Stock Analysis and Option Pricing

This project is a comprehensive tool for analyzing stocks and pricing options using two models:

• Black-Scholes Model

• Monte Carlo Simulations

It includes features such as stock price visualization with moving averages, RSI calculation, next-day stock price prediction, and interactive visualizations.

Features

1. Black-Scholes Option Pricing

   • Calculates the price of European-style options.

   • Outputs key metrics known as Greeks:

     • Delta: Rate of change of the option price with respect to the stock price.

     • Gamma: Rate of change of Delta with respect to the stock price.

     • Theta: Rate of change of the option price with respect to time.

     • Vega: Sensitivity of the option price to volatility.

     • Rho: Sensitivity of the option price to interest rates.

   • Call Option (C) is calculated using the following formula:

     

   • Put Option (P) is calculated using the following formula (the same formulas for d1 and d2 are used as above):

     

2. Monte Carlo Simulation

   • Simulates multiple stock price paths to estimate the price of European-style options.

   • Interactive graph to visualize up to 10,000 simulated paths.

   • Simulation

     • Each price path is modeled using the stochastic differential equation where:

       • Time step (trading days = 252/year)

       • Random variable sampled from a standard normal distribution

3. Stock Data Visualization

   • Historical stock prices with 20-day, 50-day, and 200-day moving averages.

   • Relative Strength Index (RSI) with overbought (70) and oversold (30) levels.

4. Next-Day Stock Price Prediction

   • Predicts the next closing price using linear regression trained on historical stock data.

5. Streamlit-Based Interface

   • Interactive web interface with:

     • Input fields for stock ticker, strike price, volatility, risk-free rate, and time to maturity.

     • Option to select between Black-Scholes or Monte Carlo models.

     • Dynamic graphs for simulation paths and plot visualization.

Installation

Prerequisites

• Python 3.8 or higher

• Required libraries:

  • numpy

  • pandas

  • scipy

  • sklearn

  • plotly

  • streamlit

  • yfinance

• Install these dependencies using "pip install"

• After navigating to the project directory, run using: python -m streamlit run main.py

Usage

1. Select Pricing Model

   • Choose either "Black-Scholes" or "Monte Carlo" from the sidebar.

2. Input Parameters:

   • Enter stock ticker symbol (e.g., AAPL).

   • Specify strike price, volatility, risk-free rate, time to maturity, and option type (call/put).

     • The 10 year treasury yield is used for the risk free rate

   • If using Monte Carlo, adjust the number of simulation paths.

3. Visualize Results:

   • View historical stock price with moving averages and RSI.

   • See predicted next closing price.

   • Get the option price and Greeks for Black-Scholes or the simulated price paths for Monte Carlo.

File Structure

• main.py: Streamlit interface for the application.

• stock_prediction.py: Handles data fetching, preparation, and price prediction.

• MonteCarloBasic.py: Contains Monte Carlo simulation logic and interactive path visualization.

• blackScholes.py: Implements the Black-Scholes model and Greeks calculation.

• requirements.txt: Lists project dependencies.

Contributing

1. Fork the repository.

2. Create a new branch: git checkout -b feature-name

3. Commit your changes: git commit -m "Add new feature"

4. Push to the branch: git push origin feature-name

5. Open a pull request.