# 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.Algorithm import *

from QuantConnect.Algorithm import QCAlgorithm

from QuantConnect.Data.UniverseSelection import *

from datetime import datetime

import decimal as d

import pandas as pd

### <summary>

### In this algortihm we show how you can easily use the universe selection feature to fetch symbols

### to be traded using the BaseData custom data system in combination with the AddUniverse{T} method.

### AddUniverse{T} requires a function that will return the symbols to be traded.

### </summary>

### <meta name="tag" content="using data" />

### <meta name="tag" content="universes" />

### <meta name="tag" content="custom universes" />

class DropboxUniverseSelectionAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2013,1,1)

self.SetEndDate(2013,12,31)

self.backtestSymbolsPerDay = None

self.current_universe = []

self.UniverseSettings.Resolution = Resolution.Daily;

self.AddUniverse("my-dropbox-universe", self.selector)

def selector(self, data):

# handle live mode file format

if self.LiveMode:

# fetch the file from dropbox

url = "https://www.dropbox.com/s/2az14r5xbx4w5j6/daily-stock-picker-live.csv?dl=1"

df = pd.read_csv(url, header = None)

# if we have a file for today, return symbols

if not df.empty:

self.current_universe = df.iloc[0,:].tolist()

# no symbol today, leave universe unchanged

return self.current_universe

# backtest - first cache the entire file

if self.backtestSymbolsPerDay is None:

url = "https://www.dropbox.com/s/rmiiktz0ntpff3a/daily-stock-picker-backtest.csv?dl=1"

self.backtestSymbolsPerDay = pd.read_csv(url, header = None, index_col = 0)

index = int(data.strftime("%Y%m%d"))

if index in self.backtestSymbolsPerDay.index:

self.current_universe = self.backtestSymbolsPerDay.loc[index,:].dropna().tolist()

return self.current_universe

def OnData(self, slice):

if slice.Bars.Count == 0: return

if self.changes == None: return

# start fresh

self.Liquidate()

percentage = 1 / d.Decimal(slice.Bars.Count)

for tradeBar in slice.Bars.Values:

self.SetHoldings(tradeBar.Symbol, percentage)

# reset changes

self.changes = None

def OnSecuritiesChanged(self, changes):

self.changes = changes