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

### <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(2018,9, 18)  #Set Start Date
self.SetEndDate(2018,10,18)    #Set End Date
self.SetCash(100000)           #Set Strategy Cash

self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))

# Find more symbols here: http://quantconnect.com/data

self.resolution = Resolution.Daily

self.tr_max_period = 5
self.tr = self.TR(eurusd.Symbol, self.resolution)
self.RegisterIndicator(eurusd.Symbol, self.tr, self.resolution)

self.tr_max = IndicatorExtensions.MAX(self.tr, self.tr_max_period)
self.RegisterIndicator(eurusd.Symbol, self.tr_max, self.resolution)

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
'''
self.Log("High minus Low: "+str(data["EURUSD"].High-data["EURUSD"].Low )+" | TR: "+str(self.tr.Current.Value)+" | TR_MAX: "+str(self.tr_max)+" | TR_MAX IsReady: "+str(self.tr_max.IsReady) )                        ```