Original paper
Abstract
This paper studies the option-like behavior of popular momentum strategies implemented in foreign exchange markets. The results confirm those of Daniel and Moskowitz (2013) in finding strong option-like behavior for both momentum measures, based on the cumulative return from 12 and 6 months prior to the formation date to one month prior to the formation date. Surprisingly, there is no such evidence for the popular momentum strategy accounting for a one-month formation period.
Keywords:Â asset pricing, international financial markets, momentum crash, foreign exchange markets, currency markets.
Trading rules
- Focus on 63 currency pairs relative to USD.
- 4-week holding and look-back periods.
- Identify the top 20% winner currencies showcasing the greatest prior excess returns.
- Identify the bottom 20% loser currencies exhibiting the least prior excess returns.
- Establish long positions in currencies showing the most appreciation relative to USD.
- Set up short positions in currencies demonstrating the most depreciation against USD.
Python code
Backtrader
import backtrader as bt
class OneMonthCurrencyMomentum(bt.Strategy):
params = (
("lookback_period", 4 * 5), # 4 weeks, assuming 5 trading days per week
("holding_period", 4 * 5),
("portfolio_size", 0.2), # Top and bottom 20% of currencies
)
def __init__(self):
self.currency_data = self.datas[1:] # Ignore the first data feed (USD)
self.order_dict = dict()
def next(self):
if self.datetime.weekday() == 0: # Execute strategy on Mondays
if len(self.data) < self.p.lookback_period:
return
# Calculate returns for each currency
currency_returns = [
(data, (data.close[0] - data.close[-self.p.lookback_period]) / data.close[-self.p.lookback_period])
for data in self.currency_data
]
# Sort currencies by returns
sorted_currencies = sorted(currency_returns, key=lambda x: x[1], reverse=True)
# Determine the number of currencies to trade
num_currencies = int(len(sorted_currencies) * self.p.portfolio_size)
# Close existing positions
for data, order in self.order_dict.items():
if order and order.status in [order.Accepted, order.Submitted]:
self.cancel(order)
elif data in self.positions:
self.close(data)
# Enter long positions for top currencies
for data, return_ in sorted_currencies[:num_currencies]:
self.order_dict[data] = self.buy(data)
# Enter short positions for bottom currencies
for data, return_ in sorted_currencies[-num_currencies:]:
self.order_dict[data] = self.sell(data)
# Example usage with sample data feeds
if __name__ == "__main__":
cerebro = bt.Cerebro()
cerebro.addstrategy(OneMonthCurrencyMomentum)
# Add data feeds (assuming they are in a list called 'datafeeds')
for data in datafeeds:
cerebro.adddata(data)
cerebro.broker.set_cash(10000)
cerebro.run()Please note that this code assumes you have already prepared a list of data feeds (datafeeds) for the 63 currencies against USD. You can use Backtrader’s GenericCSVData or any other supported data format to load the data. The code provided is just the implementation of the strategy itself.