Michael Handschuh
18a559943e
- Adds log to display the python version the algorithm is using. - Fixes python algorithms that were failing because of small subtleties like leading zeroes. - Updates pythonnet with a version compiled with python 3.6 flags - Changes in DockerfileFoundation: we now use miniconda to manage the python environment. - Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and PyTorch/Pyro (#1385). - Changes readme in Algorithm.Python to show steps to install miniconda
66 lines
2.9 KiB
Python
66 lines
2.9 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Indicators import *
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### <summary>
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### Uses daily data and a simple moving average cross to place trades and an ema for stop placement
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="trading and orders" />
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class DailyAlgorithm(QCAlgorithm):
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(2013,1,1) #Set Start Date
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self.SetEndDate(2014,1,1) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.AddEquity("SPY", Resolution.Daily)
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self.AddEquity("IBM", Resolution.Hour).SetLeverage(1.0)
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self.macd = self.MACD("SPY", 12, 26, 9, MovingAverageType.Wilders, Resolution.Daily, Field.Close)
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self.ema = self.EMA("IBM", 15 * 6, Resolution.Hour, Field.SevenBar)
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self.lastAction = None
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def OnData(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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if not self.macd.IsReady: return
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if not data.ContainsKey("IBM"): return
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if data["IBM"] is None:
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self.Log("Price Missing Time: %s"%str(self.Time))
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return
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if self.lastAction is not None and self.lastAction.date() == self.Time.date(): return
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self.lastAction = self.Time
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quantity = self.Portfolio["SPY"].Quantity
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if quantity <= 0 and self.macd.Current.Value > self.macd.Signal.Current.Value and data["IBM"].Price > self.ema.Current.Value:
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self.SetHoldings("IBM", 0.25)
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elif quantity >= 0 and self.macd.Current.Value < self.macd.Signal.Current.Value and data["IBM"].Price < self.ema.Current.Value:
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self.SetHoldings("IBM", -0.25) |