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