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Fundamentalio
FreeOpen SourceSelf-Hosted
Most stock screening tools force you to choose between a data dashboard that tells you nothing and a financial model that takes days to build — neither applies a coherent investment philosophy to the numbers for you. fundamentalio runs Peter Lynch's framework against a ticker and hands you a structured analysis without requiring you to manually cross-reference PEG ratios, debt load, and business-model clarity on your own.
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Get This Tool
License: MIT Any use incl. commercial
Local-run terms: MIT license permits running, modifying, and commercial use of the source code.
Screenshots 4
Pricing
- Model
- Free
Summary
The tool pulls fundamentals via yfinance and sends them through OpenAI in either a quick-scan or deep-research mode, so you can screen a watchlist fast or stress-test a single position with more context. Because every analysis is a one-shot OpenAI call, there is no memory between runs — each report starts cold. The Lynch framing is the differentiator: the prompt logic is built around his specific criteria, not generic financial ratios, which means output reads like a philosophy-aligned verdict rather than a data dump. Self-hosted and MIT-licensed, so your API keys and tickers stay off third-party servers. The ceiling is clear: if your process needs portfolio-level comparison, backtesting, or screening across hundreds of tickers in a session, the architecture does not support it.
Bottom line: Pick this when you want a Lynch-style read on a single company in minutes and you are comfortable running a local Python project; abandon it when your workflow needs batch screening, persistent history, or integration with a brokerage.
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Pros
- Lynch-specific prompt framing, so output maps directly to his investment criteria — PEG sensitivity, business-model clarity, debt tolerance — rather than producing a generic summary you still have to interpret through a philosophy yourself.
- Two-mode depth control (quick vs. deep), so you can triage a watchlist without paying OpenAI token costs for a full deep analysis on every name, then spend those tokens only on positions you are actually evaluating.
- Self-hosted with local credential storage via .env, so your ticker queries and API keys never leave your machine — relevant if you treat your watchlist as competitively sensitive.
- MIT-licensed and fully open source, which means you can read, modify, and extend the prompt logic if Lynch's framework is a starting point rather than a final word for your process.
- yfinance integration for data retrieval, so you are not manually exporting spreadsheets or paying for a financial data subscription just to feed the analysis.
Cons
- No batch or multi-ticker session support: screening a watchlist of twenty stocks means running the tool twenty separate times with no shared output layer, and at that volume the manual process defeats the time savings the tool is meant to provide — teams with screening-volume needs switch to a dedicated screener with exportable filters.
- Single-shot OpenAI calls with no memory between runs mean every report starts from zero, so you cannot ask follow-up questions, compare two reports programmatically, or build on a prior analysis — any iterative research workflow requires you to copy-paste output manually or build a wrapper yourself.
- No hosted interface, no API surface, and no frontend: setup requires Python, dependency installation, and .env configuration, which puts the tool outside reach for investors who are not comfortable with a terminal — the README describes macOS and Windows installation steps, but there is no fallback for non-technical users.
- Output quality is bounded by yfinance data availability and OpenAI's knowledge, meaning thinly traded stocks, recent earnings surprises not yet reflected in yfinance, or companies with unusual capital structures produce analysis the model cannot reliably handle — the README carries a disclaimer, and teams doing due diligence on small-caps will hit this wall before large-cap users do.
About
- Platforms
- Python
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- June 12, 2026
Best For
Who it's for
- Individual investors
- Fundamental analysis users
- Lynch philosophy followers
What it does well
- Fundamental stock screening
- Peter Lynch methodology application
- Investment report generation
Integrations
Frequently Asked Questions
- Is Fundamentalio free?
- Fundamentalio offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is Fundamentalio open source?
- Yes. Fundamentalio is open source.
- Can I self-host Fundamentalio?
- Yes. Fundamentalio supports self-hosting on your own infrastructure.
- What platforms does Fundamentalio support?
- Fundamentalio is available on: Python.
fundamentalio is an open-source Python project that applies Peter Lynch’s investment criteria to individual stocks by combining yfinance’s fundamental data retrieval with OpenAI’s language models. You point it at a ticker, choose between quick or deep research mode, and receive a generated report framed around Lynch’s core tests: do you understand the business, is growth priced reasonably, is the balance sheet survivable, and does the company have a defensible position in its category. The entire workflow runs locally — no hosted service, no dashboard — using credentials you supply in a local .env file.
The defining design choice is the two-mode structure. Quick Research is built for triage: it processes the most signal-dense fundamentals and returns a shorter verdict faster, suited to running through a watchlist. Deep Research pulls broader context and produces a longer output that walks through Lynch’s framework in more detail, suited to a stock you are already considering seriously. Both modes use single-shot OpenAI calls, meaning the model does not loop, self-correct, or chain reasoning steps — the quality of output is bounded by what fits in one prompt and one response.
The tool fits an individual investor who already thinks in Lynch’s terms and wants analytical scaffolding without building it from scratch. It breaks at the boundaries the architecture draws: no batch mode means screening fifty tickers requires running the tool fifty times with no aggregation layer, and no persistent storage means you cannot query across past reports. Teams that need portfolio-level views, screener-style filtering, or audit trails for investment decisions will exhaust what this repo offers quickly and are better served by a dedicated financial data platform or a custom pipeline built on top of a financial data API.
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