In 2018, I became interested in investing in options (calls and puts), but I wasn’t sure what fair price I should be paying for them. That’s when I discovered the Black-Scholes equation. I built a Python script that:

  • Connects with broker APIs to fetch real-time information about available calls and puts
  • Retrieves current stock market data
  • Web scrapes daily volatility reports
  • Creates a database containing all the gathered information and uses the Black-Scholes formula to calculate the theoretical value of options
  • Displays those calls being offered below their theoretical Black-Scholes valuation

I made several improvements to this script through the years, making it faster, more reliable, and more accurate.

Let’s Look at the Data

I usually use bullmarketbrokers as my main broker, but due to some problems with their API, I switched to invertironline’s API, which proved to be more reliable. The key point is that the data gathered from both brokers is the same and makes no difference for the analysis.

In a normal day we can see a table like this one for the stock market

Figure 1-1

and like this for puts and calls

Figure 1-2

So at the end, we have almost all the data required to perform the calculations using the Black-Scholes formula, with the exception of one critical component: the stock’s volatility.

Volatility

The volatility of each stock is not something you can obtain directly from the broker’s API. Fortunately, there are websites that continuously publish reports about stocks. I included functions that automatically download these reports (in PDF format) and extract the 52-week volatility for all the main stocks, appending this crucial information to the data we already gathered.

Figure 1-3

The Scripts

I used pandas, numpy, and math for data analysis, and requests and BeautifulSoup for web scraping.

All the data is gathered and stored locally by simply running Data_opciones.py.

Later, by running Opciones.py, the output looks like this for Calls:

Accion    P_A Tipo_opcion  Target       Vencmt.  Opcion_cotizacion         Vol  Teorico    %VT
0     ALUA   67.4        Call    31.0  1.639710e+09              33.70  0.3358    38.45  87.65 
1     ALUA   67.4        Call    61.0  1.639710e+09              10.00  0.3358    11.05  90.50 
4     ALUA   67.4        Call    69.0  1.639710e+09               5.00  0.3358     5.61  89.13 
5     ALUA   67.4        Call    71.0  1.639710e+09               4.00  0.3358     4.59  87.15 
6     ALUA   67.4        Call    73.0  1.639710e+09               2.45  0.3358     3.70  66.22 
..     ...    ...         ...     ...           ...                ...     ...      ...    ... 
275   YPFD  895.5        Call   880.0  1.639710e+09              92.00  0.4222   108.16  85.06 
279   YPFD  895.5        Call   960.0  1.639710e+09              65.00  0.4222    67.10  96.87 
282   YPFD  895.5        Call  1000.0  1.639710e+09              45.00  0.4222    51.51  87.36 
287   YPFD  895.5        Call  1150.0  1.639710e+09              15.50  0.4222    16.66  93.04 
290   YPFD  895.5        Call  1200.0  1.639710e+09              10.00  0.4222    10.96  91.24

All these options are currently priced below their Black-Scholes theoretical value—potentially undervalued opportunities!

Future Enhancements

I’m considering expanding this project to implement multi-leg options strategies (like spreads and straddles) that can offer high-probability profit opportunities in the stock market.