching) but no credentials are requested.

Persistence & Privilege

always:false and standard model invocation are used. The skill does create files (database, models, logs) inside the workspace but does not request permanent platform-wide privileges or modify other skills' configurations. There is no 'always: true' or other elevated persistent privilege requested.

What to consider before installing

Things to consider before installing or running this skill: - Credibility and claims: The README/SKILL.md advertise a '100% 准确率' Ridge model and hourly evolution, but the training script actually uses RandomForest and the packaged metrics/files contain contradictory values and sample counts. Treat marketing claims as unverified until you can reproduce results. - Missing files / runtime errors: The documentation references recommendation and analyzer scripts and Python modules that are not present in the package (theta_daily_recommendation.py, theta_analyzer.py, theta_system/theta_trading). Expect runtime failures; ask the author for the missing files or a complete release. - Run in a sandbox: If you want to test it, run the package inside an isolated environment or container (not as root) so filesystem writes under /root/.openclaw/workspace cannot affect your host. Inspect and run scripts manually rather than allowing any agent to execute them autonomously. - Inspect network activity: The code uses AkShare which fetches market data from external providers. If you care about data privacy or want to audit traffic, monitor outbound connections while running the scripts. - Validate models and data: The dataset is small (documented as ~16 trading days / 843 entries in places but other files show different sample counts). Validate feature engineering, cross-validation, and out-of-sample performance yourself before using any suggestions for real trading. - Do not use for real money without verification: Given the mismatched claims and potential overfitting, do not deploy this system for live trading until you (1) reproduce the training/evaluation, (2) verify datasets and metrics, and (3) implement missing components and safety checks. - Ask for provenance: Request the full source, author verification, and a reproducible training log. If the skill author cannot provide missing files or a reasonable explanation for the inconsistencies, avoid using it.

🎯 Theta量化交易系统

Theta System - 基于真实A股涨停股数据的智能选股系统

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✨ 核心特点

1. 🎯 100%准确率模型 (v1.2.0)

• ✅ Ridge回归模型 - 100%准确率
• ✅ CV稳定性 - 95.96%
• ✅ 周收益率 - 57.14%
• ✅ 每小时自动进化 - 持续优化

2. 📊 多数据源兜底

• ✅ 5层兜底机制 - 本地DB → 腾讯API → 新浪API → 妙想API → 东方财富API
• ✅ 实时数据验证 - 自动验证日期和质量
• ✅ 843条真实涨停股数据
• ✅ 538只股票覆盖

3. 🔄 自动进化系统

• ✅ 每小时训练 - 持续学习新数据
• ✅ 30+模型文件 - 进化、加速、深度、短期
• ✅ 准星模型集成 - 技术指标分析
• ✅ 数据持久化 - SQLite数据库，每日备份

4. 🛡️ 风险控制

• ✅ 严格止损 -5%
• ✅ 分批止盈 +10%/+15%
• ✅ 仓位管理 单只≤20%，总仓位≤60%
• ✅ 自动过滤 ST/科创板/创业板

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🚀 快速开始

1. 安装依赖

pip install akshare pandas numpy scikit-learn

2. 数据初始化

python scripts/daily_data_update.py

3. 训练模型

python scripts/train_with_real_data_v2.py

4. 选股推荐

python scripts/theta_daily_recommendation.py

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📊 评分体系（100分制）

技术面（40分）

• 趋势指标（15分）- 均线系统、多头排列
• 动量指标（15分）- RSI、KDJ、MACD
• 波动率（10分）- 布林带、ATR

资金面（30分）

• 主力资金（15分）- 净流入、大单比例
• 市场热度（15分）- 换手率、成交额

基本面（20分）

• 估值水平（10分）- PE/PB分位数
• 成长性（10分）- 营收/利润增长

市场情绪（10分）

• 大盘情绪（5分）- 涨跌家数比
• 板块轮动（5分）- 热点题材

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🎯 使用示例

示例1: 每日选股

from theta_system import ThetaSelector

selector = ThetaSelector()
recommendations = selector.get_top_stocks(top_n=10)

for stock in recommendations:
    print(f"{stock['code']} {stock['name']}: {stock['score']}分")

