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patternfetch — Market-State Briefs for AI Trading Agents

patternfetch — Market-State Briefs for AI Trading Agents

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patternfetch.comFinance0Updated 15w ago

About patternfetch — Market-State Briefs for AI Trading Agents

patternfetch provides compact market-state reports for AI trading agents covering US stocks, ETFs and crypto spot. It combines technical indicators and chart patterns with historical hit rates and baseline comparisons, via REST and MCP.

Key features

  1. 01Compact market briefs
  2. 02Patterns with hit rates and lift
  3. 03Multi-timeframe market alignment
  4. 04Change-only updates with delta
  5. 05Historical analog outcome distributions
  6. 06Market scans and capability details

Use cases

  1. 01Review a ticker’s technical picture
  2. 02Compare signals across timeframes
  3. 03Track changes during market polling
  4. 04Screen assets by regime or pattern

MCP support

Provides six market-data tools through a remote Streamable HTTP MCP server, with a local stdio bridge also available. Tool discovery is available without a key; tool calls require authentication.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://patternfetch.com/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

6
ParameterTypeRequiredDescription
tickerstringYesSymbol to analyse. US stock or ETF like "AAPL" / "SPY", or a crypto spot pair like "BTC/USDT". Always spell crypto as a pair: a bare "BTC" or "ETH" is a real US-listed ETF, NOT the coin, and it will return that ETF's prices without failing. Affected responses carry a "notice" field.
timeframestringYesBar size. One of 1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w. Coverage differs per asset class — see the capabilities tool.
limitintegerNoHow many recent bars to analyse: integer >= 20, capped at 1000, default 200. Fewer bars = fewer tokens.
marketenumNoOptional asset-class override. Omit and it is inferred from the ticker: a "BASE/QUOTE" pair is crypto, a plain symbol is a US stock/ETF.crypto | stock

Details

From the project README

patternfetch

patternfetch is a market-data API for AI agents covering US stocks, ETFs and crypto spot. One call with a ticker and a timeframe returns a token-compact market-state report: compact candles, detected chart and candlestick patterns, support and resistance levels, market regime, and interpreted indicators (RSI, EMA). Every detected pattern carries its backtested historical hit rate and its lift against the pattern-free baseline of the same market, so an agent can tell a pattern that carries information from one that does not. Six tools — brief, multi, delta, analogs, scan, capabilities — reachable over REST and MCP, with one-click OAuth, credit billing via Stripe or x402 USDC on Base, a keyless demo endpoint, and $3 starter credit on signup. Impersonal market data, not investment advice.

npm patternfetch MCP server license

Why it's smaller: for BTC/USDT 4h (120 candles), a raw OHLCV dump is ~3,260 tokens of just numbers the model still has to analyze; patternfetch's interpreted analysis is ~1,323 tokens, already decided. Reproduce it (no account needed).

  • Coverage: US stocks and ETFs (split- and dividend-adjusted, delayed/EOD, via Yahoo), crypto spot (realtime, via Binance).
  • Timeframes: 1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w.
  • Access: REST at patternfetch.com/v1/*, MCP at patternfetch.com/mcp (Streamable HTTP), plus a local stdio bridge (patternfetch-mcp).

Why base rates and lift

A detector that only reports double_top, confidence 0.92 tells an agent nothing about whether that pattern has ever meant anything. patternfetch attaches an evidence block to each detected pattern:

{
  "name": "double_top",
  "confidence": 0.92,
  "evidence": {
    "scope": "US stocks & ETFs",
    "tf": "1d",
    "band": "0.75-1.00",
    "horizon": 10,
    "n": 7508,
    "hitRate": 0.431,
    "ci95": 0.011,
    "lift": {
      "baseline": 0.419979,
      "baselineN": 46038,
      "lift": 0.011021,
      "ci95": 0.012075,
      "informative": false,
      "reading": "indistinguishable-from-baseline"
    }
  }
}

hitRate is the realizable gross directional base rate: the fraction of non-overlapping historical occurrences of that pattern, in that timeframe and confidence band, whose close-to-close return over the next horizon bars went the expected direction. The forward window starts at detection, so there is no lookahead. No stops, fees or slippage are modelled.

lift compares that hit rate against the baseline of the same market with no pattern present. Many patterns come back indistinguishable-from-baseline — that is the honest result, and reporting it is the point. An agent can filter on informative instead of trusting a geometric confidence score.

Calibration. Across 105 audited categories, 3 fall outside their confidence interval — fewer than the ~5.3 that chance alone predicts across 105 comparisons. For US stocks it is 0 of 60. Method and full tables: patternfetch.com/pattern-base-rates-study. The measurement is reproducible with the open-source honest-signals tool.

Quickstart

No key required — the demo endpoint is public:

curl -X POST https://patternfetch.com/v1/demo \
  -H 'content-type: application/json' \
  -d '{"ticker":"AAPL","timeframe":"1d"}'

With a key (self-serve, $3 starter credit):

curl -X POST https://patternfetch.com/v1/keys -d '{"email":"[email protected]"}'

curl -X POST https://patternfetch.com/v1/brief \
  -H 'authorization: Bearer pf_...' \
  -H 'content-type: application/json' \
  -d '{"ticker":"BTC/USDT","timeframe":"4h"}'

JavaScript client:

npm install patternfetch
import { Patternfetch } from 'patternfetch';

const { key } = await new Patternfetch().createKey('[email protected]');
const pf = new Patternfetch({ apiKey: key });

const brief = await pf.brief({ ticker: 'AAPL', timeframe: '1d' });

console.log(brief.analysis.nl);
// "AAPL: uptrend (strong), +0.14% last 1d, RSI 71.66 (overbought),
//  bearish_engulfing (conf 1, hist 41% over 10b, lift -0.7pp vs 42% base (within noise))."

for (const p of brief.analysis.patterns) {
  if (p.evidence?.lift.informative) console.log(p.name, p.evidence.hitRate, p.evidence.lift.lift);
}

Tools

Six tools, the same set over MCP (patternfetch_*) and REST (POST /v1/*).

