Overall Statistics Total Trades 5 Average Win 4.38% Average Loss -0.67% Compounding Annual Return 21.979% Drawdown 11.700% Expectancy 2.781 Net Profit 10.277% Sharpe Ratio 1.707 Loss Rate 50% Win Rate 50% Profit-Loss Ratio 6.56 Alpha 0.272 Beta -3.181 Annual Standard Deviation 0.122 Annual Variance 0.015 Information Ratio 1.542 Tracking Error 0.122 Treynor Ratio -0.065 Total Fees \$10.18
```import clr
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
import decimal as d
import math
import numpy as np
import pandas as pd
import statistics
from datetime import datetime, timedelta

class MovingAverageCrossAlgorithm(QCAlgorithm):

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.SetStartDate(2017,9,8) #Set Start Date
self.SetEndDate(2018,3,6)   #Set End Date
self.SetCash(100000)             #Set Strategy Cash

# Find more symbols here: http://quantconnect.com/data

# create a 21 day exponential moving average
self.SPY = self.EMA("SPY", 252, Resolution.Daily);

self.previous = None

def OnData(self, data):

#Define a small tolerance on our checks to avoid bouncing
tolerance = 0.003;

#SPY
if self.Securities["SPY"].Price > self.SPY.Current.Value * d.Decimal(1 + tolerance):
cash = self.Portfolio.Cash
number_of_shares = (cash/self.Securities["SPY"].Price)
self.MarketOrder("SPY", number_of_shares)

if self.Securities["SPY"].Price < self.SPY.Current.Value * d.Decimal(1 - tolerance):
self.Liquidate("SPY")

self.previous = self.Time                        ```