Financial Modeling

Hands-on exploration of quantitative finance models through two complementary tracks:

• Python prototypes under python-model/ for rapid experimentation and mathematical validation
• A TypeScript visualizer in ts-visualizer/ that turns validated models into interactive dashboards

The goal is to understand theory, document assumptions, and present results with polished visuals that are easy to share.

Repository Layout

• python-model/ contains per-model Python implementations and write-ups
• ts-visualizer/ hosts the React + TypeScript application and related documentation
• docs/ (if present within directories) provides model-specific notes, derivations, or usage tips

Getting Started

Python prototypes

1. Install Python 3.11+ and create a virtual environment
2. Navigate to a model directory under python-model/
3. Follow the local README for dependencies (most rely on the standard library, some use NumPy or SciPy)
4. Run the scripts or notebooks to reproduce calculations and sample scenarios

TypeScript visualizer

1. Navigate to ts-visualizer/
2. Install dependencies with npm install
3. Start the dev server via npm run dev
4. For production builds use npm run build and refer to ts-visualizer/docs/start-new-visualizer.md for GitHub Pages deployment details

Documentation

• ts-visualizer/README.md describes the app shell, feature set, and scripts
• ts-visualizer/docs/adding-model.md outlines how to register additional models
• ts-visualizer/docs/models/{MODEL_NAME}.md captures background, validation, and UI behavior for the my model implementation

Model Roadmap

ID	Model	Python prototype	Visualizer implementation	Notes
00	Black-Scholes Model	Complete	Complete	European option pricing with closed-form solution
01	Dividend Discount Model (DDM)	Planned	Planned	Foundational equity valuation using dividend growth
02	Three-Statement Model	Planned	Planned	Links Income Statement, Balance Sheet, and Cash Flow dynamically
03	Discounted Cash Flow (DCF)	Planned	Planned	Intrinsic valuation via projected free cash flow discounting
04	Comparable Company Analysis	Planned	Planned	Relative valuation using market multiples
05	Capital Asset Pricing Model (CAPM)	Planned	Planned	Single-factor model linking beta to expected returns
06	Markowitz Portfolio Optimization	Planned	Planned	Mean-variance allocation to find efficient frontier
07	Binomial Option Pricing	Planned	Planned	Discrete tree-based method for American options
08	Monte Carlo Option Pricing	Planned	Planned	Simulation-based pricing for path-dependent derivatives
09	Value at Risk (VaR)	Planned	Planned	Statistical risk measurement via distribution tails
10	GARCH Volatility Forecasting	Planned	Planned	Time-series model for volatility clustering
11	Fama-French Three-Factor Model	Planned	Planned	Extends CAPM with size and value risk factors
12	Black-Litterman Asset Allocation	Planned	Planned	Bayesian blend of market equilibrium and investor views
13	Leveraged Buyout (LBO) Model	Planned	Planned	Private equity model analyzing debt-financed acquisitions
14	Merger & Acquisition (M&A) Model	Planned	Planned	Pro-forma analysis of combined entity financials
15	Interest Rate Models (Vasicek/CIR)	Planned	Planned	Stochastic models for interest rate evolution
16	Heston Stochastic Volatility Model	Planned	Planned	Advanced option pricing with random volatility dynamics
17	Credit Default Swap (CDS) Pricing	Planned	Planned	Derivative pricing for credit risk protection

Workflow Overview

1. Prototype new models in Python to internalize theory and verify calculations
2. Capture findings, assumptions, and validation notes in per-model documentation
3. Port the model into ts-visualizer/ using the shared FinancialModelDefinition contract
4. Register the model so it appears in the UI and document the visualization steps
5. Optionally extract reusable pieces into portable/ for other analytics dashboards

Future Extensions

• Expand the model set across asset classes and risk methodologies
• Integrate historical market data pulls for calibration and backtesting
• Add automated tests comparing Python outputs with the visualizer implementation
• Explore deployment of the visualizer toolkit to economics or statistics domains using the portable/ package

License

This repository is licensed under MIT. Use, modify, and share freely.