Overall Statistics |
Total Trades 1736 Average Win 0.39% Average Loss -0.57% Compounding Annual Return -0.504% Drawdown 41.100% Expectancy -0.012 Net Profit -10.306% Sharpe Ratio -0.017 Probabilistic Sharpe Ratio 0.000% Loss Rate 41% Win Rate 59% Profit-Loss Ratio 0.68 Alpha -0.001 Beta -0.007 Annual Standard Deviation 0.076 Annual Variance 0.006 Information Ratio -0.392 Tracking Error 0.193 Treynor Ratio 0.193 Total Fees $1138.73 Estimated Strategy Capacity $0 Lowest Capacity Asset CME_SF1.QuantpediaFutures 2S |
# https://quantpedia.com/strategies/currency-momentum-factor/ # # Create an investment universe consisting of several currencies (10-20). Go long three currencies with the highest 12-month momentum against USD # and go short three currencies with the lowest 12-month momentum against USD. Cash not used as margin invest on overnight rates. Rebalance monthly. import data_tools class CurrencyMomentumFactor(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.data = {} self.period = 12 * 21 self.SetWarmUp(self.period) self.symbols = [ "CME_AD1", # Australian Dollar Futures, Continuous Contract #1 "CME_BP1", # British Pound Futures, Continuous Contract #1 "CME_CD1", # Canadian Dollar Futures, Continuous Contract #1 "CME_EC1", # Euro FX Futures, Continuous Contract #1 "CME_JY1", # Japanese Yen Futures, Continuous Contract #1 "CME_MP1", # Mexican Peso Futures, Continuous Contract #1 "CME_NE1", # New Zealand Dollar Futures, Continuous Contract #1 "CME_SF1" # Swiss Franc Futures, Continuous Contract #1 ] for symbol in self.symbols: data = self.AddData(data_tools.QuantpediaFutures, symbol, Resolution.Daily) data.SetFeeModel(data_tools.CustomFeeModel(self)) data.SetLeverage(5) self.data[symbol] = self.ROC(symbol, self.period, Resolution.Daily) self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.AfterMarketOpen(self.symbols[0]), self.Rebalance) def Rebalance(self): sorted_by_performance = sorted([x for x in self.data.items() if x[1].IsReady], key = lambda x: x[1].Current.Value, reverse = True) long = [x[0] for x in sorted_by_performance[:3]] short = [x[0] for x in sorted_by_performance[-3:]] # Trade execution. invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested] for symbol in invested: if symbol not in long + short: self.Liquidate(symbol) for symbol in long: self.SetHoldings(symbol, 1 / len(long)) for symbol in short: self.SetHoldings(symbol, -1 / len(short))
# Custom fee model class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD")) # Quandl "value" data class QuandlValue(PythonQuandl): def __init__(self): self.ValueColumnName = 'Value' # Quantpedia data. # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaFutures(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config, line, date, isLiveMode): 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]) return data