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
Probabilistic 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
-1.105
Tracking Error
0.04
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
import numpy as np
from datetime import timedelta
from QuantConnect.Securities.Option import OptionPriceModels

### <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):
        self.SetStartDate(2017, 6, 1)
        self.SetEndDate(2017, 6, 15)
        self.SetWarmUp(30,Resolution.Daily) # Warm up technical indicators
        self.AddEquity("IBM", Resolution.Minute)
        self.option = self.AddOption("IBM", Resolution.Minute)
        self.option.SetFilter(-3, +3, timedelta(0), timedelta(30))
        self.option.PriceModel = OptionPriceModels.CrankNicolsonFD()
        
        
        self.Schedule.On(self.DateRules.EveryDay("IBM"),self.TimeRules.AfterMarketOpen("IBM", 60),Action(self.print_first_hour))

    def OnData(self, slice):
        self.option_data = slice
        
    
        

    def print_first_hour(self):
        for chain in self.option_data.OptionChains.Values:
            self.Log(f'{self.Time}--> Values of Delta: {[x.Greeks.Delta for x in chain]}')