EMA Crossover Strategy

Hi Guys!

I'm trying to test different period EMA crossovers using the template codes provided in the QCU.

But when i try to backtest, no results come up. Is there something that i'm missing?

Appreciate your help guys.

Thank you!

import numpy as np
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import clr

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
import decimal as d

### <summary>
### In this example we look at the canonical 15/30 day moving average cross. This algorithm
### will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
### back below the 30.
### </summary>
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="moving average cross" />
### <meta name="tag" content="strategy example" />
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(2018, 10, 1) #Set End Date
self.SetCash(1000) #Set Strategy Cash
# Find more symbols here:

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

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

self.previous = None

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# a couple things to notice in this method:
# 1. We never need to 'update' our indicators with the data, the engine takes care of this for us
# 2. We can use indicators directly in math expressions
# 3. We can easily plot many indicators at the same time

# wait for our slow ema to fully initialize
if not self.slow.IsReady:

# only once per day
if self.previous is not None and ==

# 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.slow.Current.Value * d.Decimal(1 + tolerance):
self.Log("BUY >> {0}".format(self.Securities["XAUUSD"].Price))
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.slow.Current.Value:
self.Log("SELL >> {0}".format(self.Securities["XAUUSD"].Price))

self.previous = self.Time
Update Backtest

Your starting cash is 1000 and the price of "XAUUSD" is greater than 1000 from the log. There's no order in the algorithm because the buying power is insufficient. You can set a higher starting cash. Other than that, don't forget to specify the resolution in self.AddCfd(). The default value is Resolution.Minute. 


Oh I see! Thank you so much for your help! 

Still trying to get the hang of things. Thank you!


Update Backtest


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