wehub-resource-sync
63 lines
3.4 KiB
Python
63 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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class EqualWeightingPortfolioConstructionModel(PortfolioConstructionModel):
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'''Provides an implementation of IPortfolioConstructionModel that gives equal weighting to all securities.
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The target percent holdings of each security is 1/N where N is the number of securities.
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For insights of direction InsightDirection.UP, long targets are returned and
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for insights of direction InsightDirection.DOWN, short targets are returned.'''
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def __init__(self, rebalance = Resolution.DAILY, portfolio_bias = PortfolioBias.LONG_SHORT):
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'''Initialize a new instance of EqualWeightingPortfolioConstructionModel
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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__()
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self.portfolio_bias = portfolio_bias
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# If the argument is an instance of Resolution or Timedelta
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# Redefine rebalancing_func
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rebalancing_func = rebalance
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if isinstance(rebalance, Resolution):
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rebalance = Extensions.to_time_span(rebalance)
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if isinstance(rebalance, timedelta):
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rebalancing_func = lambda dt: dt + rebalance
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if rebalancing_func:
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self.set_rebalancing_func(rebalancing_func)
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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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# give equal weighting to each security
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count = sum(x.direction != InsightDirection.FLAT and self.respect_portfolio_bias(x) for x in active_insights)
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percent = 0 if count == 0 else 1.0 / count
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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) * percent
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return result
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def respect_portfolio_bias(self, insight):
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'''Method that will determine if a given insight respects the portfolio bias
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Args:
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insight: The insight to create a target for
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'''
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return self.portfolio_bias == PortfolioBias.LONG_SHORT or insight.direction == self.portfolio_bias
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