DCF Model

This repository contains a Discounted Cash Flow (DCF) valuation model, implemented as a shared Python valuation engine and an interactive web application.

The project ensures that both the exploratory Jupyter notebook and the full-stack web application produce identical outputs by running on the same underlying DCF logic.

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Project Overview

The project is divided into two primary environments:

1. Python Model (python_model/)

   • The core valuation engine and financial logic.
   • Handles data fetching, fallback pricing, and canonical formulas.
   • Includes a Jupyter notebook for static walkthroughs and exploration.

2. Interactive Site (interactive_site/)

   • A full-stack web application for building and exploring DCF models interactively.
   • Built with a Next.js frontend and FastAPI backend.
   • Leverages the shared Python package from python_model/ for all core math.

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What This Project Does

1. Python Valuation Engine

The python_model/ directory houses the core logic. This is the best place to understand the model quickly without running the full web app.

• dcf_engine.py – Forecast building, discounting, and sensitivity analysis.
• data_api.py – Fetching and fallback logic for market data and fundamentals.
• dcf_model.ipynb – A GitHub-friendly notebook demonstrating the model's outputs.

Run the notebook setup:

python3 -m pip install -r python_model/requirements.txt

Then open python_model/dcf_model.ipynb.

2. Interactive Web Application

The interactive_site/ directory contains the product side:

• Frontend (app/, components/) – Next.js UI components and valuation workspaces.
• Backend (backend/) – FastAPI backend that handles search, valuation orchestration, and market data fetching.

Run the application:

cd interactive_site
npm install
npm run dev

Access the frontend on http://localhost:4000 and the backend on http://127.0.0.1:8000.

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Resiliency and Features

• Robust Fallbacks: The system gracefully handles missing market data using alternative exchange APIs (Yahoo, FMP), historical averages, or conservative defaults.
• Native Currency Support: Listings preserve and use their native currencies (e.g., USD, GBP, INR, JPY) throughout the valuation process.

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Notes

• Generated and temporary folders (.next, __pycache__, node_modules, .ipynb_checkpoints, etc.) are intentionally omitted and should not be committed.
• The repository is kept intentionally lean, focusing purely on the shared model, the notebook, and the interactive UI.