ToolWhat it returnsWhen an agent calls it
briefMarket-state report for one ticker + timeframe: compact candles, patterns with base rate and lift, support/resistance, regime, RSI/EMA, one-line summary.The default. It needs the current technical picture of one market without dumping raw OHLCV into context.
multiOne brief per timeframe (default 1h, 4h, 1d) plus a cross-timeframe alignment read that spells out agreement or divergence, e.g. 1h up / 4h up / 1d down.It wants to know whether a setup is confirmed or contradicted across horizons, without three separate brief calls.
deltaOnly what changed since the last brief for that ticker + timeframe — trend flips, new patterns, RSI-state changes. Returns changed: false when nothing material moved.It polls the same market repeatedly. Call brief once, then delta on every later poll to keep token cost near zero.
analogsHistorical windows whose shape resembles current price action, with the full distribution of what followed: win rate, median, mean, min, max and n over a fixed forward horizon.It wants the historical outcome spread for a setup rather than a point estimate. Not a prediction, not a strategy backtest.
scanScreener over a curated universe of liquid US large-caps, core and sector ETFs and major crypto pairs. Filter by asset class, regime, pattern and minimum base rate; rows return ranked by base rate with 95% CI. Precomputed daily.It needs to find candidates across the market rather than analyse a ticker it already named. Feed the shortlist into brief.
capabilitiesSupported assets, timeframes, endpoints, limits and pricing. No input.First, before relying on any assumption about coverage.
Client methods
MethodEndpoint
brief({ticker, timeframe, limit?, fields?, market?})POST /v1/brief
multi({ticker, timeframes?, limit?, market?})POST /v1/multi
delta({ticker, timeframe, limit?})POST /v1/delta
analogs({ticker, timeframe, window?, horizon?})POST /v1/analogs
scan({assetClass?, regime?, pattern?, tf?, minBaseRate?, limit?})POST /v1/scan
candles({ticker, timeframe})POST /v1/candles
platforms()GET /v1/platforms
createKey(email)POST /v1/keys

MCP

patternfetch is a remote MCP server (Streamable HTTP) at https://patternfetch.com/mcp. Tools: patternfetch_brief, patternfetch_multi, patternfetch_delta, patternfetch_analogs, patternfetch_scan, patternfetch_capabilities. Discovery (initialize, tools/list) is free — no key. Only tools/call needs auth.

One-click OAuth (nothing to paste) — in Claude Code, Claude Desktop, Cursor or Smithery, add the URL and authorize once; a free-tier key is minted for you:

claude mcp add --transport http patternfetch https://patternfetch.com/mcp

In claude.ai: Customize → Connectors → Add custom connector → https://patternfetch.com/mcp → Authorize.

Or with a Bearer key — add to your MCP config:

{
  "mcpServers": {
    "patternfetch": {
      "url": "https://patternfetch.com/mcp",
      "headers": { "Authorization": "Bearer pf_..." }
    }
  }
}

Get a free key (small starter credit) at https://patternfetch.com/v1/keys.

Local stdio bridge

Prefer a local stdio server (Claude Desktop, sandboxes, no inbound HTTP)? This package ships patternfetch-mcp, a zero-dependency stdio↔HTTP bridge that exposes the same tools and forwards calls to patternfetch.com:

{
  "mcpServers": {
    "patternfetch": {
      "command": "npx",
      "args": ["-y", "patternfetch-mcp"],
      "env": { "PATTERNFETCH_API_KEY": "pf_..." }
    }
  }
}

tools/list works with no key and falls back to the embedded snapshot (mcp-tools.json) when the remote is unreachable, so introspection always succeeds. Tool calls use PATTERNFETCH_API_KEY, OAuth or x402. Override the endpoint with PATTERNFETCH_MCP_URL.

Refresh the snapshot from the live server:

curl -s -X POST https://patternfetch.com/mcp \
  -H 'content-type: application/json' \
  -H 'accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Pricing

$3 starter credit on signup, at least $0.50 of it usable immediately without a card. After that, pay per call from credit, topped up via Stripe or x402 USDC on Base. Studio plan: $19/month including $25 of usage.

CallPrice
/v1/brief$0.010
/v1/multi$0.025
/v1/delta$0.008 ($0.001 when nothing changed)
/v1/candles$0.005
/v1/analogs$0.050
/v1/scan$0.020

Live figures: GET /v1/platforms.

Legal

patternfetch provides impersonal market data and algorithmic signals for informational purposes only. NOT investment, financial, legal or tax advice, and not a recommendation to buy, sell or hold any security or crypto-asset. Outputs are not personalized to you. Base rates are gross directional frequencies without stops, fees or slippage; past performance and historical analogs do not guarantee future results. Markets are volatile — you may lose all capital. Do your own research. See patternfetch.com/disclaimer, /methodology and /terms.

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