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| 名称 |
季节性与日历效应策略 |
| 描述 |
季节性与日历效应策略，基于月份效应、星期效应等时间模式生成交易信号，适用于任意 OHLCV 数据。 category: strategy |
Seasonal / Calendar Effect Strategy
Purpose
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.
Signal Logic
Month Effect (Default)
- Specified bullish months → go long
- Specified bearish months → go short / stay out
- All other months → stay flat
Day-of-Week Effect (Optional Overlay)
- Monday / Friday effects
- Start-of-month / end-of-month effects
Combined Mode
Month signal × weekday signal; open a position only when both confirm.
Common Calendar Effects Reference
| 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] |
Parameters
| 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 |
Common Pitfalls
pd.DatetimeIndex.month starts from 1 (1=January)pd.DatetimeIndex.weekday starts from 0 (0=Monday, 4=Friday)- Seasonal strategies are statistical regularities, not deterministic signals, so pay attention to sample size in backtests
- Neutral months (neither in
bullish nor bearish) should output 0 and must not be skipped
Dependencies
pip install pandas numpy
Signal Convention
1 = long (bullish window), -1 = short (bearish window), 0 = stand aside