Skip to main content
Glama
CaioAllgayer

TradeLab-MCP

by CaioAllgayer

TradeLab-MCP

MCP server for quantitative research. The AI defines the strategy. MetaTrader 5 Strategy Tester runs the backtest.

IA (Codex / GPT / Grok / Gemini / Claude)
        │ MCP
        ▼
Trading Research MCP
        │
        ▼
terminal64.exe  →  Strategy Tester oficial  →  runs/<run_id>/

Fork of PHUICMT/mcp-mt5 (MIT). It does not reimplement the tester in Python.

Principle

MT5 is the authority for orders, fills, ticks, spread, SL/TP, margin, commissions, and P&L.

Python only organizes experiments (run_id, manifest, hashes, parser, derived statistics).

Each backtest generates a unique identifier. Nothing is retrieved by the “most recent file”.

runs/20260817_073412_a8f231/
  tester.ini
  strategy.mq5
  strategy.ex5
  report.htm
  tester.log
  trades.csv
  metrics.json
  manifest.json

Related MCP server: OpenFinClaw CLI

MCP Tools (V1)

Tool

Function

health

MT5, MetaEditor, terminal, data dir, Experts, Tester, build

compile

Compiles .mq5 and returns hashes

run_backtest

Official tester → run_id + metrics + artifacts

run_batch

Same strategy, multiple assets, sequential

get_run

Retrieves an experiment by run_id

get_trades

Normalized trades from that run_id

compare_runs

Metrics diff between runs

walk_forward

IS/OOS windows, each an official backtest

smoke_test

Compile + short tester (configurable symbol/period/model/date)

Installation

Windows + MetaTrader 5 + Python 3.10+.

cd C:\Users\caioa\TradeLab-MCP
python -m pip install -e ".[dev]"

MCP Client:

{
  "mcpServers": {
    "tradelab": {
      "command": "tradelab-mcp",
      "env": {
        "MT5_INSTALL": "C:\\Program Files\\MetaTrader 5",
        "TRADE_LAB_ROOT": "C:\\Users\\caioa\\TradeLab-MCP\\research"
      }
    }
  }
}

Variables: MT5_INSTALL, MT5_DATA, MT5_TERMINAL_HASH, TRADE_LAB_ROOT.

Example

Compile este EA e faça um backtest de PETR4 D1,
entre 2015 e 2025, usando real ticks.

The agent calls run_backtest(strategy="experts/RSI2.mq5", symbol="PETR4", timeframe="D1", from_date="2015.01.01", to_date="2025.08.01", model=4) and receives:

{
  "run_id": "20260817_073412_a8f231",
  "status": "completed",
  "symbol": "PETR4",
  "period": "D1",
  "model": "real_ticks",
  "metrics": {
    "total_trades": 134,
    "net_profit": 18342.21,
    "profit_factor": 1.48
  }
}

get_run("20260817_073412_a8f231") returns exactly that experiment.

Where EAs live in MT5

Every automated test EA is published to:

<MQL5>\Experts\TradeLab MCP\

On this PC this resolves to:

C:\Users\caioa\AppData\Roaming\MetaQuotes\Terminal\D0E8209F77C8CF37AD8BF550E51FF075\MQL5\Experts\TradeLab MCP

In the Strategy Tester the expert appears as TradeLab MCP\RSI2. The folder is always relative to the active terminal (layout.experts_dir), not a fixed hash.

The source in git remains in experts/. The MCP copies source + .ex5 + local includes to the terminal folder.

The lab's base EA (CTrade, ArraySetAsSeries, one position) has not been standardized yet — it will come later, without rushing.

Acceptance EA

experts/RSI2.mq5

  • RSI(2)

  • buys if RSI < 10

  • exits if RSI > 70

  • one position

  • no optimization

Acceptance test #1 is: the same EA / asset / dates / inputs / capital / model in manual Strategy Tester and via MCP must produce the same metrics and the same trades.

Tests

pytest

Integration tests (Windows + real MT5):

$env:TRADE_LAB_INTEGRATION = "1"
$env:TRADE_LAB_SYMBOL = "EURUSD"
pytest tests/test_integration_mt5.py -v

Priority

reproducibility > reliability > simplicity > speed > features

V1 is sequential (lock mt5.lock on the data directory). No parallel Python backtester. No .opt parser as official source.

A
license - permissive license
-
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • A
    license
    -
    quality
    D
    maintenance
    MCP server that provides AI agents with financial tools including real-time quotes, backtesting, technical analysis, and multi-exchange data via a simple CLI interface.
    1
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    An MCP server exposing a registry of paper-backed quantitative trading methods plus a deterministic, no-LLM decision helper for reproducible trading research.
    13
    MIT
  • A
    license
    A
    quality
    D
    maintenance
    MCP server that exposes the Backtest360 backtesting engine API as tools, enabling AI agents to conversationally discover indicators, build and validate strategies, run backtests, and read results.
    14
    MIT

View all related MCP servers

Related MCP Connectors

  • MCP server exposing the Backtest360 engine API as tools for AI agents.

  • MCP server for Gainium — manage trading bots, deals, and balances via AI assistants

  • Build, backtest, and deploy quantitative trading strategies from your AI agent.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/CaioAllgayer/TradeLab-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server