Options research strategy

To analyze options data and filter the options chain, you can use the OptionChainProvider class. This class provides a list of option contracts for an underlying symbol at a requested date. You can then manually filter the contract list returned by GetOptionContractList. The manual filtering will be limited to the information included in the Symbol (strike, expiration, type, style) and/or prices from a History call.

Here is an example of how you can filter the options chain to obtain the correct option:

def OptionsFilter(self, data):
    ''' OptionChainProvider gets a list of option contracts for an underlying symbol at requested date.
    Then you can manually filter the contract list returned by GetOptionContractList.
    The manual filtering will be limited to the information included in the Symbol
    (strike, expiration, type, style) and/or prices from a History call '''

    contracts = self.OptionChainProvider.GetOptionContractList(self.symbol, data.Time)
    self.underlyingPrice = self.Securities[self.symbol].Price
    # filter the out-of-money put options from the contract list which expire close to self.DTE num of days from now
    otm_puts = [i for i in contracts if i.ID.OptionRight == OptionRight.Put and
    i.ID.StrikePrice < self.OTM * self.underlyingPrice and
    self.DTE - 4 < (i.ID.Date - data.Time).days < self.DTE + 4]

    if len(otm_puts) > 0:
        # sort options by closest to self.DTE days from now and desired strike, and pick first
        contract = sorted(sorted(otm_puts, key = lambda x: abs((x.ID.Date - self.Time).days - self.DTE)),
        key = lambda x: abs(x.ID.StrikePrice - self.underlyingPrice))[0]
        if contract not in self.contractsAdded:
            self.contractsAdded.add(contract)
            # use AddOptionContract() to subscribe the data for specified contract
            self.AddOptionContract(contract, Resolution.Minute)

In this example, the OptionsFilter function is used to filter out-of-the-money put options from the contract list which expire close to a specified number of days from now (self.DTE). The options are then sorted by the closest to self.DTE days from now and the desired strike, and the first option is picked.

To sell the option, you can use the SetHoldings function:

# if not invested and option data added successfully, sell option
if not self.Portfolio[self.contract].Invested and data.ContainsKey(self.contract):
    self.SetHoldings(self.contract, -0.02)  # sell 0.02 delta puts

Please note that this is a simplified example and you might need to adjust it according to your specific needs, such as adding stop loss and take profit logic.

For more information, you can refer to the QuantConnect documentation and the QuantConnect forum.

i wan to do this analysis in the research notebook.

To perform this analysis in the research notebook, you can use the QuantBook class. This class allows you to access QuantConnect's data and API methods in the research environment. Here is an example of how you can use QuantBook to analyze options data:

# Create a QuantBook instance
qb = QuantBook()

# Select the asset data
spy = qb.AddEquity("SPY")
option = qb.AddOption("SPY")

# Set the filter for the options
option.SetFilter(-2, 2, timedelta(0), timedelta(15))

# Get the options chain
option_chain = qb.GetOptionChain(option.Symbol)

# Filter the options chain to get the put options
put_options = [i for i in option_chain if i.ID.OptionRight == OptionRight.Put]

# Further filter the put options to get the ones with 0.02 delta
desired_puts = [i for i in put_options if i.Greeks.Delta == 0.02]

# Now you can analyze the desired_puts as per your requirements

Please note that this is a simplified example and you might need to adjust it according to your specific needs. For more information, you can refer to the QuantConnect documentation and the QuantConnect forum.

i get this error:

AttributeError                            Traceback (most recent call last)

Cell In[11], line 9

      7 option.SetFilter(-2, 2, timedelta(0), timedelta(15))

      8 # Get the options chain

----> 9 option_chain = qb.GetOptionChain(option.Symbol)

     10 # Filter the options chain to get the put options

     11 put_options = [i for i in option_chain if i.ID.OptionRight == OptionRight.Put]

AttributeError: 'QuantBook' object has no attribute 'GetOptionChain'

I apologize for the confusion. The GetOptionChain method is not available in the Research environment. In the Research environment, you can use the GetOptionHistory method to get historical option data. Here is an example:

# Create a QuantBook instance
qb = QuantBook()

# Select the asset data
spy = qb.AddEquity("SPY")
option = qb.AddOption("SPY")

# Set the filter for the options
option.SetFilter(-2, 2, timedelta(0), timedelta(15))

# Get the historical option data
option_history = qb.GetOptionHistory(option.Symbol, datetime(2021, 1, 1), datetime(2022, 1, 1))

# Get the options chain from the historical data
option_chain = option_history.GetAllData()

# Filter the options chain to get the put options
put_options = [i for i in option_chain if i.ID.OptionRight == OptionRight.Put]

# Further filter the put options to get the ones with 0.02 delta
desired_puts = [i for i in put_options if i.Greeks.Delta == 0.02]

# Now you can analyze the desired_puts as per your requirements

Please note that this is a simplified example and you might need to adjust it according to your specific needs. For more information, you can refer to the QuantConnect documentation and the QuantConnect forum.

1. put_options = [i for i in option_chain if i.ID.OptionRight == OptionRight.Put]
   gives error:
   AttributeError: 'str' object has no attribute 'ID'

The error message suggests that the option_chain variable is a string, not a list of option contracts. Therefore, it doesn't have the ID attribute.

When you use the GetOptionHistory method, it returns a Slice object that contains the historical data. You need to access the OptionChains property of this object to get the option chains.

Here is an example of how you can do this:

# Get the historical option data
option_history = qb.GetOptionHistory(option.Symbol, datetime(2021, 1, 1), datetime(2022, 1, 1))

# Get the options chain from the historical data
option_chains = option_history.GetAllData()

# Filter the options chain to get the put options
put_options = [i for i in option_chains if i.Symbol.ID.OptionRight == OptionRight.Put]

Please note that this is a simplified example and you might need to adjust it according to your specific needs. For more information, you can refer to the QuantConnect documentation and the QuantConnect forum.

this is the wrong syntax. option_chains is a multi index pandas dataframe

I apologize for the confusion. You're correct, the GetOptionHistory method returns a pandas DataFrame with a multi-index.

To filter the DataFrame for put options, you can use the pandas DataFrame methods. Here is an example:

# Get the historical option data
option_history = qb.GetOptionHistory(option.Symbol, datetime(2021, 1, 1), datetime(2022, 1, 1))

# Get the options chain from the historical data
option_chains = option_history.GetAllData()

# Filter the options chain to get the put options
put_options = option_chains[option_chains["type"] == "put"]

In this example, option_chains["type"] == "put" creates a boolean mask that is True for rows where the type column is "put". This mask is then used to index option_chains, resulting in a DataFrame that only includes put options.

Please note that this is a simplified example and you might need to adjust it according to your specific needs. For more information, you can refer to the QuantConnect documentation and the QuantConnect forum.