wehub-resource-sync
74 lines
3.4 KiB
Python
74 lines
3.4 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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### <summary>
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### In this algorithm we demonstrate how to use the coarse fundamental data to define a universe as the top dollar volume and set the algorithm to use raw prices
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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 RawPricesCoarseUniverseAlgorithm(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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# what resolution should the data *added* to the universe be?
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self.universe_settings.resolution = Resolution.DAILY
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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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# Set the security initializer with the characteristics defined in CustomSecurityInitializer
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self.set_security_initializer(self.custom_security_initializer)
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# this add universe method accepts a single parameter that is a function that
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# accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
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self.add_universe(self.coarse_selection_function)
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self.__number_of_symbols = 5
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def custom_security_initializer(self, security):
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'''Initialize the security with raw prices and zero fees
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Args:
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security: Security which characteristics we want to change'''
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security.set_data_normalization_mode(DataNormalizationMode.RAW)
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security.set_fee_model(ConstantFeeModel(0))
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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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# this event fires whenever we have changes to our universe
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def on_securities_changed(self, changes):
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# liquidate removed securities
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for security in 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 changes.added_securities:
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self.set_holdings(security.symbol, 0.2)
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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.log(f"OnOrderEvent({self.utc_time}):: {order_event}")
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