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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:

      Black-Scholes-Call

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

      Black-Scholes-Put

  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.