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Copy pathMovingAverageCrossAlgorithm.py
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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# 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 http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import clr
clr.AddReference("System")
clr.AddReference("QuantConnect.Algorithm")
clr.AddReference("QuantConnect.Indicators")
clr.AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
### <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(2009, 1, 1) #Set Start Date
self.SetEndDate(2015, 1, 1) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.AddEquity("SPY")
# 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, 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:
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 *(1 + tolerance):
self.Log("BUY >> {0}".format(self.Securities["SPY"].Price))
self.SetHoldings("SPY", 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.Log("SELL >> {0}".format(self.Securities["SPY"].Price))
self.Liquidate("SPY")
self.previous = self.Time