wehub-resource-sync
75 lines
3.9 KiB
Python
75 lines
3.9 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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### Regression algorithm testing portfolio construction model control over rebalancing,
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### specifying a custom rebalance function that returns null in some cases, see GH 4075.
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### </summary>
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class PortfolioRebalanceOnCustomFuncRegressionAlgorithm(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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self.universe_settings.resolution = Resolution.DAILY
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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(2015, 1, 1)
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self.set_end_date(2018, 1, 1)
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self.settings.rebalance_portfolio_on_insight_changes = False
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self.settings.rebalance_portfolio_on_security_changes = False
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self.set_universe_selection(CustomUniverseSelectionModel("CustomUniverseSelectionModel", lambda time: [ "AAPL", "IBM", "FB", "SPY", "AIG", "BAC", "BNO" ]))
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self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, TimeSpan.from_minutes(20), 0.025, None))
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(self.rebalance_function))
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self.set_execution(ImmediateExecutionModel())
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self.last_rebalance_time = self.start_date
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def rebalance_function(self, time):
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# for performance only run rebalance logic once a week, monday
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if time.weekday() != 0:
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return None
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if self.last_rebalance_time == self.start_date:
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# initial rebalance
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self.last_rebalance_time = time
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return time
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deviation = 0
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count = sum(1 for security in self.securities.values() if security.invested)
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if count > 0:
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self.last_rebalance_time = time
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portfolio_value_per_security = self.portfolio.total_portfolio_value / count
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for security in self.securities.values():
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if not security.invested:
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continue
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reserved_buying_power_for_current_position = (security.buying_power_model.get_reserved_buying_power_for_position(
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ReservedBuyingPowerForPositionParameters(security)).absolute_used_buying_power
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* security.buying_power_model.get_leverage(security)) # see GH issue 4107
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# we sum up deviation for each security
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deviation += (portfolio_value_per_security - reserved_buying_power_for_current_position) / portfolio_value_per_security
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# if securities are deviated 1.5% from their theoretical share of TotalPortfolioValue we rebalance
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if deviation >= 0.015:
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return time
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return None
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def on_order_event(self, order_event):
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if order_event.status == OrderStatus.SUBMITTED:
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if self.utc_time != self.last_rebalance_time or self.utc_time.weekday() != 0:
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raise ValueError(f"{self.utc_time} {order_event.symbol}")
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