Turn-of-the-Month Time-Series Momentum Futures Strategy

The strategy shorts USD via TWI futures during FOMC meetings, applies 10:1 leverage, and remains neutral otherwise, targeting USD volatility around key economic announcements.

ASSET CLASS: CFDs, ETFs, futures | REGION: United States | FREQUENCY:
Daily | MARKET: currencies | KEYWORD: Federal Open Market Committee ,Meeting Effect , US Dollar

I. STRATEGY IN A NUTSHELL

This strategy shorts the USD against a basket of global currencies using USD TWI futures, trading only on the eight annual FOMC meeting days. A 10:1 leverage is applied, with neutral positions maintained outside these events to limit exposure.

II. ECONOMIC RATIONALE

FOMC announcement days create uncertainty about future monetary policy, prompting investors to demand a risk premium. This leads to higher expected foreign exchange returns during these announcement periods.

III. SOURCE PAPER

Policy Announcements in FX Markets [Click to Open PDF]

Philippe Mueller, London School of Economics; Paolo Porchia, Instituto Empresa; Andrea Vedolin, London School of Economics

<Abstract>

A strategy that is short the US dollar and long the rest of the world earns large excess returns on days of scheduled meetings of the Federal Open Market Committee (FOMC). Moreover, the difference between announcement and non announcement returns becomes larger during periods of high uncertainty and bad economic conditions. To reconcile our findings, we develop a model of an international long-run risk economy in which asset prices respond to revisions of monetary policy. Monetary policy uncertainty commands a risk premium that is larger in weaker economic conditions. A calibrated version is consistent with the cross-sectional pattern of currency risk premia observed in the data.

IV. BACKTEST PERFORMANCE

Annualised Return	7.71%
Volatility	13.78%
Beta	0.004
Sharpe Ratio	0.56
Sortino Ratio	-0.328
Maximum Drawdown	N/A
Win Rate	56%

V. FULL PYTHON CODE

from AlgorithmImports import *
from pandas.tseries.offsets import BDay
class FederalOpenMarketCommitteeMeetingEffectUSDollar(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2004, 1, 1)
        self.set_cash(100_000)
        
        # fed days
        csv_string_file: str = self.download('data.quantpedia.com/backtesting_data/economic/fed_days.csv')
        dates: List[str] = csv_string_file.split('\r\n')
        dates: List[datetime.date] = [(datetime.strptime(x, "%Y-%m-%d") - BDay(1)).date() for x in dates]
        # import futures data 
        data: Security = self.add_data(QuantpediaFutures, "ICE_DX1", Resolution.DAILY)
        data.set_fee_model(CustomFeeModel())
        self.symbol: Symbol = data.symbol
        self.close_next_day: bool = False
        self.schedule.on(self.date_rules.on(dates), self.time_rules.at(0, 0), self.day_before_fed)
        self.schedule.on(self.date_rules.every_day(self.symbol), self.time_rules.at(0, 0), self.rebalance)
    
    def day_before_fed(self) -> None:
        if self.time.date() >= QuantpediaFutures.get_last_update_date()[self.symbol.value]:
            return
        if not self.portfolio[self.symbol].is_short:
            self.set_holdings(self.symbol, -1)
            
    def rebalance(self) -> None:
        if self.close_next_day:
            self.liquidate(self.symbol)
            self.close_next_day = False
        if self.portfolio[self.symbol].is_short:
            self.close_next_day = True
# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))
        
# Quantpedia data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
    _last_update_date: Dict[str, datetime.date] = {}
    @staticmethod
    def get_last_update_date() -> Dict[str, datetime.date]:
       return QuantpediaFutures._last_update_date
    def GetSource(self, config:SubscriptionDataConfig, date:datetime, isLiveMode:bool) -> SubscriptionDataSource:
        return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
    def Reader(self, config:SubscriptionDataConfig, line:str, date:datetime, isLiveMode:bool) -> BaseData:
        data = QuantpediaFutures()
        data.Symbol = config.Symbol
        
        if not line[0].isdigit(): return None
        split = line.split(';')
        
        data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
        data['back_adjusted'] = float(split[1])
        data['spliced'] = float(split[2])
        data.Value = float(split[1])
        # store last update date
        if config.Symbol.Value not in QuantpediaFutures._last_update_date:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
        if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
        return data

VI. Backtest Performance