wehub-resource-sync
83 lines
4.4 KiB
Python
83 lines
4.4 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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from EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
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class AccumulativeInsightPortfolioConstructionModel(EqualWeightingPortfolioConstructionModel):
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'''Provides an implementation of IPortfolioConstructionModel that allocates percent of account
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to each insight, defaulting to 3%.
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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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By default, no rebalancing shall be done.
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Rules:
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1. On active Up insight, increase position size by percent
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2. On active Down insight, decrease position size by percent
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3. On active Flat insight, move by percent towards 0
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4. On expired insight, and no other active insight, emits a 0 target'''
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def __init__(self, rebalance = None, portfolio_bias = PortfolioBias.LONG_SHORT, percent = 0.03):
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'''Initialize a new instance of AccumulativeInsightPortfolioConstructionModel
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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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percent: percent of portfolio to allocate to each position'''
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super().__init__(rebalance)
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self.portfolio_bias = portfolio_bias
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self.percent = abs(percent)
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self.sign = lambda x: -1 if x < 0 else (1 if x > 0 else 0)
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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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percent_per_symbol = {}
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insights = sorted(self.algorithm.insights.get_active_insights(self.current_utc_time), key=lambda insight: insight.generated_time_utc)
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for insight in insights:
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target_percent = 0
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if insight.symbol in percent_per_symbol:
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target_percent = percent_per_symbol[insight.symbol]
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if insight.direction == InsightDirection.FLAT:
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# We received a Flat
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# if adding or subtracting will push past 0, then make it 0
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if abs(target_percent) < self.percent:
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target_percent = 0
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else:
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# otherwise, we flatten by percent
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target_percent += (-self.percent if target_percent > 0 else self.percent)
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target_percent += self.percent * insight.direction
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# adjust to respect portfolio bias
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if self.portfolio_bias != PortfolioBias.LONG_SHORT and self.sign(target_percent) != self.portfolio_bias:
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target_percent = 0
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percent_per_symbol[insight.symbol] = target_percent
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return dict((insight, percent_per_symbol[insight.symbol]) for insight in active_insights)
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def create_targets(self, algorithm, insights):
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'''Create portfolio targets from the specified insights
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Args:
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algorithm: The algorithm instance
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insights: The insights to create portfolio targets from
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Returns:
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An enumerable of portfolio targets to be sent to the execution model'''
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self.current_utc_time = algorithm.utc_time
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return super().create_targets(algorithm, insights)
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