Overall Statistics |
Total Trades 106 Average Win 0.74% Average Loss -0.58% Compounding Annual Return 7.014% Drawdown 13.300% Expectancy 0.404 Net Profit 10.078% Sharpe Ratio 0.655 Loss Rate 38% Win Rate 62% Profit-Loss Ratio 1.26 Alpha 0.037 Beta 1.731 Annual Standard Deviation 0.106 Annual Variance 0.011 Information Ratio 0.477 Tracking Error 0.106 Treynor Ratio 0.04 Total Fees $207.29 |
# https://quantpedia.com/Screener/Details/7 # The investment universe consists of global large cap stocks (or US large cap stocks). # At the end of the each month, the investor constructs equally weighted decile portfolios # by ranking the stocks on the past one year volatility of daily price. The investor # goes long stocks with the lowest volatility. from QuantConnect.Data.UniverseSelection import * import math import numpy as np import pandas as pd import scipy as sp class ShortTermReversalAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2017, 1, 1) # Set Start Date self.SetEndDate(2018, 6, 1) # Set Start Date self.SetCash(100000) # Set Strategy Cash self.lookback = 252 self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.symbolDataDict = {} self.AddEquity("SPY", Resolution.Daily) self.Schedule.On(self.DateRules.MonthStart("SPY"),self.TimeRules.AfterMarketOpen("SPY"), self.rebalance) def CoarseSelectionFunction(self, coarse): # drop stocks which have no fundamental data or have too low prices selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)] # rank the stocks by dollar volume filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True) return [ x.Symbol for x in filtered[:100]] def FineSelectionFunction(self, fine): # filter stocks with the top market cap top = sorted(fine, key = lambda x: x.EarningReports.BasicAverageShares.ThreeMonths * (x.EarningReports.BasicEPS.TwelveMonths*x.ValuationRatios.PERatio), reverse=True) return [x.Symbol for x in top[:50]] def rebalance(self): sorted_symbolData = sorted(self.symbolDataDict, key=lambda x: self.symbolDataDict[x].Volatility()) # pick 5 stocks with the lowest volatility long_stocks = sorted_symbolData[:5] stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested] # liquidate stocks not in the list for i in stocks_invested: if i not in long_stocks: self.Liquidate(i) # long stocks with the lowest volatility for i in long_stocks: self.SetHoldings(i, 1/5) def OnData(self, data): for symbol, symbolData in self.symbolDataDict.items(): # update the indicator value for newly added securities if symbol not in self.addedSymbols: symbolData.Price.Add(IndicatorDataPoint(symbol, self.Time, self.Securities[symbol].Close)) self.addedSymbols = [] self.removedSymbols = [] def OnSecuritiesChanged(self, changes): # clean up data for removed securities self.removedSymbols = [x.Symbol for x in changes.RemovedSecurities] for removed in changes.RemovedSecurities: symbolData = self.symbolDataDict.pop(removed.Symbol, None) # warm up the indicator with history price for newly added securities self.addedSymbols = [ x.Symbol for x in changes.AddedSecurities if x.Symbol.Value != "SPY"] history = self.History(self.addedSymbols, self.lookback+1, Resolution.Daily) for symbol in self.addedSymbols: if symbol not in self.symbolDataDict.keys(): symbolData = SymbolData(symbol, self.lookback) self.symbolDataDict[symbol] = symbolData if str(symbol) in history.index: symbolData.WarmUpIndicator(history.loc[str(symbol)]) class SymbolData: '''Contains data specific to a symbol required by this model''' def __init__(self, symbol, lookback): self.symbol = symbol self.Price = RollingWindow[IndicatorDataPoint](lookback) def WarmUpIndicator(self, history): # warm up the RateOfChange indicator with the history request for tuple in history.itertuples(): item = IndicatorDataPoint(self.symbol, tuple.Index, float(tuple.close)) self.Price.Add(item) def Volatility(self): data = [float(x.Value) for x in self.Price] # time = [x.EndTime for x in self.Price] # price_series = pd.Series(data, index=time) return np.std(data)