Run a valuation before studying the formulas
Start with the CLI, learn the output order, and see why a skipped model can be more useful than another per-share estimate.
Read this publication →A more disciplined way to invest
I had invested conservatively for years and earned good returns, but I wanted to expand my portfolio without relying on a promising ticker or a single familiar ratio. I needed a repeatable way to ask whether a company looked fairly valued or undervalued.
The engine grew from that need. It turns one ticker into several valuation arguments, records weak or missing data, and checks whether each model belongs in the analysis before it runs. It cannot remove the risk of investing, but making larger decisions without that discipline would leave me with even less to challenge.
From ticker symbol to valuation report
The pipeline separates data loading, domain modeling, model policy, calculation, and presentation. Each layer passes a structured result to the next one instead of quietly repeating earlier work.
Flowchart
Start with the result, then inspect the evidence
Choose a model that fits the business
Match cash flow, earnings, revenue, dividends, returns, and assets to the question each valuation method can answer.
Read this publication →Check the inputs behind the result
Follow provider fields into StockMetrics, inspect selected missing-value reasons, and understand the currency boundary that still needs work.
Read this publication →Different models answer different questions
Cash flow, earnings, revenue, dividends, returns, and net assets describe different parts of a business. The engine runs each method through the same manager contract without pretending their answers should agree.
Cash flow
DCF and Reverse DCF
Forward DCF estimates intrinsic value from projected cash flows; Reverse DCF asks what growth the current market price already assumes.
Market comparison
P/E, EV/EBITDA, and P/S
Comparable-value models use earnings, operating performance, revenue, and configured multiples without pretending one method fits every business.
Business structure
ROE, DDM, and NAV
Return, dividend, and asset-based models help when cash-flow or market-multiple approaches do not fit the business well.
Assumptions remain part of the output
Growth does not enter a formula as one unexplained percentage. The engine selects a signal, records its source, constrains unstable values, and builds Bear, Base, and Bull paths for models that need projections.
Optional weighted blending and sector mean reversion remain explicit. When evidence is too thin for the richer path, the system falls back and records why.
Scenario engine
See how signals become projections
See how selection, clamping, scenario adjustments, and mean reversion become a growth path you can inspect.
Read the decision path →One run, five visible stages
- Normalize
- Diagnose
- Qualify
- Value
- Explain
Human-readable at the terminal
CLI tables make checks, scenarios, and comparisons useful during exploration without hiding skipped methods or analytical warnings.
Structured for the next interface
Compact JSON carries metrics, checks, reports, skips, and the summary as an integration surface rather than scraping values back out of terminal text.
The foundation of a larger pipeline
The engine's structured JSON is not only a CLI output — it is the anchor for two other projects. Equilyze wraps the engine and layers AI agents on top to write investment analysis, and Pressroom then turns that analysis into a finished, published article.
Read more: how three projects merge into one pipeline →The outcome
A valuation run that can be questioned, traced, and compared instead of merely quoted.
The estimates are analytical tools, not investment advice. Use them to inspect assumptions, compare models, and find weaknesses in the input data.
Technical deep dives
Follow the project beyond the landing page. Each publication focuses on one architectural boundary, implementation decision, or operational lesson.
Vault publications
- Your first valuation run11 min read · #python #valuation #tutorial
Install the equity valuation engine, run one ticker, and learn to read suitability checks, scenarios, skipped models, and JSON.
- How to choose a valuation model11 min read · #valuation #finance #models
Match DCF, multiples, dividends, assets, and reverse DCF to the financial shape of a business before trusting any estimate.
- From market data to usable metrics10 min read · #python #valuation #data-quality
Follow provider data into StockMetrics, learn what missing-value diagnostics preserve, and see which gaps can still mislead a model.
- Assumptions, scenarios, and honest uncertainty10 min read · #valuation #finance #scenarios
Learn how historical signals, configured limits, and scenario policy become Bear, Base, and Bull paths without pretending to predict.
- DCF and reverse DCF, step by step11 min read · #valuation #dcf #finance
Build a discounted cash-flow valuation, inspect its sensitivity, and reverse the calculation to understand market-implied expectations.
- Six other ways to value a company11 min read · #valuation #multiples #finance
Compare P/E, EV/EBITDA, P/S, ROE, DDM, and NAV through independent examples, and learn why every model needs its own guardrails.
- How to read a valuation result11 min read · #valuation #json #analysis
Interpret scenario rows, skipped models, the equal-weight composite, model dispersion, and JSON without turning output into a target price.
- Extending the engine without breaking its contract10 min read · #python #architecture #valuation
Trace the architecture behind data fields, diagnostics, suitability, valuation managers, CLI output, JSON, and tests before adding features.