Overall Statistics |
Total Trades 718 Average Win 0.24% Average Loss -0.38% Compounding Annual Return 1.416% Drawdown 26.700% Expectancy 0.000 Net Profit 8.031% Sharpe Ratio 0.192 Loss Rate 38% Win Rate 62% Profit-Loss Ratio 0.62 Alpha -0.062 Beta 4.118 Annual Standard Deviation 0.095 Annual Variance 0.009 Information Ratio -0.014 Tracking Error 0.095 Treynor Ratio 0.004 Total Fees $0.00 |
# https://quantpedia.com/Screener/Details/118 from QuantConnect.Python import PythonQuandl from datetime import timedelta import numpy as np import pandas as pd class TimeSeriesMomentumEffect(QCAlgorithm): def Initialize(self): self.SetStartDate(2013,1, 1) self.SetEndDate(2018, 7, 1) self.SetCash(2500000) # self.lme_symbols = [ # ] self.cme_symbols = ["CHRIS/CME_LC1", # Live Cattle Futures, Continuous Contract #1 "CHRIS/CME_LN1", # Lean Hog Futures, Continuous Contract #1 ] #Settle self.ice_symbols = ["CHRIS/ICE_B1", # Brent Crude Futures, Continuous Contract "CHRIS/ICE_G1", # Gas Oil Futures, Continuous Contract "CHRIS/ICE_CT1", # Cotton No. 2 Futures, Continuous Contract "CHRIS/ICE_KC1", # Coffee C Futures, Continuous Contract "CHRIS/ICE_CC1", # Cocoa Futures, Continuous Contract "CHRIS/ICE_SB1", # Sugar No. 11 Futures, Continuous Contract ] #Settle self.cbot_symbols = ["CHRIS/CME_C1", #Corn Futures, Continuous Contract #1 (C1) (Front Month) "CHRIS/CME_S1", #Soybean Futures, Continuous Contract #1 (S1) (Front Month) "CHRIS/CME_SM1", #Soybean Meal Futures, Continuous Contract #1 (SM1) (Front Month) "CHRIS/CME_BO1", #Soybean Oil Futures, Continuous Contract #1 (BO1) (Front Month) "CHRIS/CME_W1", #Wheat Futures, Continuous Contract #1 (W1) (Front Month) ] #Settle self.equityIndex_symbols = ["CHRIS/LIFFE_Z1", #FTSE 100 Index Futures, Continuous Contract #1 (Z1) (Front Month) "CHRIS/CME_SP1", #S&P 500 Futures, Continuous Contract #1 (SP1) (Front Month) "CHRIS/ASX_AP1", #ASX SPI 200 Index Futures, Continuous Contract #1 (AP1) (Front Month) "CHRIS/LIFFE_FCE1",#CAC40 Index Futures, Continuous Contract #1 (FCE1) (Front Month) "CHRIS/EUREX_FDAX1", #DAX Futures, Continuous Contract #1 (FDAX1) (Front Month) "CHRIS/LIFFE_FTI1", #AEX Index Futures, Continuous Contract #1 (FTI1) (Front Month) ] #Settle # self.nymex_symbols = [ # ] # self.comex_symbols = [ # ] # self.tocom_symbols = [ # ] self.currency_symbols =["AUD/USD","EUR/USD","CAD/USD","JPY/USD"] #Value period = 252 self.roc = {} # self.symbols_Settle = self.cme_symbols + self.ice_symbols + self.cbot_symbols + self.equityIndex_symbols self.symbols_Settle = self.cme_symbols + self.ice_symbols + self.cbot_symbols # self.symbols_Value = self.currency_symbols # self.symbols_Value = [] # self.symbols = self.symbols_Settle + self.symbols_Value self.symbols = self.symbols_Settle for symbol in self.symbols_Settle: self.AddData(QuandlFutures, symbol, Resolution.Daily) self.roc[symbol] = RateOfChange(period) hist = self.History([symbol], 400, Resolution.Daily).loc[symbol] for i in hist.itertuples(): self.roc[symbol].Update(i.Index, i.settle) # for symbol in self.symbols_Value: # self.AddData(QuandlForex, symbol, Resolution.Daily) # self.roc[symbol] = RateOfChange(period) # hist = self.History([symbol], 400, Resolution.Daily).loc[symbol] # for i in hist.itertuples(): # self.roc[symbol].Update(i.Index, i.value) # Rebalance the portfolio every month self.Schedule.On(self.DateRules.MonthStart("CHRIS/CME_S1"), self.TimeRules.AfterMarketOpen("CHRIS/CME_S1"), self.Rebalance) def OnData(self, data): # Update the indicator value every day for symbol in self.symbols: if data.ContainsKey(symbol) and self.roc[symbol].IsReady: self.roc[symbol].Update(self.Time, data[symbol].Value) def Rebalance(self): self.long = [symbol for symbol in self.roc if self.roc[symbol].Current.Value > 0] self.short = [symbol for symbol in self.roc if self.roc[symbol].Current.Value < 0] for kvp in self.Portfolio: security_hold = kvp.Value # liquidate the futures which is no longer in the trading list if security_hold.Invested and (security_hold.Symbol.Value not in (self.long+self.short)): self.Liquidate(security_hold.Symbol) weights_long = {} weights_short = {} volatility = {} for symbol in self.symbols: hist = self.History(self.Symbol(symbol), 252, Resolution.Daily).loc[symbol]['value'].tolist() volatility[symbol] = 1/np.std(hist,ddof = 1) #calculate the historical volatility and get its reciprocal for long in self.long: weights_long[long] = volatility[long]/sum(volatility.values()) for short in self.short: weights_short[short] = volatility[short]/sum(volatility.values()) for long in self.long: self.SetHoldings(long, 0.5*weights_long[long]/sum(weights_long.values())) for short in self.short: self.SetHoldings(short, -0.5*weights_short[short]/sum(weights_short.values())) class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "Settle" class QuandlForex(PythonQuandl): def __init__(self): self.ValueColumnName = "Value"