# 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 *
class CoarseFineUniverseSelectionBenchmark(QCAlgorithm):
def initialize(self):
self.set_start_date(2017, 11, 1)
self.set_end_date(2018, 3, 1)
self.set_cash(50000)
self.universe_settings.resolution = Resolution.MINUTE
self.add_universe(self.coarse_selection_function, self.fine_selection_function)
self.number_of_symbols = 150
self.number_of_symbols_fine = 40
self._changes = None
# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def coarse_selection_function(self, coarse):
selected = [x for x in coarse if (x.has_fundamental_data)]
# sort descending by daily dollar volume
sorted_by_dollar_volume = sorted(selected, 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] ]
# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
def fine_selection_function(self, fine):
# sort descending by P/E ratio
sorted_by_pe_ratio = sorted(fine, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)
# take the top entries from our sorted collection
return [ x.symbol for x in sorted_by_pe_ratio[:self.number_of_symbols_fine] ]
def on_data(self, data):
# 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)
for security in self._changes.added_securities:
self.set_holdings(security.symbol, 0.02)
self._changes = None
def on_securities_changed(self, changes):
self._changes = changes