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
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(2017, 01, 01)  #Set Start Date
self.SetEndDate(2017, 06, 30)    #Set End Date
self.SetCash(100000)           #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock
self.symbol = option.Symbol
# In Initialize
self.OptionSymbol = option.Symbol;
self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
# filter the contracts with strikes between(ATM Strike - 10 * strike space value, market price + 10 * strike space value) and with expiration days less than 180 days
option.SetFilter(-10, +10, timedelta(0), timedelta(180))

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
'''

for i in data.OptionChains:
if i.Key != self.symbol: continue
optionchain = i.Value
self.Log("underlying price:" + str(optionchain.Underlying.Price))
df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
index=[x.Symbol.Value for x in optionchain],
columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
self.Log(str(df))```