920linjerry-stack/capital-studio — explained in plain English
Analysis updated 2026-05-18
Build a DCF valuation model with adjustable growth and discount rate assumptions.
Construct an LBO model with configurable debt structures and exit scenarios.
Simulate an M&A deal through the card-game style Deal Arena to test synergy and viability.
Export any model to a formula-native Excel workbook for further editing.
| 920linjerry-stack/capital-studio | adya84/ha-world-cup-2026 | afk-surf/safeclipper | |
|---|---|---|---|
| Stars | 16 | 16 | 16 |
| Language | Python | Python | Python |
| Setup difficulty | easy | easy | moderate |
| Complexity | 3/5 | 2/5 | 3/5 |
| Audience | researcher | general | developer |
Figures from each repo's GitHub metadata at analysis time.
Standard Python venv plus pip install, then run app.py and open localhost in a browser.
Capital Studio is a financial modeling tool that runs entirely on your own computer. It is aimed at students, researchers, and analysts who want to practice or study three classic methods used in investment analysis: DCF valuation, LBO modeling, and M&A deal simulation. All calculations happen locally, and no data is sent to external servers or AI services. DCF stands for Discounted Cash Flow, a technique for estimating what a company is worth by projecting its future cash earnings and working backward to a present value. Capital Studio provides a dashboard for setting the assumptions behind those projections, such as growth rates and discount rates. It can export the model to an Excel file where every cell contains live formulas rather than static numbers, so you can continue adjusting the model directly in Excel. LBO stands for Leveraged Buyout, a type of acquisition financed mostly with borrowed money. The tool lets you build an LBO model with configurable entry and exit assumptions, debt structures, and return scenarios, and exports a formula-native Excel workbook with the same live-formula structure. The M&A Arena is the most distinctive part. It frames merger analysis as a card game where two companies are matched against each other, and it works through synergy, viability, and accretion or dilution logic to evaluate whether a deal makes financial sense. The results are fully deterministic: the same inputs always produce the same output, with no randomness and no AI involved. The app supports US stocks, Hong Kong stocks, and mainland China A-shares. Price and financial data is pulled from public sources. The README notes data may be delayed or occasionally unavailable, and is explicit that the tool is for educational and research use only, not investment advice. It runs as a local web app accessible from your browser after a standard Python setup.
A local financial modeling app for practicing DCF valuation, LBO modeling, and M&A deal simulation, with everything running on your own machine.
Mainly Python. The stack also includes Python, Flask, yfinance.
MIT license: use, modify, and distribute freely, including commercially, as long as you keep the copyright notice.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.