wehub-resource-sync
78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
# 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}")
|