wehub-resource-sync
66 lines
2.8 KiB
Python
66 lines
2.8 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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### Simple indicator demonstration algorithm of MACD
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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="plotting indicators" />
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class MACDTrendAlgorithm(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(2004, 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", Resolution.DAILY)
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# define our daily macd(12,26) with a 9 day signal
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self.__macd = self.macd("SPY", 12, 26, 9, MovingAverageType.EXPONENTIAL, Resolution.DAILY)
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self.__previous = datetime.min
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self.plot_indicator("MACD", True, self.__macd, self.__macd.signal)
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self.plot_indicator("SPY", self.__macd.fast, self.__macd.slow)
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def on_data(self, data):
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'''on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
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# wait for our macd to fully initialize
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if not self.__macd.is_ready: return
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# only once per day
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if self.__previous.date() == self.time.date(): return
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# define a small tolerance on our checks to avoid bouncing
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tolerance = 0.0025
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holdings = self.portfolio["SPY"].quantity
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signal_delta_percent = (self.__macd.current.value - self.__macd.signal.current.value)/self.__macd.fast.current.value
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# if our macd is greater than our signal, then let's go long
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if holdings <= 0 and signal_delta_percent > tolerance: # 0.01%
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# longterm says buy as well
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self.set_holdings("SPY", 1.0)
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# of our macd is less than our signal, then let's go short
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elif holdings >= 0 and signal_delta_percent < -tolerance:
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self.liquidate("SPY")
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self.__previous = self.time
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