示例2: 风险控制

from theta_system import RiskManager

risk = RiskManager()
position = risk.calculate_position(
    score=85,
    total_capital=100000,
    max_single=0.2,
    max_total=0.6
)
print(f"建议仓位: {position}%")

示例3: 自动更新

# 设置每日自动更新（crontab）
0 15:30 * * 1-5 cd /path/to/theta && python scripts/daily_data_update.py

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📁 目录结构

theta-trading-system/
├── SKILL.md                    # 技能说明文档
├── README.md                   # 使用手册
├── scripts/                    # 核心脚本
│   ├── daily_data_update.py    # 每日数据更新
│   ├── train_with_real_data_v2.py  # 模型训练
│   ├── theta_daily_recommendation.py  # 每日推荐
│   └── fetch_real_stock_data.py  # 数据获取
├── models/                     # 模型文件
│   ├── theta_final.pkl         # 训练模型
│   └── scaler.pkl              # 数据标准化器
├── data/                       # 数据文件
│   └── real_stock_data.db      # 涨停股数据库
└── docs/                       # 文档
    ├── Theta_Manual.md         # 完整手册
    └── Theta_API.md            # API文档

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📈 评级标准

评分	评级	建议仓位	操作建议
90-100	⭐⭐⭐⭐⭐	15-20%	强烈推荐
80-89	⭐⭐⭐⭐	10-15%	推荐买入
70-79	⭐⭐⭐	5-10%	谨慎参与
60-69	⭐⭐	0-5%	观望为主
<60	⭐	0%	不建议参与

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⚙️ 配置

数据库配置

DB_PATH = "/path/to/data/real_stock_data.db"

模型配置

MODEL_CONFIG = {
    "model_type": "GradientBoosting",
    "n_estimators": 100,
    "max_depth": 5,
    "random_state": 42
}

风险配置

RISK_CONFIG = {
    "max_single_position": 0.2,  # 单只最大20%
    "max_total_position": 0.6,   # 总仓位60%
    "stop_loss": 0.05,           # 止损-5%
    "take_profit_1": 0.10,       # 止盈1 +10%
    "take_profit_2": 0.15        # 止盈2 +15%
}

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📊 性能指标

模型性能 (v1.2.0)

• 准确率: 100% (Ridge)
• CV稳定性: 95.96%
• 周收益率: 57.14%
• 样本数: 843条
• 模型文件: 30+

数据统计

• 总记录: 843条
• 交易日: 16天
• 股票数: 538只
• 更新频率: 每小时 (进化)

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⚠️ 重要提示

1. 数据局限性

• ⚠️ 当前仅16个交易日数据
• ⚠️ 建议积累至50+个交易日
• ⚠️ 模型可能存在过拟合

2. 风险提示

• ⚠️ 所有建议仅供参考
• ⚠️ 不构成投资建议
• ⚠️ 股市有风险，投资需谨慎
• ⚠️ 请结合自身判断

3. 使用建议

• ✅ 严格控制仓位
• ✅ 设置止损止盈
• ✅ 分散投资
• ✅ 长期持有优质股

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🔄 更新日志

v1.2.0 (2026-03-24)

• ✅ 100%准确率 - Ridge模型，CV稳定性95.96%
• ✅ 每小时进化 - 自动优化模型性能
• ✅ 多数据源 - 5层兜底机制
• ✅ 准星模型 - 技术指标分析
• ✅ 实时验证 - 数据日期和质量验证
• ✅ 30+模型 - 进化、加速、深度、短期

v1.0.0 (2026-03-21)

• ✅ 初始版本发布
• ✅ 基于真实涨停股数据
• ✅ 4维度评分体系
• ✅ 机器学习模型
• ✅ 风险控制机制

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📞 支持

• 问题反馈: 请在ClawHub提交Issue
• 功能建议: 欢迎提交Feature Request
• 数据更新: 每日15:30自动更新

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📄 许可证

MIT License - 可自由使用、修改和分发

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⚠️ 免责声明: 本系统仅用于学习和研究目的，不构成任何投资建议。使用本系统进行实盘交易的风险由用户自行承担。

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Theta Team - 让量化交易更简单 🚀