Explore the numbers.
Inspect the method.
A focused public demonstration of an investment-research platform. Compare real reported financials for 12 companies, experiment with illustrative quantitative models, and see how the data is validated.
Real annual SEC filings power the company research. Portfolio prices, returns, DCF inputs, and peer-model inputs are simulated.
Start here
Three short stops give reviewers a useful picture of the work.
Reported annual financials · SEC EDGAR
12 companies · up to 3 fiscal yearsInteractive portfolio example
Simulated holdings and pricesSample allocation
Explore virtual assets
Illustrative prices · not live quotesModel demonstrations
Illustrative calculations · simulated financial inputsSEC Financials
Real annual SEC financial statements for a curated set of 12 companies. Historical reporting periods and an illustrative margin scenario; this limited public dataset does not include live quotes or personal holdings.
Company analysis
SEC annual filingsAnnual reported trends
Peer snapshot
Fiscal year-end dates may differSource: SEC EDGAR Company Facts.
Portfolio simulator
A sample portfolio with simulated holdings and prices. Make hypothetical transactions in this browser session; no orders or real-time market quotes.
Positions
Virtual transaction
ETF holdings overlap
Rebalancing plan
Quant
Demonstrate portfolio optimization and modeled downside using simulated returns, not historical market observations. For real reported financials, explore SEC Financials.
Analysis settings
Valuation
Interactive discounted cash flow methodology using illustrative company assumptions. These values are not actual company valuations; SEC Financials contains the real reported figures.
Company DCF
Company peers
Explore nearest-neighbor methodology using simulated company fundamentals. For comparisons based on actual filings, visit SEC Financials.
Methods & engineering
How QuantForge separates reported financial evidence, model assumptions, and authenticated personal data. This page describes the implemented public demonstration and its limitations.
Data lineage
Historical SEC snapshotThe public browser does not request raw filings or private market quotes. A locally generated and reviewed snapshot is embedded when the site is built. Figures do not auto-refresh.
What the figures mean
Reported vs. derived vs. hypothetical| Output | How it is calculated | Interpretation |
|---|---|---|
| Revenue, operating income | Eligible annual US-GAAP facts in 10-K / 10-K/A | Historical reported data, not a stock quote |
| Operating margin | Operating income / revenue | Derived only where both fields exist |
| Revenue growth | (Current annual revenue / prior annual revenue) − 1 | Reporting periods follow issuer fiscal years |
| FCF proxy | Operating cash flow − reported cash PP&E purchases | Simple disclosure-derived proxy; not unlevered FCFF |
| Margin stress | Reported annual revenue × hypothetical adjusted margin | Scenario sensitivity; not an earnings forecast |
Missing values remain unavailable rather than zero-filled. Company fiscal year-ends are not aligned; broad sector groupings are editorial and are not normalized industry classifications.
Model demonstrations and boundaries
Interactive · illustrativePortfolio simulator
Virtual positions, example prices, rebalancing weights, and estimated fees. Everything stays in the browser session and does not place orders.
Quantitative modeling
Simulated returns, covariance, constrained allocation, and Monte Carlo downside. Results are not calibrated forecasts of market risk.
DCF sensitivity
Illustrative cash-flow assumptions with discounting and terminal-value sensitivity. These are not live company valuations.
Company similarity
Standardized synthetic fundamentals demonstrate nearest-neighbor comparisons. Use SEC Financials for actual company comparisons.
Engineering practices
Implementation evidenceData engineering
Named SEC issuer identifiers, annual-period filtering, filing cutoffs, input validation, recorded sources and accession details.
Statistical modeling
Constrained optimization, covariance-based portfolio analysis, downside simulation, DCF, and nearest-neighbor feature standardization.
Application engineering
Python backend, static hosted frontend, automated Python and JavaScript checks, continuous integration, and a restrictive script Content Security Policy.
Privacy boundary
Public research data is separate from the owner-only workspace. Private holdings and Alpaca access are protected by authenticated endpoints and authenticator MFA.
The private workspace provides real market observations and virtual trading for the owner only. Public visitors cannot use that API as an open data source.
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Positions
Market observation and virtual trade
IEX stock/ETF trades or Alpaca crypto venue observations. Transactions require a fresh timestamp. No brokerage order is placed.
Research notes
Watchlist: None
Quant / valuation / ML
The other tabs currently run the public fictional demonstration models. They do not use your private market observations. Full server-backed analysis is a separate integration milestone.