| Overall Statistics |
|
Total Trades 774 Average Win 0.07% Average Loss -0.04% Compounding Annual Return 4.610% Drawdown 28.800% Expectancy 0.687 Net Profit 22.459% Sharpe Ratio 0.312 Probabilistic Sharpe Ratio 3.953% Loss Rate 40% Win Rate 60% Profit-Loss Ratio 1.82 Alpha -0.048 Beta 1.08 Annual Standard Deviation 0.129 Annual Variance 0.017 Information Ratio -0.667 Tracking Error 0.062 Treynor Ratio 0.037 Total Fees $1160.04 Estimated Strategy Capacity $470000.00 Lowest Capacity Asset LSXMB W9TJFGFY3VJ9 |
#region imports
from AlgorithmImports import *
#endregion
# 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(2014, 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 Universe.Unchanged
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)]
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 Universe.Unchanged
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