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 asideまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.
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AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance.
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Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks "which alphas exist", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.
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A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报，预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示，并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股，不预测财务造假。
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Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.
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Diagnose failed or underperforming backtests, locate the root cause, and fix the issue
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