Overall Statistics |
Total Trades 1680 Average Win 0.14% Average Loss -0.14% Compounding Annual Return 8.605% Drawdown 13.300% Expectancy 0.008 Net Profit 0.704% Sharpe Ratio 0.357 Loss Rate 49% Win Rate 51% Profit-Loss Ratio 0.99 Alpha 2.032 Beta -119.443 Annual Standard Deviation 0.392 Annual Variance 0.154 Information Ratio 0.317 Tracking Error 0.392 Treynor Ratio -0.001 Total Fees $2648.09 |
#from System.Collections.Generic import List import decimal as d import numpy as np import pandas as pd import time class BatchProblem(QCAlgorithm): def Initialize(self): self.SetStartDate(2017, 5, 24) #Set Start Date self.SetEndDate( 2017, 6, 24) #Set End Date self.SetCash(100000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Minute #self.UniverseSettings.Leverage = 4 self.max_per_side_assets = 20 # so 2*N is total assets at L=2 self.coarse_count = 300 #3000 self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) self.AddUniverse(self.CoarseSelectionFunction) self.AddEquity("SPY", Resolution.Minute) self.Schedule.On(self.DateRules.EveryDay("SPY"), #self.TimeRules.At(8,45), self.TimeRules.AfterMarketOpen("SPY", 0), Action(self.Downloader)) self.Schedule.On(self.DateRules.EveryDay("SPY"), #self.TimeRules.At(8,55), self.TimeRules.AfterMarketOpen("SPY", 1), Action(self.Downloader)) self.Schedule.On(self.DateRules.EveryDay("SPY"), #self.TimeRules.At(9,5), self.TimeRules.AfterMarketOpen("SPY", 2), Action(self.Downloader)) self.Schedule.On(self.DateRules.EveryDay("SPY"), #self.TimeRules.At(9,15), self.TimeRules.AfterMarketOpen("SPY", 3), Action(self.Downloader)) self.Schedule.On(self.DateRules.EveryDay("SPY"), #self.TimeRules.At(9,25), self.TimeRules.AfterMarketOpen("SPY", 4), Action(self.Strategy)) self.Schedule.On(self.DateRules.EveryDay("SPY"), #self.TimeRules.AfterMarketOpen("SPY", 0), self.TimeRules.AfterMarketOpen("SPY", 5), Action(self.Rebalance)) self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.BeforeMarketClose("SPY", 3), Action(self.FireSale)) self.universe = [] self.Reset_Baskets() self.n_hist_items = 3 #100 self.splotName = 'Strategy Info' sPlot = Chart(self.splotName) sPlot.AddSeries(Series('N_Universe', SeriesType.Line, 0)) # Label only self.AddChart(sPlot) def Reset_Baskets(self): self.current_symbols_long = [] self.current_symbols_short = [] self.split_universe = [] self.current_subset = 0 self.asset_hist = {} def Downloader(self): self.Log("Downloader %d "%self.current_subset + str(self.Time)) if len(self.split_universe) == 0: return # Download subset of universe to prevent timeout via History server for symbol in self.split_universe[self.current_subset]: self.asset_hist[symbol] = self.History([symbol,], self.n_hist_items, Resolution.Daily).astype(np.float32) self.current_subset += 1 def ProcessAsset(self, symbol): asset_hist = self.asset_hist[symbol] #compute something with history to show difference dp = asset_hist.open.pct_change().values[-1] return symbol, dp def Strategy(self): self.Log("Strategy " + str(self.Time)) if len(self.universe) == 0: return # Collect the info we need per assets to filter/mask res = [self.ProcessAsset(symbol) for symbol in self.universe]# if str(symbol) in self.asset_hist] symbols, std = zip(*res) symbols = np.array(symbols) std = np.array(std) self.current_symbols_long = symbols[np.argsort(std)][-self.max_per_side_assets:] self.current_symbols_short = symbols[np.argsort(std)][:self.max_per_side_assets] def Rebalance(self): self.Log("Rebalance " + str(self.Time)) # Enter short positions if len(self.current_symbols_short) > 0: for sym in self.current_symbols_short: self.SetHoldings(sym, -0.1) # Enter long positions if len(self.current_symbols_long) > 0: for sym in self.current_symbols_long: self.SetHoldings(sym, 0.1) self.Plot(self.splotName,'N_Universe', len(self.universe)) def FireSale(self): self.Liquidate() def CoarseSelectionFunction(self, coarse): self.Log("CoarseSelectionFunction " + str(self.Time)) # Filter the values of the dict: by price values = filter(lambda x: (x.Price >= 5.0) and (x.Price <= 250.0), coarse) # sort descending by daily dollar volume sortedByDollarVolume = sorted(values, key=lambda x: x.DollarVolume, reverse=True) # Assets selected # we need to return only the symbol objects result = [ x.Symbol for x in sortedByDollarVolume[:self.coarse_count] ] # Clear the baskets and prepare for a new universe self.Reset_Baskets() # we need to call Strategy n times to hit our uni target self.split_universe = np.array_split(result, 4) # universe is ~2k so use 4 updated taking ~5-8 min each? return result # this event fires whenever we have changes to our universe # this event fires whenever we have changes to our universe def OnSecuritiesChanged(self, changes): # liquidate removed securities for security in changes.RemovedSecurities: if security.Symbol in self.universe: self.universe.remove(security.Symbol) if security.Invested: self.Log(str("OnSecuritiesChanged Liquidate of %s"%str(security.Symbol))) self.Liquidate(security.Symbol) # we want equal allocation in each security in our universe for security in changes.AddedSecurities: security.MarginModel = PatternDayTradingMarginModel() if security.Symbol not in self.universe: if str(security.Symbol) != "SPY": self.universe.append(security.Symbol)