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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 clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Securities import *
from datetime import datetime
### <summary>
### In this algortihm, we fetch a list of tickers with corresponding dates from a file on Dropbox.
### We then create a fine fundamental universe which contains those symbols on their respective dates.###
### </summary>
### <meta name="tag" content="download" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="custom data" />
class DropboxCoarseFineAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 9, 23) # Set Start Date
self.SetEndDate(2019, 9, 30) # Set End Date
self.SetCash(100000) # Set Strategy Cash
self.AddUniverse(self.SelectCoarse, self.SelectFine)
self.universeData = None
self.nextUpdate = datetime(1, 1, 1) # Minimum datetime
self.url = "https://www.dropbox.com/s/x2sb9gaiicc6hm3/tickers_with_dates.csv?dl=1"
def OnEndOfDay(self):
for security in self.ActiveSecurities.Values:
self.Debug(f"{self.Time.date()} {security.Symbol.Value} with Market Cap: ${security.Fundamentals.MarketCap}")
def SelectCoarse(self, coarse):
return self.GetSymbols()
def SelectFine(self, fine):
symbols = self.GetSymbols()
# Return symbols from our list which have a market capitalization of at least 10B
return [f.Symbol for f in fine if f.MarketCap > 1e10 and f.Symbol in symbols]
def GetSymbols(self):
# In live trading update every 12 hours
if self.LiveMode:
if self.Time < self.nextUpdate:
# Return today's row
return self.universeData[self.Time.date()]
# When updating set the new reset time.
self.nextUpdate = self.Time + timedelta(hours=12)
self.universeData = self.Parse(self.url)
# In backtest load once if not set, then just use the dates.
if self.universeData is None:
self.universeData = self.Parse(self.url)
# Check if contains the row we need
if self.Time.date() not in self.universeData:
return Universe.Unchanged
return self.universeData[self.Time.date()]
def Parse(self, url):
# Download file from url as string
file = self.Download(url).split("\n")
# # Remove formatting characters
data = [x.replace("\r", "").replace(" ", "") for x in file]
# # Split data by date and symbol
split_data = [x.split(",") for x in data]
# Dictionary to hold list of active symbols for each date, keyed by date
symbolsByDate = {}
# Parse data into dictionary
for arr in split_data:
date = datetime.strptime(arr[0], "%Y%m%d").date()
symbols = [Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in arr[1:]]
symbolsByDate[date] = symbols
return symbolsByDate
def OnSecuritiesChanged(self, changes):
self.Log(f"Added Securities: {[security.Symbol.Value for security in changes.AddedSecurities]}")