wehub-resource-sync
46 lines
2.2 KiB
Python
46 lines
2.2 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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### Regression algorithm testing GH feature 3790, using SetHoldings with a collection of targets
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### which will be ordered by margin impact before being executed, with the objective of avoiding any
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### margin errors
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### </summary>
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class SetHoldingsMultipleTargetsRegressionAlgorithm(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(2013,10, 7)
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self.set_end_date(2013,10,11)
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# use leverage 1 so we test the margin impact ordering
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self._spy = self.add_equity("SPY", Resolution.MINUTE, Market.USA, False, 1).symbol
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self._ibm = self.add_equity("IBM", Resolution.MINUTE, Market.USA, False, 1).symbol
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# Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
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# Commented so regression algorithm is more sensitive
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#self.settings.minimum_order_margin_portfolio_percentage = 0.005
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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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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.portfolio.invested:
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self.set_holdings([PortfolioTarget(self._spy, 0.8), PortfolioTarget(self._ibm, 0.2)])
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else:
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self.set_holdings([PortfolioTarget(self._ibm, 0.8), PortfolioTarget(self._spy, 0.2)])
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