wehub-resource-sync
53 lines
2.6 KiB
Python
53 lines
2.6 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 Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
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from Alphas.ConstantAlphaModel import ConstantAlphaModel
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from Execution.ImmediateExecutionModel import ImmediateExecutionModel
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from Risk.MaximumSectorExposureRiskManagementModel import MaximumSectorExposureRiskManagementModel
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### <summary>
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### This example algorithm defines its own custom coarse/fine fundamental selection model
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### with equally weighted portfolio and a maximum sector exposure.
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### </summary>
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class SectorExposureRiskFrameworkAlgorithm(QCAlgorithm):
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'''This example algorithm defines its own custom coarse/fine fundamental selection model
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### with equally weighted portfolio and a maximum sector exposure.'''
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def initialize(self):
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# Set requested data resolution
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self.universe_settings.resolution = Resolution.DAILY
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self.set_start_date(2014, 3, 25)
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self.set_end_date(2014, 4, 7)
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self.set_cash(100000)
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# set algorithm framework models
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self.set_universe_selection(FineFundamentalUniverseSelectionModel(self.select_coarse, self.select_fine))
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self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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self.set_risk_management(MaximumSectorExposureRiskManagementModel())
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def on_order_event(self, order_event):
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if order_event.status == OrderStatus.FILLED:
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self.debug(f"Order event: {order_event}. Holding value: {self.securities[order_event.symbol].holdings.absolute_holdings_value}")
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def select_coarse(self, coarse):
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tickers = ["AAPL", "AIG", "IBM"] if self.time.date() < date(2014, 4, 1) else [ "GOOG", "BAC", "SPY" ]
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return [Symbol.create(x, SecurityType.EQUITY, Market.USA) for x in tickers]
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def select_fine(self, fine):
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return [f.symbol for f in fine]
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