AlexCatarino
101 lines
4.2 KiB
Python
101 lines
4.2 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 datetime import datetime
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from csv import reader
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from urllib import urlopen
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from clr import AddReference
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AddReference("System.Core")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Data.UniverseSelection import *
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class DropboxUniverseSelectionAlgorithm(QCAlgorithm):
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'''In this algortihm we show how you can easily use the universe selection feature to fetch symbols
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to be traded using the AddUniverse method. This method accepts a function that will return the
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desired current set of symbols. Return Universe.Unchanged if no universe changes should be made'''
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def __init__(self):
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# the changes from the previous universe selection
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self.__changes = SecurityChanges.None
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# only used in backtest for caching the file results
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self.__backtestSymbolsPerDay = {}
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def CoarseSelectionFunction(self, dateTime):
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url = "https://www.dropbox.com/s/2az14r5xbx4w5j6/daily-stock-picker-live.csv?dl=1" \
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if self.LiveMode else \
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"https://www.dropbox.com/s/rmiiktz0ntpff3a/daily-stock-picker-backtest.csv?dl=1"
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# handle live mode file format
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if self.LiveMode:
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# fetch the file from dropbox
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file = urlopen(url).read()
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# if we have a file for today, break apart by commas and return symbols
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if len(file) > 0: return file.ToCsv()
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# no symbol today, leave universe unchanged
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return self.Universe.Unchanged
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# backtest - first cache the entire file
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if len(self.__backtestSymbolsPerDay) == 0:
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# fetch the file from dropbox only if we haven't cached the result already
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file = reader(urlopen(url))
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for line in file:
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date = datetime.strptime(line[0], '%Y%m%d')
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self.__backtestSymbolsPerDay[date] = line[1:]
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# if we have symbols for this date return them, else specify Universe.Unchanged
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return self.__backtestSymbolsPerDay.get(datetime(dateTime), self.Universe.Unchanged)
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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.SetStartDate(2013,01,01) #Set Start Date
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self.SetEndDate(2013,12,31) #Set End Date
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# this sets the resolution for data subscriptions added by our universe
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self.UniverseSettings.Resolution = Resolution.Daily
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self.AddUniverse("my-dropbox-universe", Resolution.Daily, self.CoarseSelectionFunction)
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def OnData(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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if len(data.Bars) == 0: return
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if self.__changes == SecurityChanges.None: return
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# start fresh
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self.Liquidate()
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percentage = 1./len(data.Bars)
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for tradeBar in data.Bars.Values:
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self.SetHoldings(tradeBar.Symbol, percentage)
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# reset changes
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self.__changes = SecurityChanges.None
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def OnSecuritiesChanged(self, changes):
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'''Event fired each time the we add/remove securities from the data feed'''
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# each time our securities change we'll be notified here
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self.__changes = changes |