forex-predict-mcp

A FastMCP server that exposes XGBoost directional predictions for EURUSD=X and GBPUSD=X as MCP tools. Packaged as a Docker stdio image — no env vars required.

Designed to run alongside the oanda-mcp-server as a tool for a Claude trading agent.

Tools

get_market_features(ticker)

Fetches ~1 year of daily OHLCV data from Yahoo Finance plus cross-asset data (DXY, VIX, EURUSD), computes the full feature vector, and returns it for inspection.

Parameters:

• ticker — "EURUSD=X" or "GBPUSD=X"

Response:

{
  "ticker": "EURUSD=X",
  "as_of": "2026-06-19",
  "features": {
    "return_5d": 0.0124,
    "return_10d": 0.0231,
    "return_20d": -0.0052,
    "rsi_14": 58.3,
    "atr_14": 0.00234,
    "macd_hist": 0.00012,
    "sma50_ratio": 1.003,
    "dxy_return_5d": -0.0081,
    "dxy_return_20d": -0.0152,
    "vix_return_5d": 0.122,
    "vix_vs_sma20": -0.048
  }
}

GBPUSD=X additionally includes eurusd_return_5d and eurusd_return_20d.

get_forex_signal(ticker, features)

Runs the baked-in XGBoost model against pre-computed features and returns a directional signal.

Parameters:

• ticker — "EURUSD=X" or "GBPUSD=X"

• features — the features dict from get_market_features()

Response:

{
  "ticker": "EURUSD=X",
  "as_of": "2026-06-19",
  "signal": "UP",
  "prob_up": 0.773,
  "confidence": 0.773
}

Related MCP server: curistat-mcp

Typical agent workflow

1. get_market_features("EURUSD=X")  →  inspect RSI, DXY, VIX values
2. get_forex_signal("EURUSD=X", features)  →  UP 77.3%
3. cross-reference with Oanda live price and open positions
4. place or skip order

.mcp.json configuration

Add the following entry to your .mcp.json (typically at the project root or ~/.claude/.mcp.json):

{
  "mcpServers": {
    "forex-predict-mcp": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "--rm", "-i", "sleepingtalent/forex-predict-mcp:latest"]
    }
  }
}

If you already have other servers (e.g. oanda-mcp-server), add forex-predict-mcp alongside them:

{
  "mcpServers": {
    "oanda-mcp-server": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "--rm", "-i", "-e", "OANDA_API_KEY", "-e", "OANDA_ACCOUNT_ID", "-e", "OANDA_ENVIRONMENT", "sleepingtalent/oanda-mcp-server:latest"]
    },
    "forex-predict-mcp": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "--rm", "-i", "sleepingtalent/forex-predict-mcp:latest"]
    }
  }
}

Models

XGBoost binary classifiers trained on 5 years of daily data:

Models are baked into the Docker image at build time. To update: retrain in weights-biases-example, run uv run task export_models, copy the JSON files into src/forex_predict_mcp/models/, and push — CI publishes a new tagged image automatically.