Recognize classic candlestick patterns using pandas, a Python library for tabular data. Combine bullish and bearish pattern scores into long, short, or stand-aside signals, with adjustable thresholds for candle bodies and shadows.
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Candlestick pattern recognition engine, pure pandas vectorized implementation of 15 classic candlestick patterns (5 single-candle + 5 double-candle + 4 triple-candle + 1 trend confirmation), generating a composite signal from bullish/bearish pattern scores.
安装前可以查看交给 AI 代理的指令内容。
Identifies 15 classic candlestick patterns and generates trading signals:
| Pattern | Signal | Description |
|---|---|---|
| Hammer | Bullish | Long lower shadow with a small body at the top |
| Inverted Hammer | Bullish | Long upper shadow with a small body at the bottom |
| Shooting Star | Bearish | Long upper shadow with a small body at the bottom (appears after an uptrend) |
| Doji | Neutral | Open and close are nearly equal |
| Spinning Top | Neutral | Small body with roughly equal upper and lower shadows |
| Pattern | Signal |
|---|---|
| Bullish Engulfing | Bullish |
| Bearish Engulfing | Bearish |
| Bullish Harami | Bullish |
| Bearish Harami | Bearish |
| Piercing Line | Bullish |
| Dark Cloud Cover | Bearish |
| Pattern | Signal |
|---|---|
| Morning Star | Bullish |
| Evening Star | Bearish |
| Three White Soldiers | Bullish |
| Three Black Crows | Bearish |
Bullish patterns score +1, bearish patterns score -1. Go long when the total score is > 0, go short when it is < 0, and stand aside when it equals 0.
| Parameter | Default | Description |
|---|---|---|
| body_pct | 0.1 | Threshold for body-to-range ratio in a doji |
| shadow_ratio | 2.0 | Ratio of shadow length to body length |
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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