XFINLAB

Financial Intelligence Infrastructure — APIs, SDKs, and an MCP server for developers and AI agents, plus a consumer research platform built on the same backend.

Real market events, FinBERT sentiment, technical/market-structure analysis, SEC/CFTC/FDIC/USDA/CBOE official data, and Monte Carlo stress testing. Every field is traceable to a real computation or an official data source — nothing fabricated or interpolated.

Get a free API key · API docs · llms.txt · Consumer product

Quick start (API)

pip install "git+https://github.com/lnanology/Xfinlab.git#subdirectory=sdk/python"

from xfinlab_intelligence import XfinlabClient

client = XfinlabClient(api_key="xfl_...")  # free tier, issued instantly
sentiment = client.sentiment("AAPL")
technical = client.technical("AAPL", period="6mo")
fundamentals = client.fundamentals("AAPL")

Or JavaScript/Node:

npm install "github:lnanology/Xfinlab#path:sdk/js"

const { XfinlabClient } = require('xfinlab-intelligence');
const client = new XfinlabClient('xfl_...');
const sentiment = await client.sentiment('AAPL');

19 endpoints total — market events, sentiment, AI debate, technical/market-structure, Monte Carlo stress testing, insider trading, institutional ownership, short interest, SEC XBRL fundamentals, CBOE VIX term structure, FDIC bank health, USDA agriculture, EIA energy, crypto, cross-region market map, and Pro-tier webhooks. Full reference: intelligence-api.html.

Related MCP server: FinClaw

MCP server (for Claude and other AI agents)

Every field this server returns is either a real computation/real official-source value, or null with an explanation — the MCP ecosystem has a lot of servers now, few of them say anything about the actual quality of the data behind the tool calls. This one does, in public: xfinlab.com/trust.html shows every underlying data collector's live/down status in real time.

Already live in production — no setup needed, just point an MCP-compatible client at it:

{
  "mcpServers": {
    "xfinlab": {
      "url": "https://api.xfinlab.com/api/mcp",
      "headers": { "X-API-Key": "xfl_..." }
    }
  }
}

Server source: api/mcp_server.py. Tools: get_market_events, get_sentiment, get_technical_analysis, get_intelligence_feed, get_global_market_map. Same auth and free tier as the REST API. Docs: intelligence-api.html#mcp.

Production usage

Early-stage, honest numbers — pulled from the platform's own admin metrics on 2026-09-22, not curated for effect. Individual emails aren't published here even though they're visible in the admin panel — no reason to expose real people's addresses in a public README.

• 15 API keys issued (14 active, 1 revoked) since the self-serve free tier launched, to roughly 13 distinct outside developers/researchers — not counting the operator's own test key, which shows 0 calls, so none of the volume below is self-generated traffic.

• 669 weighted API calls served all-time, concentrated in a small number of real integrations: the single heaviest account alone accounts for more than half of all-time volume, the second-heaviest for roughly another fifth.

• One signup used a .edu email address — an early, small signal of academic/research interest rather than only casual trials.

• 5 organic accounts on the consumer research site (xfinlab.com), separate from the API-only signups above. Of 7 total registered accounts, 2 are the operator's own account and an internal LINE-bot integration, not external users — excluded from that count.

• No response-time or uptime SLA number is published here — it isn't actually measured yet, and this project holds itself to the same zero-fabrication policy it markets to users, so an unmeasured number doesn't get invented for a case study either. Per-data-source status (not per-request latency) is live on that same page.

Two real bugs this surfaced, fixed the same day each was found:

• SEC Form 4 false positives (Sept 2026). SEC EDGAR's own browse-edgar type filter turned out to behave as a prefix wildcard, not an exact match — type=4 was silently matching unrelated 424B-series filings as "insider trading" activity. Caught by the platform's own data-source health alerting (not a user bug report), confirmed against SEC's live responses — its own pagination link rewrites type=4 to type=4%25, which is what gave the bug away.

• Point-in-time/vintage data store (Sept 2026). A quant researcher's comment on r/quant pointed out that treating a filing's period-end date as "known on that date" ignores real filing lag and later restatements — a classic look-ahead-bias source in backtests. Built a dedicated store (services/point_in_time_store.py) where every fundamentals value carries a real filing-availability timestamp instead of the period it describes, and a restated value is stored as a new immutable row rather than overwriting the original — so "what did we know on date X" and "what's the latest known value" are two different, both-correct queries.

SDKs & examples

Both SDKs are MIT-licensed (sdk/LICENSE) and have zero required dependencies beyond the standard library / native fetch.

Consumer product

The same backend also powers xfinlab.com, a retail investment-research platform:

Local development

python3 mock-server.py

Then open http://localhost:8080. The production backend is a separate FastAPI app (backend/main.py, deployed on Railway as api.xfinlab.com); the static site above deploys separately on Vercel as xfinlab.com.

More docs

• XFINLAB_ARCHITECTURE.md — full system architecture

• PROJECT_ROADMAP.md — roadmap

• PROJECT_STYLE_GUIDE.md — UI style guide

• DATA-LICENSE-MATRIX.md — upstream data source licensing for every paid endpoint

• MCP_MARKETPLACE_SUBMISSION.md — MCP server directory submission copy