wehub-resource-sync
48 lines
2.5 KiB
Python
48 lines
2.5 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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from AlgorithmImports import *
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### <summary>
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### Test algorithm using 'ConfidenceWeightedPortfolioConstructionModel' and 'ConstantAlphaModel'
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### generating a constant 'Insight' with a 0.25 confidence
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### </summary>
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class ConfidenceWeightedFrameworkAlgorithm(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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# Set requested data resolution
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self.universe_settings.resolution = Resolution.MINUTE
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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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self.set_start_date(2013,10,7) #Set Start Date
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self.set_end_date(2013,10,11) #Set End Date
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self.set_cash(100000) #Set Strategy Cash
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symbols = [ Symbol.create("SPY", SecurityType.EQUITY, Market.USA) ]
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# set algorithm framework models
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self.set_universe_selection(ManualUniverseSelectionModel(symbols))
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self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(minutes = 20), 0.025, 0.25))
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self.set_portfolio_construction(ConfidenceWeightedPortfolioConstructionModel())
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self.set_execution(ImmediateExecutionModel())
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def on_end_of_algorithm(self):
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# holdings value should be 0.25 - to avoid price fluctuation issue we compare with 0.28 and 0.23
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if (self.portfolio.total_holdings_value > self.portfolio.total_portfolio_value * 0.28
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or self.portfolio.total_holdings_value < self.portfolio.total_portfolio_value * 0.23):
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raise ValueError("Unexpected Total Holdings Value: " + str(self.portfolio.total_holdings_value))
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