Original paper

SSRN-id3081165.pdf603.7KB

Abstract

An abundance of academic evidence and theory exists on the efficacy and intuition behind momentum investing, yet a limited number of studies discuss the feasibility of running momentum portfolios in practice. And no study to date has directly analyzed implementation costs for a live momentum portfolio.

As a result, many are still quick to dismiss momentum as difficult or costly to implement because of its high turnover. In this paper, we use seven years of live data to evaluate the implementability of momentum investing. We show that live momentum portfolios are capable of capturing the momentum premium, even after accounting for expenses, estimated trading costs, taxes, and other frictions associated with real-life portfolios.

Keywords: Factors, styles, implementation, trading costs, taxes, momentum

Trading rules

Concentrate on the top quintile of stocks from NYSE, NASDAQ, and AMEX
Select top 20% stocks with best 12-month momentum (performance)
Construct an equally weighted portfolio
Adjust the portfolio every month

Python code

Backtrader

import backtrader as bt
import backtrader.feeds as btfeeds
import datetime

class Momentum(bt.Strategy): 
    params = (
        ('momentum_period', 252),
        ('rebalance_interval', 21),
        ('num_stocks', 0.2),
    )

    def __init__(self): 
        self.add_timer(
            bt.timer.SESSION_END, monthdays=[1], monthcarry=True
        )  # Rebalance monthly
        self.inds = {}
        for d in self.datas: 
            self.inds[d] = bt.indicators.ROC(d.close, period=self.params.momentum_period)

    def prenext(self): 
        self.next()

    def notify_timer(self, timer, when, *args, **kwargs): 
        if self._getminperstatus() < 0: 
            return
        stocks = list(self.datas)
        stocks.sort(key=lambda d: self.inds[d][0], reverse=True)
        selected_stocks = stocks[:int(len(stocks) * self.params.num_stocks)]
        for d in self.datas: 
            target = self.broker.getvalue() * (1 / len(selected_stocks)) if d in selected_stocks else 0
            self.order_target_value(d, target)

cerebro = bt.Cerebro()

# Add data for NYSE, NASDAQ, and AMEX stocks
# Replace 'NYSE.csv', 'NASDAQ.csv', and 'AMEX.csv' with appropriate files
for market in ['NYSE.csv', 'NASDAQ.csv', 'AMEX.csv']:
    data = btfeeds.GenericCSVData(
        dataname=market,
        fromdate=datetime.datetime(2000, 1, 1),
        todate=datetime.datetime(2020, 12, 31),
        dtformat=('%Y-%m-%d'),
        openinterest=-1,
        nullvalue=0.0,
        plot=False,
    )
    cerebro.adddata(data)

cerebro.broker.setcash(1000000.0)
cerebro.broker.setcommission(commission=0.001)  # 0.1% commission
cerebro.addstrategy(Momentum)

print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
results = cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot()

Make sure to replace ‘NYSE.csv’, ‘NASDAQ.csv’, and ‘AMEX.csv’ with the appropriate files containing the stock data for the mentioned exchanges. This code uses the Backtrader framework to implement the trading strategy as described.

Implementing Momentum: What Have We Learned?

Original paper

Abstract

Trading rules

Python code

Backtrader