wehub-resource-sync
65 lines
3.0 KiB
Python
65 lines
3.0 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 MaximumDrawdownPercentPortfolio(RiskManagementModel):
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'''Provides an implementation of IRiskManagementModel that limits the drawdown of the portfolio to the specified percentage.'''
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def __init__(self, maximum_drawdown_percent = 0.05, is_trailing = False):
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'''Initializes a new instance of the MaximumDrawdownPercentPortfolio class
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Args:
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maximum_drawdown_percent: The maximum percentage drawdown allowed for algorithm portfolio compared with starting value, defaults to 5% drawdown</param>
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is_trailing: If "false", the drawdown will be relative to the starting value of the portfolio.
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If "true", the drawdown will be relative the last maximum portfolio value'''
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self.maximum_drawdown_percent = -abs(maximum_drawdown_percent)
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self.is_trailing = is_trailing
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self.initialised = False
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self.portfolio_high = 0
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def manage_risk(self, algorithm, targets):
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'''Manages the algorithm's risk at each time step
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Args:
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algorithm: The algorithm instance
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targets: The current portfolio targets to be assessed for risk'''
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current_value = algorithm.portfolio.total_portfolio_value
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if not self.initialised:
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self.portfolio_high = current_value # Set initial portfolio value
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self.initialised = True
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# Update trailing high value if in trailing mode
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if self.is_trailing and self.portfolio_high < current_value:
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self.portfolio_high = current_value
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return [] # return if new high reached
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pnl = self.get_total_drawdown_percent(current_value)
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if pnl < self.maximum_drawdown_percent and len(targets) != 0:
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self.initialised = False # reset the trailing high value for restart investing on next rebalcing period
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risk_adjusted_targets = []
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for target in targets:
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symbol = target.symbol
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# Cancel insights
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algorithm.insights.cancel([symbol])
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# liquidate
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risk_adjusted_targets.append(PortfolioTarget(symbol, 0))
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return risk_adjusted_targets
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return []
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def get_total_drawdown_percent(self, current_value):
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return (float(current_value) / float(self.portfolio_high)) - 1.0
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