wehub-resource-sync
79 lines
3.4 KiB
Python
79 lines
3.4 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 AlgorithmImports import *
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### <summary>
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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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### </summary>
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="indicator classes" />
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### <meta name="tag" content="moving average cross" />
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### <meta name="tag" content="strategy example" />
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class MovingAverageCrossAlgorithm(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.set_start_date(2009, 1, 1) #Set Start Date
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self.set_end_date(2015, 1, 1) #Set End Date
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self.set_cash(100000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.add_equity("SPY")
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# create a 15 day exponential moving average
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self.fast = self.ema("SPY", 15, Resolution.DAILY)
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# create a 30 day exponential moving average
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self.slow = self.ema("SPY", 30, Resolution.DAILY)
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self.previous = None
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def on_data(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.is_ready:
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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["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 *(1 + tolerance):
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self.log("BUY >> {0}".format(self.securities["SPY"].price))
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self.set_holdings("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["SPY"].price))
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self.liquidate("SPY")
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self.previous = self.time
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