Overall Statistics Total Trades 62 Average Win 0.93% Average Loss -1.33% Compounding Annual Return 13.747% Drawdown 25.900% Expectancy 0.057 Net Profit 43.525% Sharpe Ratio 0.689 Loss Rate 38% Win Rate 62% Profit-Loss Ratio 0.70 Alpha 0.242 Beta -6.552 Annual Standard Deviation 0.187 Annual Variance 0.035 Information Ratio 0.596 Tracking Error 0.187 Treynor Ratio -0.02 Total Fees \$236.53
```from clr import AddReference

from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from System.Collections.Generic import List
import decimal as d
import numpy as np
import time
from datetime import datetime
import numpy as np
from scipy import stats
import pandas as pd

class AFCMOM(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.first = -1
self.bi_weekly = 0
self.SetStartDate(2014,10,07)
self.SetEndDate(2016,10,07)
self.SetCash(100000)
self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
self.UniverseSettings.Resolution = Resolution.Daily
self.spy_200_sma = self.SMA("SPY",200,Resolution.Daily)
self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday,DayOfWeek.Wednesday), \
self.TimeRules.At(12, 0), \
Action(self.rebalnce))

self.SetWarmUp(201)

def CoarseSelectionFunction(self, coarse):
filtered_stocks = filter(lambda x: x.DollarVolume >250000,coarse)
filtered_stocks = filter(lambda x: x.HasFundamentalData,filtered_stocks)
filtered_stocks = filter(lambda x: x.Price >=20,filtered_stocks)
filtered_stocks = filtered_stocks[:100]
return [stock.Symbol for stock in filtered_stocks]

def FineSelectionFunction(self, fine):
filtered_stocks = filter(lambda x: x.SecurityReference.IsPrimaryShare,fine)
return [stock.Symbol for stock in filtered_stocks]

def OnSecuritiesChanged(self, changes):
dt = datetime(self.Time.year,self.Time.month,self.Time.day)
if dt.weekday() != 3 or self.Securities[self.spy].Price < self.spy_200_sma.Current.Value:
return
ATR = self.my_ATR(stock,14)
self.stocks_to_trade.sort(key = lambda x: self.get_slope(stock,90),reverse= True)
cash = float(self.Portfolio.Cash)
oo = len(self.Transactions.GetOpenOrders(stock))
if self.Securities[stock].Price >self.moving_average(stock,100) and not self.gapper(stock,90) and cash >0 and not oo:
self.SetHoldings(stock,self.weight(stock,ATR))

def rebalnce(self):
self.bi_weekly +=1
if self.bi_weekly%2 == 0:
for stock in self.Portfolio.Values:
if stock.Invested:
symbol = stock.Symbol
shares_held = float(self.Portfolio[symbol].Quantity)
if (self.Securities[symbol].Price < self.moving_average(symbol,100) and shares_held >0) or (self.gapper(symbol,90) and shares_held>0):
self.Liquidate(symbol)
else:
if shares_held >0:
ATR = self.my_ATR(symbol,20)
cost_basis = float(self.Portfolio[symbol].AveragePrice)
shares_held = float(self.Portfolio[symbol].Quantity)
percent_of_p = ((cost_basis * shares_held )/ float(self.Portfolio.TotalPortfolioValue))
weight= self.weight(symbol,ATR)
diff_in_desired_weight = weight -percent_of_p
if diff_in_desired_weight < 0:
order_amount = shares_held * diff_in_desired_weight
self.MarketOrder(symbol,order_amount)

def gapper(self,security,period):
if not self.Securities.ContainsKey(security):
return 0
security_data = self.History(security,period,Resolution.Daily)
close_data = [float(data) for data in security_data['close']]
return np.max(np.abs(np.diff(close_data))/close_data[:-1])>=0.15

def get_slope(self,security,period):
if not self.Securities.ContainsKey(security):
return 0
security_data = self.History(security,period,Resolution.Daily)
if 'close' not in security_data:
return 0
y= [np.log(float(data)) for data in security_data['close']]
x = [range(len(y))]
slope,r_value = stats.linregress(x,y)[0],stats.linregress(x,y)[2]
return ((np.exp(slope)**252)-1)*(r_value**2)

def my_ATR(self,security,period):
if not self.Securities.ContainsKey(security):
return 0
self.first+=1
security_data = self.History([security],period,Resolution.Daily)
c_data = [float(data) for data in security_data['close']]
l_data= [float(data) for data in security_data['low']]
h_data = [float(data) for data in security_data['high']]
true_range = [h-l for h,l in zip(h_data,l_data)]
average_true_range = np.mean(true_range)
average_true_range_smooted = ((average_true_range*13)+true_range[-1])/14
return average_true_range_smooted if not self.first else average_true_range

def weight(self,security,atr):
risk = float(self.Portfolio.TotalPortfolioValue)*0.0001
return (((risk/atr) * float(self.Securities[security].Price))/float(self.Portfolio.TotalPortfolioValue)*100)

def moving_average(self,security,period):
if not self.Securities.ContainsKey(security):
return 0
security_data = self.History(security,period,Resolution.Daily)
return np.mean([close for close in security_data['close']])                        ```