Original paper
Abstract
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:Â Historical Data, Stock Return Seasonality, January Effect, Seasonal Anomalies, Sell in May, Halloween Indicator, Tax Loss Selling
Trading rules
- Begin January by acquiring small-cap equities.
- Retain holdings in large-cap equities for the entire year.
Python code
Backtrader
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[0])
# Buy small-cap stocks
self.buy(data=self.datas[1])
else:
if self.is_january:
self.is_january = False
# Sell all small-cap stocks
self.sell(data=self.datas[1])
# Buy large-cap stocks
self.buy(data=self.datas[0])
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.