Scan the whole market. Get back the few real signals.

Lookup vs. discovery

Most market-data APIs — patternfetch's own /v1/brief included — are lookup: you already know the ticker, and you ask for its current state. That's the right tool once you have a candidate. But an agent screening the market has the opposite problem: it doesn't know which tickers to look at yet. Calling /v1/brief on hundreds of symbols to find the handful that matter is slow, token-heavy and expensive.

That's the whole pitch: the scanner turns lookup into discovery, so your model finds the few real signals instead of being handed one ticker at a time — and it ranks them by evidence, not by a confident-sounding label.

How it works

1. A curated universe. An evidence-backed set of liquid US large-caps, core & sector ETFs, and major crypto pairs is scanned so every result is a name with enough history to measure honestly.
2. Precomputed, not live. Scanning the whole universe live (a fetch + full analysis per ticker) would be slow and hammer upstream sources. Instead the universe is scanned offline on a schedule and each ticker's current-signal row is stored, so a scan is served from memory — fast and cheap.
3. Filter. Your optional filters narrow the universe by asset class, regime, pattern, timeframe and a minimum base rate.
4. Rank by the honest base rate. Survivors are ordered by the top pattern's backtested directional base rate (with its 95% CI and sample size) — the same evidence the brief attaches to a pattern — then by geometric confidence, then timeframe, then symbol.
5. Act on the shortlist. Take the ranked rows and call /v1/brief on the top names for the full picture before your agent does anything.

The filters

Every field is optional; an absent field puts no constraint on that axis. A filter-less POST /v1/scan returns the top of the universe, ranked.

Example — request & response

Ask for US stocks and crypto in an uptrend with at least a 55% base rate, top 20:

curl -X POST https://patternfetch.com/v1/scan \
  -H "Authorization: Bearer pf_…" \
  -H "Content-Type: application/json" \
  -d '{ "assetClass": "all",
        "regime": "up",
        "minBaseRate": 0.55,
        "limit": 20 }'

{
  "asOf": "2026-07-05",
  "tfs": ["1d"],
  "universe": 137,
  "count": 2,
  "results": [
    { "sym":"AAPL","tf":"1d","assetClass":"stock",
      "regime":"up","pattern":"double_bottom",
      "baseRate":0.58,"ci95":0.03,"n":300,
      "scope":"US stocks & ETFs","confidence":0.72 },
    { "sym":"BTC/USDT","tf":"1d","assetClass":"crypto",
      "regime":"up","pattern":"bullish_engulfing",
      "baseRate":0.56,"ci95":0.04,"n":180,
      "scope":"major crypto pairs","confidence":0.69 }
  ],
  "note":"Precomputed current-state scan ranked by
          impersonal historical base rate. Discovery,
          not a prediction or a buy/sell signal.",
  "disclaimer":"… informational only, not advice …"
}

Each row carries sym, tf, assetClass, regime, pattern, baseRate (the ranking key), ci95, n (sample size), scope (the corpus the rate was measured over) and geometric confidence. The envelope's universe and asOf tell you how many tickers were scanned and when. See it live →

How results are ranked

The whole feature is "ranked by the honest backtested base rate", so ordering is deterministic and evidence-first:

Tickers whose top pattern has no sufficiently-powered evidence bucket carry baseRate: null and rank last — the scanner never invents a number it can't back with history.

Over MCP

The screener is also the patternfetch_scan tool on the patternfetch MCP server — same optional filters, same ranked output. Point any MCP client (Claude, Cursor, Smithery, an OpenAI agent over a bridge) at https://patternfetch.com/mcp; tool discovery is free and a call is billed like the REST route.

# one line — OAuth mints a free-tier key, nothing to paste
claude mcp add --transport http patternfetch https://patternfetch.com/mcp

Then your agent can call patternfetch_scan with, e.g., {"assetClass":"crypto","pattern":"double_top","limit":10} and reason over the ranked shortlist. See the MCP docs.

FAQ

Try free — no signup Read the scan docs

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