Overall Statistics |
Total Trades 5751 Average Win 0.01% Average Loss 0.00% Compounding Annual Return 14.041% Drawdown 21.000% Expectancy 2.754 Net Profit 52.423% Sharpe Ratio 0.828 Loss Rate 14% Win Rate 86% Profit-Loss Ratio 3.37 Alpha 0.33 Beta -15.099 Annual Standard Deviation 0.128 Annual Variance 0.016 Information Ratio 0.712 Tracking Error 0.128 Treynor Ratio -0.007 Total Fees $6264.95 |
# https://quantpedia.com/Screener/Details/26 from QuantConnect.Data.UniverseSelection import * import math import numpy as np import pandas as pd import scipy as sp # ---------------------------------------------------------------- # To do # ---------------------------------------------------------------- # x Rebalance Monthly # x Change weighting of positions to equal weights # x Plot development of Breakpoints over time # - Narrow down to single asset (for testing purposes) # - Add calculation of target price # - Overlay covered call strategy linking strikes with breakpoints # - Overlay LT & ST replication (short put & long call) # - Extend to multiple tickers # - Extend to short leg # ---------------------------------------------------------------- # Some Notes # ---------------------------------------------------------------- # self.lowercase variables are variables defined by oneself # self.Uperrcase variables reference QC API class BooktoMarketAnomaly(QCAlgorithm): def Initialize(self): self.Debug('______________________________________________________________________________________________________________________________') self.Debug('Initializing Backtest') self.SetStartDate(2016, 1, 1) self.SetEndDate(2019, 3, 15) self.SetCash(1000000) # Granularity - Daily Resolution self.UniverseSettings.Resolution = Resolution.Daily self.sorted_by_bm = None self.current = [] self.monthly_rebalance = False # Universe + Settings self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) # Benchmark self.SetBenchmark("SPY") self.AddEquity("SPY", Resolution.Daily) # Schedule functions # Trigger an event every day a specific symbol is trading --> here monthly self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), self.rebalance) # Plotting # Chart - Master Container for the Chart: breakpPlot = Chart('Fundamentals') breakpPlot.AddSeries(Series('Breakpoint-Min', SeriesType.Line)) breakpPlot.AddSeries(Series('Breakpoint-Max', SeriesType.Line)) self.AddChart(breakpPlot) def CoarseSelectionFunction(self, coarse): if self.monthly_rebalance: # drop stocks which have no fundamental data (or have low price) self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)] return self.filtered_coarse else: self.Log('CoarseSelectionFunction went into else...') return [] def FineSelectionFunction(self, fine): if self.monthly_rebalance: # Filter stocks with positive PB Ratio fine = [x for x in fine if (x.ValuationRatios.PBRatio > 0)] # Calculate the market cap and add the "MarketCap" property to fine universe object for i in fine: i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio)) # Syntax : sorted(iterable, key, reverse) --> reverse means from highest (expensive) to lowest (cheap) top_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse=True)[:int(len(fine)*0.2)] # sorted stocks in the top market-cap list by book-to-market ratio -> cheapest first top_bm = sorted(top_market_cap, key = lambda x: 1 / x.ValuationRatios.PBRatio, reverse=True)[:int(len(top_market_cap)*0.2)] self.sorted_by_bm = [i.Symbol for i in top_bm] top_bm_sorted = sorted(top_bm, key = lambda x: x.ValuationRatios.PBRatio, reverse=True) top_bm_tickers = [i.Symbol for i in top_bm_sorted] top_bm_ratios = [i.ValuationRatios.PBRatio for i in top_bm_sorted] self.ticker_PB = np.column_stack((top_bm_tickers, top_bm_ratios)) self.Debug('Top PB Ratio ' + str(top_bm_tickers[0]) + ': ' + str(top_bm_ratios[0])) self.Debug('Bottom PB Ratio ' + str(top_bm_tickers[-1]) + ': ' + str(top_bm_ratios[-1])) # Save cut-off breakpoint for plot self.breakpoint_max = max(top_bm_ratios) self.breakpoint_min = min(top_bm_ratios) total_market_cap = np.sum([i.MarketCap for i in top_bm]) # calculate the weight with the market cap self.weights = {} for i in top_bm: self.weights[str(i.Symbol)] = 1/len(self.sorted_by_bm) #i.MarketCap/total_market_cap self.Log(self.ticker_PB) return self.sorted_by_bm else: self.Log('FineSelectionFunction went into else...') return [] def rebalance(self): # form yearly to monthly rebalance self.monthly_rebalance = True self.Debug('Rebalancing on ' + str(self.Time)) # this event fires whenever we have changes to our universe def OnSecuritiesChanged(self, changes): self.changes = changes self.Debug('Universe Changed on ' + str(self.Time)) def OnData(self, data): if not self.monthly_rebalance: return if self.sorted_by_bm: stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested] # liquidate stocks not in the trading list for i in stocks_invested: if i not in self.sorted_by_bm: self.Liquidate(i) # goes long stocks with the highest book-to-market ratio for i in self.sorted_by_bm: # Changed this to simple weight +1 for single asset self.SetHoldings(i, self.weights[str(i)]) if self.current == self.sorted_by_bm: self.Debug('True') else: self.Debug('False') self.current = self.sorted_by_bm self.monthly_rebalance = False # Later in your OnData(self, data): self.Plot('Fundamentals', 'Breakpoint Min', self.breakpoint_min) self.Plot('Fundamentals', 'Breakpoint Max', self.breakpoint_max)