Files
quantconnect--lean/Algorithm.Python/DailyAlgorithm.py
T
AlexCatarino 18d83fb8ec Updates python framework to support python datetime
Updates nuget package to support conversion from C# DateTime to python datetime.
Updates algorithms to reflect this change.
2017-05-23 13:40:26 +01:00

65 lines
3.0 KiB
Python

# 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 *
class DailyAlgorithm(QCAlgorithm):
'''Uses daily data and a simple moving average cross to place trades and an ema for stop placement'''
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(2013,01,01) #Set Start Date
self.SetEndDate(2014,01,01) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
spy_security = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily)
ibm_security = self.AddSecurity(SecurityType.Equity, "IBM", Resolution.Hour)
ibm_security.SetLeverage(1.0)
self.ibm = ibm_security.Symbol
self.spy = spy_security.Symbol
self.macd = self.MACD(self.spy, 12, 26, 9, MovingAverageType.Wilders, Resolution.Daily, Field.Close)
self.ema = self.EMA(self.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(self.ibm): return
if data[self.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
holding = self.Portfolio[self.spy]
if holding.Quantity <= 0 and self.macd.Current.Value > self.macd.Signal.Current.Value and data[self.ibm].Price > self.ema.Current.Value:
self.SetHoldings(self.ibm, 0.25)
elif holding.Quantity >= 0 and self.macd.Current.Value < self.macd.Signal.Current.Value and data[self.ibm].Price < self.ema.Current.Value:
self.SetHoldings(self.ibm, -0.25)