Overall Statistics |
Total Trades 216 Average Win 2.08% Average Loss -2.31% Compounding Annual Return 24.014% Drawdown 31.900% Expectancy 0.322 Net Profit 113.457% Sharpe Ratio 1.107 Probabilistic Sharpe Ratio 50.163% Loss Rate 30% Win Rate 70% Profit-Loss Ratio 0.90 Alpha 0.205 Beta 0.3 Annual Standard Deviation 0.196 Annual Variance 0.038 Information Ratio 0.802 Tracking Error 0.221 Treynor Ratio 0.721 Total Fees $1831.59 |
from QuantConnect.Data.UniverseSelection import * import math import numpy as np import pandas as pd import scipy as sp # import statsmodels.api as sm class FundamentalFactorAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2002, 4, 4) #Set Start Date self.SetEndDate(2005, 10, 10) #Set Start Date self.SetCash(50000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.spy = self.AddEquity("SPY", Resolution.Hour).Symbol self.holding_months = 1 self.num_screener = 40 self.num_stocks = 4 self.formation_days = 126 self.lowmom = False self.month_count = self.holding_months self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(0, 0), Action(self.monthly_rebalance)) self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(10, 0), Action(self.rebalance)) # rebalance the universe selection once a month self.rebalence_flag = 0 # make sure to run the universe selection at the start of the algorithm even it's not the manth start self.first_month_trade_flag = 1 self.trade_flag = 0 self.symbols = None def CoarseSelectionFunction(self, coarse): if self.rebalence_flag or self.first_month_trade_flag: # 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[:1000]] else: return self.symbols def FineSelectionFunction(self, fine): if self.rebalence_flag or self.first_month_trade_flag: try: filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0) and (x.EarningReports.BasicAverageShares.ThreeMonths > 0) and x.EarningReports.BasicAverageShares.ThreeMonths * (x.EarningReports.BasicEPS.TwelveMonths*x.ValuationRatios.PERatio) > 2e9] except: filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0) and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)] sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.ROIC.Value, reverse=True) sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.OperationRatios.LongTermDebtEquityRatio.Value, reverse=True) sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.FCFYield, reverse=True) stock_dict = {} # assign a score to each stock, you can also change the rule of scoring here. for i,ele in enumerate(sortedByfactor1): rank1 = i rank2 = sortedByfactor2.index(ele) rank3 = sortedByfactor3.index(ele) score = sum([rank1*0.4,rank2*0.2,rank3*0.4]) stock_dict[ele] = score # sort the stocks by their scores self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False) sorted_symbol = [x[0] for x in self.sorted_stock] self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=True) # self.sorted_symbol = [self.sorted_stock[i][0] for i in range(len(self.sorted_stock))] top= self.sorted_symbol[:self.num_screener] #top = sorted(filtered_fine, key = lambda x: x.ValuationRatios.FCFYield, reverse=True)[:self.num_screener] self.symbols = [x.Symbol for x in top] self.rebalence_flag = 0 self.first_month_trade_flag = 0 self.trade_flag = 1 return self.symbols else: return self.symbols def OnData(self, data): pass def monthly_rebalance(self): self.rebalence_flag = 1 def rebalance(self): spy_hist = self.History([self.spy], 200, Resolution.Daily).loc[str(self.spy)]['close'] spy_hist1=self.History([self.spy], 50, Resolution.Daily).loc[str(self.spy)]['close'] if self.Securities[self.spy].Price < spy_hist.mean() and self.Securities[self.spy].Price < spy_hist1.mean(): for symbol in self.Portfolio.Keys: if symbol.Value != "TLT": self.Liquidate() self.AddEquity("TLT") self.SetHoldings("TLT", 1) return if self.Securities[self.spy].Price < spy_hist.mean() and self.Securities[self.spy].Price > spy_hist1.mean(): self.state=True elif self.Securities[self.spy].Price > spy_hist.mean(): self.state=False if self.symbols is None: return chosen_df = self.calc_return(self.symbols) chosen_df = chosen_df.iloc[:self.num_stocks] self.existing_pos = 0 add_symbols = [] for symbol in self.Portfolio.Keys: if symbol.Value == 'SPY': continue if (symbol.Value not in chosen_df.index): self.SetHoldings(symbol, 0) elif (symbol.Value in chosen_df.index): self.existing_pos += 1 weight = 0.99/len(chosen_df) for symbol in chosen_df.index: self.AddEquity(symbol) self.SetHoldings(symbol, weight) def calc_return(self, stocks): hist = self.History(stocks, self.formation_days, Resolution.Daily) current = self.History(stocks, 10, Resolution.Minute) self.price = {} ret = {} for symbol in stocks: if str(symbol) in hist.index.levels[0] and str(symbol) in current.index.levels[0]: self.price[symbol.Value] = list(hist.loc[str(symbol)]['close']) self.price[symbol.Value].append(current.loc[str(symbol)]['close'][0]) for symbol in self.price.keys(): ret[symbol] = (self.price[symbol][-1] - self.price[symbol][0]) / self.price[symbol][0] df_ret = pd.DataFrame.from_dict(ret, orient='index') df_ret.columns = ['return'] if self.state==True: #if self.Securities[self.spy].Price < spy_hist.mean() and self.Securities[self.spy].Price > spy_hist1.mean():: sort_return = df_ret.sort_values(by = ['return'], ascending = True) else: sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom) #sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom) return sort_return