Over 300 years of UK stock returns reveal that well-known monthly seasonals are sample specific. For instance, the January effect only emerges around 1830. Most months have had their 50 years of fame, showing the importance of long time series to safeguard against sample selection bias, noise, and data snooping. The overall conclusion is that monthly seasonals might simply be in the eye of the beholder.
Keywords: Historical Data, Stock Return Seasonality, January Effect, Seasonal Anomalies, Sell in May, Halloween Indicator, Tax Loss Selling
- Begin January by acquiring small-cap equities.
- Retain holdings in large-cap equities for the entire year.
import backtrader as bt class JanuaryEffect(bt.Strategy): def __init__(self): self.is_january = False def next(self): if self.data.datetime.date().month == 1: if not self.is_january: self.is_january = True # Sell all large-cap stocks self.sell(data=self.datas) # Buy small-cap stocks self.buy(data=self.datas) else: if self.is_january: self.is_january = False # Sell all small-cap stocks self.sell(data=self.datas) # Buy large-cap stocks self.buy(data=self.datas) cerebro = bt.Cerebro() cerebro.addstrategy(JanuaryEffect) # Add large-cap stocks data large_cap_data = bt.feeds.GenericCSVData(...) cerebro.adddata(large_cap_data) # Add small-cap stocks data small_cap_data = bt.feeds.GenericCSVData(...) cerebro.adddata(small_cap_data) cerebro.run()
This code snippet assumes that you have set up Backtrader, and have the data feeds prepared for both large-cap and small-cap stocks. Make sure to replace the placeholders in the data feed creation with your specific data file and parameters.