Overall Statistics |
Total Trades 1490 Average Win 0.18% Average Loss -0.22% Compounding Annual Return 2.282% Drawdown 29.500% Expectancy 0.261 Net Profit 58.744% Sharpe Ratio 0.257 Probabilistic Sharpe Ratio 0.011% Loss Rate 30% Win Rate 70% Profit-Loss Ratio 0.81 Alpha 0.024 Beta -0.017 Annual Standard Deviation 0.088 Annual Variance 0.008 Information Ratio -0.199 Tracking Error 0.201 Treynor Ratio -1.31 Total Fees $169.25 |
# https://quantpedia.com/strategies/fx-carry-trade/ # # Create an investment universe consisting of several currencies (10-20). Go long three currencies with the highest central bank prime rates and # go short three currencies with the lowest central bank prime rates. The cash not used as the margin is invested in overnight rates. The strategy # is rebalanced monthly. import fk_tools class ForexCarryTradeAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) # Source: https://www.quandl.com/data/OECD-Organisation-for-Economic-Co-operation-and-Development 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 # OECD 3 month interbank rate for Switzerland is missing. # "CME_SF1", # Swiss Franc Futures, Continuous Contract #1 } for symbol, rate_symbol in self.symbols.items(): self.AddData(QuandlRate, rate_symbol, Resolution.Daily) data = self.AddData(fk_tools.QuantpediaFutures, symbol, Resolution.Daily) data.SetFeeModel(fk_tools.CustomFeeModel(self)) data.SetLeverage(5) self.Schedule.On(self.DateRules.MonthStart("CME_AD1"), self.TimeRules.AfterMarketOpen("CME_AD1"), self.Rebalance) def Rebalance(self): # Interbank rate sorting. sorted_by_rate = sorted([y for y in self.symbols if self.Securities.ContainsKey(self.symbols[y]) and self.Securities[y].Price != 0], key = lambda x: self.Securities[self.symbols[x]].Price, reverse = True) traded_count = 3 long = [x for x in sorted_by_rate[:traded_count]] short = [x for x in sorted_by_rate[-traded_count:]] # 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)) class QuandlRate(PythonQuandl): def __init__(self): self.ValueColumnName = 'Value'
import numpy as np from scipy.optimize import minimize sp100_stocks = ['AAPL','MSFT','AMZN','FB','BRKB','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK','PFE','CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL','TMO','COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR','QCOM','CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME','SPGI','COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'] def MonthDiff(d1, d2): return (d1.year - d2.year) * 12 + d1.month - d2.month def Return(values): return (values[-1] - values[0]) / values[0] def Volatility(values): values = np.array(values) returns = (values[1:] - values[:-1]) / values[:-1] return np.std(returns) def GetFutureMulitpliers(algorithm): symbol_multiplier = {} csv_string_file = algorithm.Download('data.quantpedia.com/backtesting_data/futures/contract_multiplier.csv') mulitpliers_lines = csv_string_file.split('\r\n') for line in mulitpliers_lines: symbol, multiplier = line.split(';') symbol_multiplier[symbol] = multiplier return symbol_multiplier # 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 free data class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "settle" # Quandl short interest data. class QuandlFINRA_ShortVolume(PythonQuandl): def __init__(self): self.ValueColumnName = 'SHORTVOLUME' # also 'TOTALVOLUME' is accesible # 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 # NOTE: Manager for new trades. It's represented by certain count of equally weighted brackets for long and short positions. # If there's a place for new trade, it will be managed for time of holding period. class TradeManager(): def __init__(self, algorithm, long_size, short_size, holding_period): self.algorithm = algorithm # algorithm to execute orders in. self.long_size = long_size self.short_size = short_size self.long_len = 0 self.short_len = 0 # Arrays of ManagedSymbols self.symbols = [] self.holding_period = holding_period # Days of holding. # Add stock symbol object def Add(self, symbol, long_flag): # Open new long trade. managed_symbol = ManagedSymbol(symbol, self.holding_period, long_flag) if long_flag: # If there's a place for it. if self.long_len < self.long_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, 1 / self.long_size) self.long_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Open new short trade. else: # If there's a place for it. if self.short_len < self.short_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, - 1 / self.short_size) self.short_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Decrement holding period and liquidate symbols. def TryLiquidate(self): symbols_to_delete = [] for managed_symbol in self.symbols: managed_symbol.days_to_liquidate -= 1 # Liquidate. if managed_symbol.days_to_liquidate == 0: symbols_to_delete.append(managed_symbol) self.algorithm.Liquidate(managed_symbol.symbol) if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 # Remove symbols from management. for managed_symbol in symbols_to_delete: self.symbols.remove(managed_symbol) def LiquidateTicker(self, ticker): symbol_to_delete = None for managed_symbol in self.symbols: if managed_symbol.symbol.Value == ticker: self.algorithm.Liquidate(managed_symbol.symbol) symbol_to_delete = managed_symbol if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 break if symbol_to_delete: self.symbols.remove(symbol_to_delete) else: self.algorithm.Debug("Ticker is not held in portfolio!") class ManagedSymbol(): def __init__(self, symbol, days_to_liquidate, long_flag): self.symbol = symbol self.days_to_liquidate = days_to_liquidate self.long_flag = long_flag class PortfolioOptimization(object): def __init__(self, df_return, risk_free_rate, num_assets): self.daily_return = df_return self.risk_free_rate = risk_free_rate self.n = num_assets # numbers of risk assets in portfolio self.target_vol = 0.05 def annual_port_return(self, weights): # calculate the annual return of portfolio return np.sum(self.daily_return.mean() * weights) * 252 def annual_port_vol(self, weights): # calculate the annual volatility of portfolio return np.sqrt(np.dot(weights.T, np.dot(self.daily_return.cov() * 252, weights))) def min_func(self, weights): # method 1: maximize sharp ratio return - self.annual_port_return(weights) / self.annual_port_vol(weights) # method 2: maximize the return with target volatility #return - self.annual_port_return(weights) / self.target_vol def opt_portfolio(self): # maximize the sharpe ratio to find the optimal weights cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) bnds = tuple((0, 1) for x in range(2)) + tuple((0, 0.25) for x in range(self.n - 2)) opt = minimize(self.min_func, # object function np.array(self.n * [1. / self.n]), # initial value method='SLSQP', # optimization method bounds=bnds, # bounds for variables constraints=cons) # constraint conditions opt_weights = opt['x'] return opt_weights