APEX — AI‑Powered MT5 EA Optimizer

An AI trader thinking out loud while it tests, fails, and improves a strategy.

Built with Claude Opus 4.7 for the Cerebral Valley × Anthropic — Built with Opus 4.7 hackathon.

APEX is an autonomous optimizer for MetaTrader 5 Expert Advisors. Instead of brute‑forcing parameters with grid search, an LLM reads each backtest result, decides which parameter to change and why, then runs the next backtest — iterating toward profit‑factor / drawdown / Calmar targets you set. Every reasoning step streams live to a dashboard.

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Why this is different

Traditional optimizers	APEX
Brute‑force grid / genetic search	AI reads each result, decides what to change
Black box — see only final winner	Live thinking feed + per‑iteration param diffs
No notion of why a config works	Stores AI analysis next to every run
Stops after N iterations	Stops when quality targets are met (early exit)
One‑shot validation	Out‑of‑sample + sensitivity with live progress

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Demo

6‑frame timelapse of one autonomous run — Phase 1 exploration → Phase 2 AI iteration → Phase 3 validation → verdict. Every backtest, every parameter change, and every line of AI reasoning streams live.

A static high‑res view is at screenshots/dashboard.png. The dashboard shows three live phases — Exploration → Iteration → Validation — with the AI's reasoning streaming on the right, parameter changes per iteration in the centre, and an out‑of‑sample/sensitivity validation panel that updates as MT5 finishes each test.

Other views: setup wizard · settings modal · best‑result modal with evolution path

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How it works

┌──────────────────────────────────────────────────────────────────────────┐
│                       APEX OPTIMIZATION LOOP                             │
│                                                                          │
│   Phase 1: EXPLORATION                                                   │
│     Latin‑Hypercube sample N parameter sets → run in MT5 Strategy Tester │
│     → score with Calmar / PF / MFE / session‑stability / recovery        │
│                                                                          │
│   Phase 2: AI ITERATION (autonomous loop)                                │
│     ┌──► Claude reads full history + targets + parameter schema          │
│     │      ↓                                                             │
│     │    Claude returns: { changes:[{param,value,reason}], confidence }  │
│     │      ↓                                                             │
│     │    Apply changes (clamped to schema bounds), dedupe, run backtest  │
│     │      ↓                                                             │
│     │    Stream `ai_thinking` + `param_changes` events to UI             │
│     │      ↓                                                             │
│     └──── Targets met? → exit early.  Stuck? → random escape.            │
│                                                                          │
│   Phase 3: VALIDATION                                                    │
│     Out‑of‑sample run on unseen dates + ±20% sensitivity probe on the    │
│     top parameter → verdict: RECOMMENDED / RISKY / NOT_RELIABLE          │
│                                                                          │
│   Output: ranked .set file + per‑run report folder + final verdict       │
└──────────────────────────────────────────────────────────────────────────┘

The AI loop lives in optimizer/ai_guided_loop.py; the reasoner contract is in analysis/ai_reasoner.py; event emission to the UI flows through optimizer/pipeline.py via SocketIO.

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Quick start

1. Clone + install

git clone https://github.com/tonnylegacy/MT5_Optimizer.git
cd MT5_Optimizer
python -m venv .venv
.venv\Scripts\activate            # Windows
# source .venv/bin/activate       # macOS/Linux
pip install -r requirements.txt

2. Configure

Copy the example config and fill it in:

cp config.example.yaml config.yaml

Set your Anthropic API key (get one at https://console.anthropic.com/):

# Option A — environment variable (recommended)
setx ANTHROPIC_API_KEY "sk-ant-..."     # Windows
export ANTHROPIC_API_KEY="sk-ant-..."   # macOS/Linux

# Option B — paste into config.yaml under ai.anthropic_api_key

Edit config.yaml to match your local MT5 install paths under mt5: (terminal exe, AppData path, MQL5 Files path).

3. Launch

python app.py

Open http://localhost:5000. Register your EA on the Setup page, set thresholds, hit Start, and watch the AI think.

Demo mode (no MT5 required)

Don't have MT5 installed? Run the offline demo that feeds synthetic backtest results through the same AI loop and dashboard:

python -m demo.run_demo

This is the path to use if you're a hackathon judge — you'll see the full thinking feed, parameter‑change panel, validation phase, and verdict screen without needing a Windows machine with MT5.

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Configuration cheatsheet

Key	What it does
ai.enabled	Master toggle for the AI reasoning layer.
ai.model	claude-opus-4-7 (best), claude-sonnet-4-6 (balanced), claude-haiku-4-5 (fast).
thresholds.min_profit_factor / min_calmar	Quality gates a result must clear.
optimization.max_iterations	Hard cap on AI loop iterations.
mt5.terminal_exe	Full path to terminal64.exe.
periods.train_* / validate_* / oos_*	Train + walk‑forward validation date ranges.

The full schema lives in config.example.yaml with comments.

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Project layout

MT5_Optimizer/
├── app.py                    Flask + SocketIO server (entry point)
├── config.example.yaml       Configuration template
├── analysis/
│   └── ai_reasoner.py        Claude API client (analyze + suggest_next_params)
├── optimizer/
│   ├── pipeline.py           3‑phase pipeline orchestrator
│   ├── ai_guided_loop.py     Autonomous AI iteration loop
│   ├── result_ranker.py      Scoring & ranking of runs
│   └── session_config.py     Per‑run config dataclass
├── ea/
│   └── schema.py             EA parameter schema + clamp/validation
├── mt5/                      MT5 launcher, ini builder, html report parser
├── reports/
│   └── writer.py             Per‑run HTML/CSV/JSON output
├── ui/
│   ├── templates/            dashboard.html, setup.html, reports_index.html
│   └── static/js/dashboard.js  All client‑side logic
└── tests/

See PROJECT_HANDOFF.md for a deeper architectural tour.

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Live events (SocketIO)

The dashboard subscribes to these — useful if you want to plug a different UI on top:

Event	When it fires	Payload (key fields)
phase_start	Each phase begins	phase, total, mode
run_complete	Any backtest finishes	run_id, phase, net_profit, profit_factor, calmar, max_drawdown, score, params
ai_thinking	AI narrates a decision	msg, kind (info/reasoning/decision/success/warning/hypothesis), iteration, phase
ai_iteration_start / ai_iteration_complete	Each AI loop iteration	iteration, analysis, change_records, confidence, goal_status
param_changes	Per‑iteration parameter diff	iteration, changes:[{param, from, to, reason}], confidence
validation_start / validation_run_start / validation_run_complete / validation_done	Phase 3 visibility	kind (oos/sensitivity), metrics, passing
early_termination	Pipeline stops before max_iterations	reason (targets_met/no_profit/budget_exhausted/stuck_escape/user_stop), message, details
optimization_complete	Run finished	verdict, best_run_id, set_file_url, full metrics

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Contributing

Bug reports + PRs welcome. The codebase is intentionally small enough to read in an hour:

• optimizer/pipeline.py orchestrates phases.
• optimizer/ai_guided_loop.py is the autonomous loop.
• ui/static/js/dashboard.js is one file; no frontend build step.

Run tests with pytest. There's no CI yet — fix that and we'll merge it.

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License

MIT — see LICENSE. Use it, fork it, ship it.

Built with Anthropic Claude for the reasoning layer and MetaTrader 5 for the backtests. APEX is independent of and not endorsed by either.