This project contains:
a) implementations and analysis of pricing models using Python.
b) implementations/analysis of the stock market using Python for data analysis.
There are 4 main folders:
Black-Scholes
Contains a Jupyter notebook B_s.ipynb implementing the Black-Scholes formula for European option valuation.
Linear_Algebra_Behind_Option
Contains a notebook labo.ipynb explaining the linear algebra concepts behind options pricing.
Stock_Market_Analysis
Contains a notebook SMA.ipynb retrieving historical data and performs analysis using Python and Pandas.
Simple Moving average
Contains a notebook SMA.ipynb retrieving historical data and performs analysis using Python and Pandas.
The project covers two key option pricing models:
Black-Scholes Formula
- Closed-form solution for European options
- Implemented in B_s.ipynb
- Requires underlying price, volatility, interest rates, time to maturity
- Provides quick and efficient pricing
Binomial Tree
- Discretized numerical model
- Allows early exercise valuation
- Implemented in labo.ipynb
- Intuitively represents different price paths
- Computationally more intensive than Black-Scholes
The models can be used to price European and American options. The notebooks contain examples of calculating prices under different market conditions.
The linear algebra notebook also explains how matrix operations can derive the risk-neutral pricing used in these models.
These notebooks provide an introduction to implementing and understanding key option pricing techniques in Python.