wehub-resource-sync
68 lines
3.7 KiB
Python
68 lines
3.7 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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from EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
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class InsightWeightingPortfolioConstructionModel(EqualWeightingPortfolioConstructionModel):
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'''Provides an implementation of IPortfolioConstructionModel that generates percent targets based on the
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Insight.WEIGHT. The target percent holdings of each Symbol is given by the Insight.WEIGHT from the last
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active Insight for that symbol.
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For insights of direction InsightDirection.UP, long targets are returned and for insights of direction
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InsightDirection.DOWN, short targets are returned.
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If the sum of all the last active Insight per symbol is bigger than 1, it will factor down each target
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percent holdings proportionally so the sum is 1.
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It will ignore Insight that have no Insight.WEIGHT value.'''
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def __init__(self, rebalance = Resolution.DAILY, portfolio_bias = PortfolioBias.LONG_SHORT):
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'''Initialize a new instance of InsightWeightingPortfolioConstructionModel
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Args:
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rebalance: Rebalancing parameter. If it is a timedelta, date rules or Resolution, it will be converted into a function.
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If None will be ignored.
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The function returns the next expected rebalance time for a given algorithm UTC DateTime.
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The function returns null if unknown, in which case the function will be called again in the
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next loop. Returning current time will trigger rebalance.
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portfolio_bias: Specifies the bias of the portfolio (Short, Long/Short, Long)'''
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super().__init__(rebalance, portfolio_bias)
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def should_create_target_for_insight(self, insight):
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'''Method that will determine if the portfolio construction model should create a
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target for this insight
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Args:
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insight: The insight to create a target for'''
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# Ignore insights that don't have Weight value
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return insight.weight is not None
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def determine_target_percent(self, active_insights):
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'''Will determine the target percent for each insight
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Args:
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active_insights: The active insights to generate a target for'''
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result = {}
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# We will adjust weights proportionally in case the sum is > 1 so it sums to 1.
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weight_sums = sum(self.get_value(insight) for insight in active_insights if self.respect_portfolio_bias(insight))
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weight_factor = 1.0
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if weight_sums > 1:
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weight_factor = 1 / weight_sums
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for insight in active_insights:
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result[insight] = (insight.direction if self.respect_portfolio_bias(insight) else InsightDirection.FLAT) * self.get_value(insight) * weight_factor
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return result
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def get_value(self, insight):
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'''Method that will determine which member will be used to compute the weights and gets its value
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Args:
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insight: The insight to create a target for
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Returns:
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The value of the selected insight member'''
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return abs(insight.weight)
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