# 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.Log("Initialize - Begin") self.SetStartDate(2008, 1, 1) self.SetEndDate(2019, 5, 24) self.SetCash(1000000) self.UniverseSettings.Resolution = Resolution.Daily 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 self.long = None self.short = None self.Log("Initialize - End") 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

 

I'm trying to clone this code from Tutorial and Back Test; didn't seem to even start.  I put 2 Logs in the Initialize and none of them are executed.  Is it because some of the libraries are black listed?

https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-stocks-in-small-portfolios