Overall Statistics Total Trades0Average Win0%Average Loss0%Compounding Annual Return0%Drawdown0%Expectancy0Net Profit0%Sharpe Ratio0Loss Rate0%Win Rate0%Profit-Loss Ratio0Alpha0Beta0Annual Standard Deviation0Annual Variance0Information Ratio0Tracking Error0Treynor Ratio0Total Fees\$0.00
```import numpy as np
from pprint import pprint
import pandas as pd
from datetime import timedelta

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''

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.SetStartDate(2016,10,01)  #Set Start Date
self.SetEndDate(2016,11,16)    #Set End Date
self.SetCash(25000)           #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
option.SetFilter(-10, +10, timedelta(0), timedelta(180))
# option.SetFilter(-2, 2, TimeSpan.FromDays(30), TimeSpan.FromDays(180));

def CoarseSelectionFunction(self, coarse):
'''Take the top 5 by dollar volume using coarse'''
# sort descending by daily dollar volume
sortedByDollarVolume = sorted(coarse, \
key=lambda x: x.DollarVolume, reverse=True)

# for x in sortedByDollarVolume[:5]:

# we need to return only the symbol objects
return [ x.Symbol for x in sortedByDollarVolume[:5] ]