Generates buy, sell, or stand-aside signals from calendar patterns using price and volume data. Supports configurable months and weekdays, including combined conditions, for testing seasonal trading ideas against historical data.
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Seasonal/calendar-effect strategy. Generates trading signals from time-based patterns such as month-of-year effects and day-of-week effects. Suitable for any OHLCV data.
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Uses time-based regularities in financial markets (month effects, day-of-week effects, and similar patterns) to generate trading signals. Examples include the China A-share "spring rally" (January-March) and the "sell in May" effect.
Month signal × weekday signal; open a position only when both confirm.
| Effect | Description | Reference Configuration |
|---|---|---|
| Spring rally | Higher probability of gains in China A-shares from January to March | bullish_months=[1,2,3] |
| Sell in May | Weaker performance from May to October | bearish_months=[5,6,7,8,9,10] |
| Year-end effect | Institutional rebalancing in December | bullish_months=[11,12] |
| Monday effect | Lower returns on Mondays | bearish_weekdays=[0] |
| Friday effect | Higher returns on Fridays | bullish_weekdays=[4] |
| Parameter | Default | Description |
|---|---|---|
| bullish_months | [1, 2, 3, 11, 12] | Bullish months |
| bearish_months | [5, 6, 7, 8, 9] | Bearish months |
| use_weekday | False | Whether to enable weekday effects |
| bullish_weekdays | [4] | Bullish weekdays (0=Monday, 4=Friday) |
| bearish_weekdays | [0] | Bearish weekdays |
pd.DatetimeIndex.month starts from 1 (1=January)pd.DatetimeIndex.weekday starts from 0 (0=Monday, 4=Friday)bullish nor bearish) should output 0 and must not be skippedpip install pandas numpy
1 = long (bullish window), -1 = short (bearish window), 0 = stand aside아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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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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