Finding a specific element in a matrix with set parameters

Question

I created a code for financial options and there is one last thing missing to finish it. I want my code to find the implied volatility for a specific option. My code asks the user to input the stock's ticker, date to maturity, strike price, and if the option is a call or a put.

Here is what I would like it to do:

1. Based on whether it is a call or a put, look for the suitable matrix that is given in the terminal.
2. Extract the implied volatility value conditional to the strike price (I can already handle the date and ticker in the options.get_options_chain function) the user inputted
3. Return the implied volatility

How should I go about this? I looked at many modules (pandas, pandas-DataReader, basic Python functions, NumPy, sys, etc.) and their documentation, and nothing seems to fit what I want. Here is an example of the output that I am getting from the data that I want.

from yahoo_fin import options

chain = options.get_options_chain("nflx", "2022-06-10")
print(chain)

$ python implied
{'calls':           Contract Name         Last Trade Date  Strike  Last Price    Bid    Ask  Change % Change Volume  Open Interest Implied Volatility
0   NFLX220610C00120000  2022-05-04 12:27PM EDT   120.0       75.50  46.50  49.60    0.00        -      -              0             87.26%
1   NFLX220610C00130000  2022-05-09 10:25AM EDT   130.0       50.05   0.00   0.00    0.00        -      -              0              0.00%
2   NFLX220610C00135000   2022-05-05 1:50PM EDT   135.0       55.75  34.50  35.60    0.00        -      -              1             81.37%
3   NFLX220610C00145000  2022-05-10 10:04AM EDT   145.0       36.77   0.00   0.00    0.00        -      -              0              0.00%

'puts':           Contract Name         Last Trade Date  Strike  Last Price    Bid    Ask  Change % Change Volume Open Interest Implied Volatility
0   NFLX220610P00100000   2022-05-11 3:59PM EDT   100.0        0.59   0.56   0.66    0.19  +47.50%     83            59            100.05%
1   NFLX220610P00105000  2022-05-11 11:43AM EDT   105.0        0.40   0.71   0.86   -0.20  -33.33%      2             1             96.48%
2   NFLX220610P00110000   2022-05-11 3:22PM EDT   110.0        0.97   0.96   1.10    0.38  +64.41%     35             6             93.51%

Answer 1

The result set you get is a dict with the keys calls or puts and a pd.DataFrame as a value.

Access the dict first, then select the required data in the DataFrame by selecting the strike (right row) and Implied Volatility (right column).

With iloc[0] you will get the first element within the pd.Series.

from yahoo_fin import options
import pandas as pd

# set basic parameters e. g., maturity, option type
option_type = 'calls'
strike = 180.0
last_trade_date = '2022-05-10'

chain = options.get_options_chain("nflx", "2022-06-10")
print(chain)

df = pd.DataFrame(chain.get(option_type))
vola = df[df['Strike'] == strike]['Implied Volatility'].iloc[0]

Output:

3.13%