wehub-resource-sync
80 lines
3.5 KiB
Python
80 lines
3.5 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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### Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="coarse universes" />
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### <meta name="tag" content="fine universes" />
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class CoarseFineFundamentalComboAlgorithm(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.set_start_date(2014,1,1) #Set Start Date
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self.set_end_date(2015,1,1) #Set End Date
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self.set_cash(50000) #Set Strategy Cash
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# what resolution should the data *added* to the universe be?
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self.universe_settings.resolution = Resolution.DAILY
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# this add universe method accepts two parameters:
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# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
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# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
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self.add_universe(self.coarse_selection_function, self.fine_selection_function)
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self.__number_of_symbols = 5
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self.__number_of_symbols_fine = 2
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self._changes = None
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# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
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def coarse_selection_function(self, coarse):
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# sort descending by daily dollar volume
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sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True)
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# return the symbol objects of the top entries from our sorted collection
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return [ x.symbol for x in sorted_by_dollar_volume[:self.__number_of_symbols] ]
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# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
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def fine_selection_function(self, fine):
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# sort descending by P/E ratio
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sorted_by_pe_ratio = sorted(fine, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)
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# take the top entries from our sorted collection
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return [ x.symbol for x in sorted_by_pe_ratio[:self.__number_of_symbols_fine] ]
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def on_data(self, data):
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# if we have no changes, do nothing
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if self._changes is None: return
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# liquidate removed securities
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for security in self._changes.removed_securities:
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if security.invested:
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self.liquidate(security.symbol)
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# we want 20% allocation in each security in our universe
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for security in self._changes.added_securities:
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self.set_holdings(security.symbol, 0.2)
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self._changes = None
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# this event fires whenever we have changes to our universe
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def on_securities_changed(self, changes):
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self._changes = changes
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