Original paper
Abstract
This paper investigates a link between the most popular currency strategies (carry trade, momentum, value) and sovereign ratings. I document that the profitability of the momentum strategy is large and significant among higher credit risk currencies, but is nonexistent among lower credit risk currencies. The profitability of currency momentum disappears when currencies rated BBB- or worse (16% of currency months) are excluded from the sample. The country credit risk conditions do not apply to the carry trade and value, which are profitable among lower and higher credit risk currencies. Sovereign rating changes do not have a significant impact on the performance of the most popular currency strategies.
Keywords:Â exchange rates, sovereign ratings, strategies, momentum
Trading rules
- Use 63 currency pairs relative to USD.
- Adopt a 4-week duration for both holding and look-back periods.
- Select the top 20% of currencies showing the highest lagged excess returns.
- Select the bottom 20% of currencies demonstrating the lowest lagged excess returns.
- Take long positions on currencies showing the biggest gains versus USD.
- Adopt short positions on currencies showing the biggest declines 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.