Overall Statistics Total Trades 1947 Average Win 0.82% Average Loss -0.85% Compounding Annual Return 20.718% Drawdown 31.200% Expectancy 0.314 Net Profit 1297.253% Sharpe Ratio 0.908 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 0.96 Alpha 0.192 Beta -1.197 Annual Standard Deviation 0.19 Annual Variance 0.036 Information Ratio 0.822 Tracking Error 0.19 Treynor Ratio -0.144 Total Fees \$6246.06
```from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp
# import statsmodels.api as sm

class FundamentalFactorAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2004, 01, 01)  #Set Start Date
self.SetEndDate(2018, 01, 01)  #Set Start Date
self.SetCash(50000)            #Set Strategy Cash

self.UniverseSettings.Resolution = Resolution.Daily
self.holding_months = 1
self.num_screener = 100
self.num_stocks = 10
self.formation_days = 200
self.lowmom = False
self.month_count = self.holding_months
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(0, 0), Action(self.monthly_rebalance))
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(10, 0), Action(self.rebalance))
# rebalance the universe selection once a month
self.rebalence_flag = 0
# make sure to run the universe selection at the start of the algorithm even it's not the manth start
self.symbols = None

def CoarseSelectionFunction(self, coarse):
# drop stocks which have no fundamental data or have too low prices
selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
# rank the stocks by dollar volume
filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)

return [ x.Symbol for x in filtered[:200]]
else:
return self.symbols

def FineSelectionFunction(self, fine):
hist = self.History([i.Symbol for i in fine], 1, Resolution.Daily)
try:
filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
and float(x.EarningReports.BasicAverageShares.ThreeMonths) * hist.loc[str(x.Symbol)]['close'][0] > 2e9]
except:
filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)]

top = sorted(filtered_fine, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener]
self.symbols = [x.Symbol for x in top]

self.rebalence_flag = 0
return self.symbols
else:
return self.symbols

def OnData(self, data):
pass

def monthly_rebalance(self):
self.rebalence_flag = 1

def rebalance(self):
spy_hist = self.History([self.spy], 120, Resolution.Daily).loc[str(self.spy)]['close']
if self.Securities[self.spy].Price < spy_hist.mean():
for symbol in self.Portfolio.Keys:
if symbol.Value != "TLT":
self.Liquidate()
self.SetHoldings("TLT", 1)
return

if self.symbols is None: return
chosen_df = self.calc_return(self.symbols)
chosen_df = chosen_df.iloc[:self.num_stocks]

self.existing_pos = 0
for symbol in self.Portfolio.Keys:
if symbol.Value == 'SPY': continue
if (symbol.Value not in chosen_df.index):
self.SetHoldings(symbol, 0)
elif (symbol.Value in chosen_df.index):
self.existing_pos += 1

weight = 0.99/len(chosen_df)
for symbol in chosen_df.index:
self.SetHoldings(symbol, weight)

def calc_return(self, stocks):
hist = self.History(stocks, self.formation_days, Resolution.Daily)
current = self.History(stocks, 1, Resolution.Minute)

self.price = {}
ret = {}

for symbol in stocks:
if str(symbol) in hist.index.levels[0] and str(symbol) in current.index.levels[0]:
self.price[symbol.Value] = list(hist.loc[str(symbol)]['close'])
self.price[symbol.Value].append(current.loc[str(symbol)]['close'][0])

for symbol in self.price.keys():
ret[symbol] = (self.price[symbol][-1] - self.price[symbol][0]) / self.price[symbol][0]
df_ret = pd.DataFrame.from_dict(ret, orient='index')
df_ret.columns = ['return']
sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom)

return sort_return                        ```