Overall Statistics |
Total Trades
589
Average Win
0.26%
Average Loss
-0.97%
Compounding Annual Return
4.332%
Drawdown
53.700%
Expectancy
0.135
Net Profit
84.994%
Sharpe Ratio
0.297
Loss Rate
11%
Win Rate
89%
Profit-Loss Ratio
0.27
Alpha
0.012
Beta
2.475
Annual Standard Deviation
0.19
Annual Variance
0.036
Information Ratio
0.202
Tracking Error
0.19
Treynor Ratio
0.023
Total Fees
$2555.36
|
# 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 class BooktoMarketAnomaly(QCAlgorithm): def Initialize(self): self.SetStartDate(2004, 1, 1) self.SetEndDate(2018, 7, 1) self.SetCash(1000000) self.UniverseSettings.Resolution = Resolution.Daily self.sorted_by_bm = None self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.AddEquity("SPY", Resolution.Daily) self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), self.rebalance) # Count the number of months that have passed since the algorithm starts self.months = -1 self.yearly_rebalance = True def CoarseSelectionFunction(self, coarse): if self.yearly_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: return [] def FineSelectionFunction(self, fine): if self.yearly_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 "MakretCap" property to fine universe object for i in fine: i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio)) 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 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] 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)] = i.MarketCap/total_market_cap return self.sorted_by_bm else: return [] def rebalance(self): # yearly rebalance self.months += 1 if self.months%12 == 0: self.yearly_rebalance = True def OnData(self, data): if not self.yearly_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 on stocks with the highest book-to-market ratio for i in self.sorted_by_bm: self.SetHoldings(i, self.weights[str(i)]) self.yearly_rebalance = False