Overall Statistics Total Trades 411 Average Win 3.88% Average Loss -2.49% Compounding Annual Return 14.843% Drawdown 69.600% Expectancy 0.330 Net Profit 338.064% Sharpe Ratio 0.499 Loss Rate 48% Win Rate 52% Profit-Loss Ratio 1.56 Alpha 0.103 Beta 10.752 Annual Standard Deviation 0.63 Annual Variance 0.397 Information Ratio 0.468 Tracking Error 0.63 Treynor Ratio 0.029 Total Fees \$69826.75
```# https://quantpedia.com/Screener/Details/162
from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp
from datetime import timedelta
class MomentumInSmallPortfolio(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2008, 1, 1)
self.SetEndDate(2018, 9, 1)
self.SetCash(1000000)
self.UniverseSettings.Resolution = 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
self.long = None
self.short = None
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:
# Calculate the yearly return and market cap
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)*0.75)]
has_return = []
for i in top_market_cap:
history = self.History([i.Symbol], timedelta(days=365), Resolution.Daily)
if not history.empty:
close = history.loc[str(i.Symbol)]['close']
i.returns = (close[0]-close[-1])/close[-1]
has_return.append(i)
sorted_by_return = sorted(has_return, key = lambda x: x.returns)
self.long = [i.Symbol for i in sorted_by_return[-10:]]
self.short = [i.Symbol for i in sorted_by_return[:10]]

return self.long+self.short
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.long and self.short:
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.long+self.short:
self.Liquidate(i)
for i in self.short:
self.SetHoldings(i, -0.5/len(self.short))
for i in self.long:
self.SetHoldings(i, 0.5/len(self.long))

self.long = None
self.short = None
self.yearly_rebalance = False```