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
        equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google
        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))