Overall Statistics |
Total Trades
2080
Average Win
0.07%
Average Loss
-0.06%
Compounding Annual Return
0.572%
Drawdown
26.000%
Expectancy
0.365
Net Profit
13.917%
Sharpe Ratio
0.097
Probabilistic Sharpe Ratio
0.000%
Loss Rate
39%
Win Rate
61%
Profit-Loss Ratio
1.23
Alpha
0.001
Beta
0.088
Annual Standard Deviation
0.058
Annual Variance
0.003
Information Ratio
-0.322
Tracking Error
0.158
Treynor Ratio
0.064
Total Fees
$74.74
Estimated Strategy Capacity
$0
Lowest Capacity Asset
CME_AD1.QuantpediaFutures 2S
|
# https://quantpedia.com/strategies/dollar-carry-trade/ # # The investment universe consists of currencies from developed countries (the Euro area, Australia, Canada, Denmark, Japan, New Zealand, Norway, Sweden, # Switzerland, and the United Kingdom). The average forward discount (AFD) is calculated for this basket of currencies (each currency has an equal weight). # The average 3-month rate could be used instead of the AFD in the calculation. The AFD is then compared to the 3-month US Treasury rate. The investor # goes long on the US dollar and goes short on the basket of currencies if the 3-month US Treasury rate is higher than the AFD. The investor goes short # on the US dollar and long on the basket of currencies if the 3-month US Treasury rate is higher than the AFD. The portfolio is rebalanced monthly. import numpy as np from AlgorithmImports import * class DollarCarryTrade(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbols = { "CME_AD1" : "OECD/KEI_IR3TIB01_AUS_ST_M", # Australian Dollar Futures, Continuous Contract #1 "CME_BP1" : "OECD/KEI_IR3TIB01_GBR_ST_M", # British Pound Futures, Continuous Contract #1 "CME_CD1" : "OECD/KEI_IR3TIB01_CAN_ST_M", # Canadian Dollar Futures, Continuous Contract #1 "CME_EC1" : "OECD/KEI_IR3TIB01_EA19_ST_M",# Euro FX Futures, Continuous Contract #1 "CME_JY1" : "OECD/KEI_IR3TIB01_JPN_ST_M", # Japanese Yen Futures, Continuous Contract #1 "CME_MP1" : "OECD/KEI_IR3TIB01_MEX_ST_M", # Mexican Peso Futures, Continuous Contract #1 "CME_NE1" : "OECD/KEI_IR3TIB01_NZL_ST_M", # New Zealand Dollar Futures, Continuous Contract #1 "CME_SF1" : "SNB/ZIMOMA" # Swiss Franc Futures, Continuous Contract #1 } for symbol in self.symbols: data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily) data.SetFeeModel(CustomFeeModel()) data.SetLeverage(5) # Interbank rate data. cash_rate_symbol = self.symbols[symbol] self.AddData(QuandlValue, cash_rate_symbol, Resolution.Daily) self.treasury_rate = self.AddData(QuandlValue, 'FRED/DGS3MO', Resolution.Daily).Symbol def OnData(self, data): fd = {} for future_symbol, cash_rate_symbol in self.symbols.items(): if cash_rate_symbol in data and data[cash_rate_symbol]: if self.Securities[future_symbol].GetLastData() and (self.Time.date() - self.Securities[future_symbol].GetLastData().Time.date()).days < 5: cash_rate = data[cash_rate_symbol].Value # Update cash rate only once a month. fd[future_symbol] = cash_rate if len(fd) == 0: return afd = np.mean([x[1] for x in fd.items()]) if self.Securities[self.treasury_rate].GetLastData() and (self.Time.date() - self.Securities[self.treasury_rate].GetLastData().Time.date()).days < 5: treasuries_3m_rate = self.Securities[self.treasury_rate].Price count = len(self.symbols) if treasuries_3m_rate > afd: # Long on the US dollar and goes short on the basket of currencies. for symbol in self.symbols: self.SetHoldings(symbol, -1 / count) else: # Short on the US dollar and long on the basket of currencies. for symbol in self.symbols: self.SetHoldings(symbol, 1 / count) # 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 # Quandl "value" data class QuandlValue(PythonQuandl): def __init__(self): self.ValueColumnName = 'Value' # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))