Analyze price and volume data using trend indicators, overbought and oversold measures, and volume changes. Combine these three perspectives into a single long, short, or stand-aside signal, with adjustable calculation periods and thresholds.
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Core technical indicator collection (trend EMA/ADX + mean-reversion BB/RSI + volume-price OBV/volume ratio), generates a composite signal via three-dimensional voting. Pure pandas implementation for any OHLCV data.
安装前可以查看交给 AI 代理的指令内容。
Combines three classic Western technical analysis approaches into one composite signal engine:
| Dimension | Indicators | Purpose |
|---|---|---|
| Trend | EMA(12/26) + ADX(14) | Determine direction and trend strength |
| Mean reversion | Bollinger Bands(20,2) + RSI(14) | Detect overbought and oversold conditions |
| Volume-price | OBV + volume ratio | Confirm volume participation |
Three-dimensional voting mechanism:
ewm(alpha=1/period)), not a rolling mean(volume * sign(close.diff())).cumsum()All parameters have default values and can be overridden at instantiation time:
| Parameter | Default | Description |
|---|---|---|
| ema_fast | 12 | Fast EMA period |
| ema_slow | 26 | Slow EMA period |
| adx_period | 14 | ADX calculation period |
| adx_threshold | 25.0 | ADX trend-strength threshold |
| bb_window | 20 | Bollinger Band window |
| bb_std | 2.0 | Bollinger Band standard deviation multiplier |
| rsi_period | 14 | RSI period |
| rsi_oversold | 30 | RSI oversold threshold |
| rsi_overbought | 70 | RSI overbought threshold |
| vol_ma_period | 20 | Volume moving-average period |
| obv_ma_period | 20 | OBV moving-average period |
pip install pandas numpy requests
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Compare a Chinese company's US depositary receipts, Hong Kong shares, and mainland shares after adjusting for exchange rates and share ratios. Analyze pricing gaps, valuation differences, and delisting risks to support research into its different listings.
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Retrieve stock prices, futures quotes, exchange rates, and economic indicators with the free Python library AKShare. The skill explains market-specific functions, symbol formats, and data preparation for analysis and testing strategies against historical data.
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Explore bundled libraries of investment factors, signals used to compare securities, and inspect individual formulas and metadata. Evaluate a factor or an entire library across a chosen universe and period, producing aggregate performance statistics and an HTML report.
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Assess whether a Chinese A-share company may receive ST or *ST risk warnings next financial year. Review financial forecasts, dividends, audit opinions, and regulatory penalties, then report risk severity separately from confidence in the prediction.
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Plan portfolio allocations across assets using risk, correlations, return forecasts, and trading costs. Compare five optimization methods, produce settings for config.json, and define rebalancing rules that adjust holdings back toward their target weights.
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Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.
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