# 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.
from AlgorithmImports import *
###
### Uses daily data and a simple moving average cross to place trades and an ema for stop placement
###
###
###
###
class DailyAlgorithm(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(2013,1,1) #Set Start Date
self.SetEndDate(2014,1,1) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.AddEquity("SPY", Resolution.Daily)
self.AddEquity("IBM", Resolution.Hour).SetLeverage(1.0)
self.macd = self.MACD("SPY", 12, 26, 9, MovingAverageType.Wilders, Resolution.Daily, Field.Close)
self.ema = self.EMA("IBM", 15 * 6, Resolution.Hour, Field.SevenBar)
self.lastAction = None
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''
if not self.macd.IsReady: return
if not data.ContainsKey("IBM"): return
if data["IBM"] is None:
self.Log("Price Missing Time: %s"%str(self.Time))
return
if self.lastAction is not None and self.lastAction.date() == self.Time.date(): return
self.lastAction = self.Time
quantity = self.Portfolio["SPY"].Quantity
if quantity <= 0 and self.macd.Current.Value > self.macd.Signal.Current.Value and data["IBM"].Price > self.ema.Current.Value:
self.SetHoldings("IBM", 0.25)
elif quantity >= 0 and self.macd.Current.Value < self.macd.Signal.Current.Value and data["IBM"].Price < self.ema.Current.Value:
self.SetHoldings("IBM", -0.25)