Overall Statistics Total Trades 5 Average Win 2.10% Average Loss 0% Compounding Annual Return 12.807% Drawdown 3.000% Expectancy 0 Net Profit 11.683% Sharpe Ratio 1.976 Loss Rate 0% Win Rate 100% Profit-Loss Ratio 0 Alpha 0.003 Beta 5.966 Annual Standard Deviation 0.061 Annual Variance 0.004 Information Ratio 1.655 Tracking Error 0.061 Treynor Ratio 0.02 Total Fees \$10.77
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#   QuantConnect Basic Template:
#    Fundamentals to using a QuantConnect algorithm.
#
#    You can view the QCAlgorithm base class on Github:
#    https://github.com/QuantConnect/Lean/tree/master/Algorithm
#

import numpy as np
import decimal as d

class BasicTemplateAlgorithm(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, 01, 01)  #Set Start Date
self.SetEndDate(2017, 12, 01)    #Set End Date
self.SetCash(100000)             #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data

# create a 15 day exponential moving average
self.fast = self.EMA("SPY", 15, Resolution.Daily);

# create a 30 day exponential moving average
self.slow = self.EMA("SPY", 30, Resolution.Daily);

self.previous = None

def OnData(self, slice):
#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
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["SPY"].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):