Overall Statistics Total Trades 8 Average Win 3.93% Average Loss 0% Compounding Annual Return 22.468% Drawdown 2.900% Expectancy 0 Net Profit 16.563% Sharpe Ratio 2.153 Loss Rate 0% Win Rate 100% Profit-Loss Ratio 0 Alpha 0.124 Beta 2.613 Annual Standard Deviation 0.077 Annual Variance 0.006 Information Ratio 1.944 Tracking Error 0.077 Treynor Ratio 0.064 Total Fees \$0.00
```import decimal as d
from datetime import 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, 1, 1)    #Set Start Date
self.SetEndDate(2017, 10, 1)      #Set End Date
self.SetCash(100000)             #Set Strategy Cash

# create a 13 day exponential moving average
self.fast = self.EMA("XAUUSD", 5, Resolution.Daily)

# create a 48 day exponential moving average
self.slow = self.EMA("XAUUSD", 20, Resolution.Daily)

self.previous = None

self.SetWarmUp(timedelta(days=20))

def OnData(self, data):

# wait for our slow ema to fully initialize
if self.IsWarmingUp: return

# only once per day
if self.previous is not None and self.previous.date() == self.Time.date():
return

# define a small tolerance on our checks to avoid bouncing
tolerance = 0.00015
holdings = self.Portfolio["XAUUSD"].Quantity

# we only want to go long if we're currently short or flat
if holdings <= 0:
# if the fast is greater than the slow, we'll go long
if self.fast.Current.Value > self.slow.Current.Value * d.Decimal(1 + tolerance):
self.SetHoldings("XAUUSD", 1.0)

# we only want to liquidate if we're currently long
# if the fast is less than the slow we'll liquidate our long
if holdings > 0 and self.fast.Current.Value < self.slow.Current.Value:
self.Liquidate("XAUUSD")

self.previous = self.Time```