trade-analytics-mcp

Give your AI eyes and a ruler for your trades. An open-source MCP server that lets your LLM/agent pull the real price action around every fill in an order-history export, see each trade on a candlestick chart, and measure it — heat taken, R-multiple, whether it went green before red, how much of the move it captured — so it can tell you whether a loss was bad luck or bad location.

You bring your own market-data key; the data stays on your machine.

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Why — what it actually lets your AI do

An order-history export (Tradovate, your broker's CSV, …) is just timestamps, prices, sizes, sides. From that alone an AI can spot behaviour — overtrading, revenge entries, size creep, bad time-of-day — but it is blind to trade-location quality: was the entry into structure or a chase? how much adverse excursion did you sit through? did you cut a winner early? was the stop sane for the volatility?

This MCP closes that gap — in two tiers.

🆓 Free, on your machine

resolve_symbol + get_price_context run 100% locally under your own Databento key — no account, no fee, no data leaving the machine. Your agent can:

• turn "MNQ" / "micro nasdaq" into the exact contract, point value, and front-month; and
• pull the real OHLCV around each fill (auto-windowed) with your entries/exits/stops as JSON,

so it reasons over the actual price path — where the entry sat, the shape of the move, proximity to the day's high/low — instead of guessing. That alone turns "I think I chased" into a grounded read.

💳 With a trade-analytics API key (paid)

render and analyze send only the numeric slice of that same local data to the hosted API and add the two things that are tedious or impossible to do well from raw JSON:

• render — an annotated candlestick chart image (entries ▲ / exits ▼, SL/TP zones, reference levels), returned and saved to a file so your AI can drop it into a journal.
• analyze — the precise per-trade metrics that separate good-trade-bad-outcome from bad-location: MAE / MFE (heat vs best excursion), R-multiple, went-green-first, capture efficiency, plus USD P&L, time-in-trade, win rate, and session roll-ups.

So — with the paid tools — instead of "you lost on trade #3," your AI can say:

"Trade #3 (short at 20,110) went +0.8R in your favour within three minutes, then reversed through your entry to −18 pts before you exited at −15 — MFE 12, MAE 18, capture −1.2. Location was fine (into the prior-day high); management wasn't," — and hand you the annotated chart.

A typical review

"Review my MNQ trades from last week." → your agent reconstructs the round-trips, calls resolve_symbol (MNQ → the right contract), analyze (per-trade MAE/MFE/R/capture + a session summary), and render on the notable trades (charts saved to your vault), then writes the review with the numbers and the pictures.

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Tools

Tool	What it does	Cost
resolve_symbol	"MNQ" / "micro nasdaq" → precise contract, point value, front-month	Free, local
get_price_context	OHLCV + your entries/exits/SL as JSON for a window auto-sized around the trade	Free, local
render	An annotated candlestick chart image, returned and saved to a file (PNG or SVG)	Paid
analyze	Per-trade MAE/MFE, R-multiple, went-green-first, capture efficiency + summary	Paid

resolve_symbol and get_price_context run 100% on your machine under your own data key — no account, no fee. render and analyze forward only the numeric slice of your own data to the hosted trade-analytics API and return the result; nothing is retained.

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Setup

1. Get a Databento key (your market data)

Databento is a modern market-data provider — institutional-grade historical and live data through a simple API. This example MCP implementation uses it to fetch the candles around your trades, under your own key, locally.

• No subscription. It's pay-as-you-go, and new accounts get a free usage credit that comfortably covers normal trade review — historical 1-minute OHLCV is cheap and is often effectively free within that credit.
• Sign up at databento.com → open the portal → create an API key → that's your DATABENTO_API_KEY.

Your key and your data stay on your machine; only the numeric slices you render/analyze leave it.

2. (For charts + analysis) Get a trade-analytics API key

render and analyze need a key from the trade-analytics dashboard. There's a free tier to try it; paid tiers raise the monthly quota. The free local tools work without one.

3. Add the MCP to your client

Claude Code (or any terminal harness):

claude mcp add trade-analytics \
  -e DATABENTO_API_KEY=db-your-key \
  -e TRADE_ANALYTICS_API_KEY=tc_live_your-key \
  -- npx -y trade-analytics-mcp

Drop the TRADE_ANALYTICS_API_KEY line to run just the free local tools.

Claude Desktop / other MCP clients — add to the config:

{
  "mcpServers": {
    "trade-analytics": {
      "command": "npx",
      "args": ["-y", "trade-analytics-mcp"],
      "env": {
        "DATABENTO_API_KEY": "db-your-key",
        "TRADE_ANALYTICS_API_KEY": "tc_live_your-key"
      }
    }
  }
}

Configuration

Resolved with precedence CLI flags > env > defaults:

Setting	Env	Flag	Notes
Databento key	DATABENTO_API_KEY	--databento-key	Required. Your own; data stays local.
API key	TRADE_ANALYTICS_API_KEY	--api-key	Only for render / analyze.
API URL	TRADE_ANALYTICS_API_URL	--api-url	Defaults to the hosted API.
Output dir	TRADE_ANALYTICS_OUTPUT_DIR	--output-dir	Where render saves images.

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Built on the trade-analytics API

This whole server is a thin client — it fetches data locally and calls the trade-analytics API for the rendering and analysis. That API is a general, data-agnostic service for turning OHLC data into chart images and trade metrics: send it candles (from any source), get back a chart or a trade analysis. This MCP is just one example of what you can build on it — a dashboard, a trading bot, a Discord/Slack integration, a backtest reporter, or your own journaling tool would all sit on the same endpoints.

Privacy

• Your Databento key and your market data live on your machine.
• The free tools make no network calls to us at all.
• render / analyze send only the numeric candle slice needed for that one call; the API is stateless and retains nothing.

Develop

npm install
npm run typecheck && npm test && npm run build
npm run dev   # run the stdio server locally

MIT licensed. Issues and PRs welcome.