Hi Guys, 

I am new to QC. I am trying to recreate the following trading strategy: 

1. On the last business day of every month, invest in the top 5 assets with the highest momentum score. 

2. Sell any asset that was previously in the top 5 but has since dropped out. 

3. Weight each asset by the inverse of its 63-day historical volatility.

When I do the back test is see no positions being entered. I experimented with the weights and excluded the Inverse volatility weights to enter a hard coded 1/5 as a position size, but still no trades are happening. What am I missing here? 

Many thanks and regards,

Dimitar 

# https://quantpedia.com/Screener/Details/15 import pandas as pd import numpy as np from statistics import mean from datetime import datetime from collections import defaultdict class CountryEquityIndexesMomentumAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2006, 1, 1) self.SetEndDate(datetime.now()) self.SetCash(10000) # create a dictionary to store momentum indicators for all symbols self.CombinedMomentum = {} self.inv_vola = {} self.weights = {} self.symbols = ["SPY","IWM", "QQQ", "EFA", "EEM","VNQ", "LQD", "GLD", "SHY", "IEF", "TLT", "AGG"] # warm up the MOM indicator self.SetWarmUp(252) for symbol in self.symbols: self.AddEquity(symbol, Resolution.Daily) self.Securities[symbol].SetDataNormalizationMode(DataNormalizationMode.TotalReturn) self.CombinedMomentum[symbol] = CombinedMomentum(self, symbol) #schedule the function to fire at the month start def getMonthLastTradingDay(self): month_last_day = DateTime(self.Time.year, self.Time.month, DateTime.DaysInMonth(self.Time.year, self.Time.month)) tradingDays = self.TradingCalendar.GetDaysByType(TradingDayType.BusinessDay, DateTime(self.Time.year, self.Time.month, 1), month_last_day) tradingDays = [day.Date.date() for day in tradingDays] return tradingDays[-1] #self.Schedule.On(self.DateRules.MonthEnd("SPY"), self.TimeRules.AfterMarketOpen("SPY", 60), self.Rebalance) def OnData(self, data): if self.IsWarmingUp: return self.Debug(str(self.top)) def Rebalance(self): #Determine the sec. to invest in at the last trading day of the month if (self.Time.date() == self.getMonthLastTradingDay()) and (self.Time.hour == 15): top = pd.Series(self.CombinedMomentum).sort_values(ascending = False)[:5] #Determine the weights of the portfolio # for symbol in top.index: # self.DailyReturn[symbol] = self.LOGR(symbol, 1, Resolution.Daily) # self.inv_vola[symbol] = 1/(self.STD(self.DailyReturn[symbol], 63, Resolution.Daily)) # self.Weights[symbol] = self.inv_vola[symbol].Current.Value/ self.SUM(self.inv_vola.Current.Values) for kvp in self.Portfolio: security_hold = kvp.Value # liquidate the security which is no longer in the top momentum list if security_hold.Invested and (security_hold.Symbol.Value not in top.index): self.Liquidate(security_hold.Symbol) #Buy the new sucurities added_symbols = [] for symbol in top.index: if not self.Portfolio[symbol].Invested: added_symbols.append(symbol) for added in added_symbols: self.SetHoldings(added, 1/5) class CombinedMomentum(): def __init__(self, algo, symbol): self.MOMP_1M = algo.MOMP(symbol, 21, Resolution.Daily) self.MOMP_3M = algo.MOMP(symbol, 63, Resolution.Daily) self.MOMP_6M = algo.MOMP(symbol, 126, Resolution.Daily) self.MOMP_12M = algo.MOMP(symbol, 252, Resolution.Daily) def getValue(self): value = (self.MOMP_1M.Current.Value + self.MOMP_3M.Current.Value + self.MOMP_6M.Current.Value + self.MOMP_12M.Current.Value)/4 return value # class InverseVolatility(): # def __init__(self,algo,symbol): # self.DailyReturn = algo.LOGR(symbol, 1, Resolution.Daily) # def getValue(self): # value = 1/(self.STD(self.DailyReturn, 63, Resolution.Daily).Current.Value) # return value