Overall Statistics Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees \$0.00
```import numpy as np
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data.Consolidators import *
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(2014,8, 7)  #Set Start Date
self.SetEndDate(2014,8,12)    #Set End Date
self.SetCash(100000)           #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.spySymbol = equity.Symbol

self.volumeArray = [0 for i in range(5)]
self.arrayNum = 4

consolidator.DataConsolidated += self.DailyConsolidator

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

Arguments:
data: Slice object keyed by symbol containing the stock data
'''
def DailyConsolidator(self, sender, bar):

if self.arrayNum >= 0:
self.volumeArray[self.arrayNum] = bar.Volume
self.arrayNum = self.arrayNum - 1
else:
self.arrayNum = 4

self.Log("{0} {1}".format(str(bar), bar.Volume))
self.Log("{0} , {1}, {2}, {3}, {4}".format(self.volumeArray[0],self.volumeArray[1],self.volumeArray[2],self.volumeArray[3],self.volumeArray[4]))                        ```