Alexandre Catarino
58f0caf647
Some python algorithms suffered corrections to run under the new python framework (pythonnet). Others were deleted because some features will be supported in futures implementations. Adds a method in AlgorithmPythonUtil to transform C# DateTime into Python datetime
84 lines
3.5 KiB
Python
84 lines
3.5 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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import clr
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clr.AddReference("System")
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clr.AddReference("QuantConnect.Algorithm")
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clr.AddReference("QuantConnect.Indicators")
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clr.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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import decimal as d
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class MovingAverageCrossAlgorithm(QCAlgorithm):
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'''In this example we look at the canonical 15/30 day moving average cross. This algorithm
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will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
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back below the 30.'''
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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(2009, 01, 01) #Set Start Date
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self.SetEndDate(2015, 01, 01) #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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equity = self.AddEquity("SPY")
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self.spy = equity.Symbol
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# create a 15 day exponential moving average
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self.fast = self.EMA(self.spy, 15, Resolution.Daily);
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# create a 30 day exponential moving average
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self.slow = self.EMA(self.spy, 30, Resolution.Daily);
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self.previous = 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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# a couple things to notice in this method:
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# 1. We never need to 'update' our indicators with the data, the engine takes care of this for us
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# 2. We can use indicators directly in math expressions
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# 3. We can easily plot many indicators at the same time
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# wait for our slow ema to fully initialize
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if not self.slow.IsReady:
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return
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# only once per day
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if self.previous is not None and self.previous.Date == self.Time.Date:
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return
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# define a small tolerance on our checks to avoid bouncing
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tolerance = 0.00015;
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holdings = self.Portfolio[self.spy].Quantity
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# we only want to go long if we're currently short or flat
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if holdings <= 0:
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# if the fast is greater than the slow, we'll go long
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if self.fast.Current.Value > self.slow.Current.Value * d.Decimal(1 + tolerance):
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self.Log("BUY >> {0}".format(self.Securities[self.spy].Price))
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self.SetHoldings(self.spy, 1.0)
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# we only want to liquidate if we're currently long
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# if the fast is less than the slow we'll liquidate our long
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if holdings > 0 and self.fast.Current.Value < self.slow.Current.Value:
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self.Log("SELL >> {0}".format(self.Securities[self.spy].Price))
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self.Liquidate(self.spy)
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self.previous = self.Time |