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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from AlgorithmImports import *
### <summary>
### Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
### <meta name="tag" content="fine universes" />
class CoarseFundamentalTop3Algorithm(QCAlgorithm):
def initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.set_start_date(2014,3,24) #Set Start Date
self.set_end_date(2014,4,7) #Set End Date
self.set_cash(50000) #Set Strategy Cash
# what resolution should the data *added* to the universe be?
self.universe_settings.resolution = Resolution.DAILY
# this add universe method accepts a single parameter that is a function that
# accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
self.add_universe(self.coarse_selection_function)
self.__number_of_symbols = 3
self._changes = None
# sort the data by daily dollar volume and take the top '__number_of_symbols'
def coarse_selection_function(self, coarse):
# sort descending by daily dollar volume
sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True)
# return the symbol objects of the top entries from our sorted collection
return [ x.symbol for x in sorted_by_dollar_volume[:self.__number_of_symbols] ]
def on_data(self, data):
self.log(f"OnData({self.utc_time}): Keys: {', '.join([key.value for key in data.keys()])}")
# if we have no changes, do nothing
if self._changes is None: return
# liquidate removed securities
for security in self._changes.removed_securities:
if security.invested:
self.liquidate(security.symbol)
# we want 1/N allocation in each security in our universe
for security in self._changes.added_securities:
self.set_holdings(security.symbol, 1 / self.__number_of_symbols)
self._changes = None
# this event fires whenever we have changes to our universe
def on_securities_changed(self, changes):
self._changes = changes
self.log(f"OnSecuritiesChanged({self.utc_time}):: {changes}")
def on_order_event(self, fill):
self.log(f"OnOrderEvent({self.utc_time}):: {fill}")