LEGSTECH Optimizer
6caafdb794
Major upgrade making the AI loop visible and the project ready for public release. UI / UX - Live AI Thinking Feed: streams reasoning, decisions, and outcomes per iteration - Parameter Changes panel: prev → new + reason for every AI-driven edit - Validation Activity panel: out-of-sample + sensitivity runs with live metrics - Early Termination banner: surfaces why optimization stopped (targets met, no profit, budget, stuck, user stop) - 3-phase tracker renamed Exploration / Iteration / Validation with live N/total - Best Result modal exposes Evolution Path showing how the AI arrived at the winner - Run-detail modal accessible from every recent run row - Setup form validation (dates, walk-forward order, params selection, AI targets) - Pause button removed; misleading sidebar nav consolidated to Dashboard / New Run / Reports / Source Backend - AIGuidedLoop streams ai_thinking, param_changes, ai_targets_met, ai_stuck - Pipeline emits validation_start / validation_run_start / validation_run_complete / validation_done - Pipeline emits early_termination on every early-stop path - /api/best_result returns best run + full evolution chain - /api/run/<id> + /api/runs sorted by ts - AIReasoner falls back to ANTHROPIC_API_KEY env var when config is a placeholder - Demo mode (APEX_DEMO_MODE=1) generates deterministic synthetic backtests so judges can run end-to-end without MT5 Open-source readiness - README.md with pitch, demo flow, architecture diagram, quickstart, event reference - LICENSE (MIT) - config.example.yaml template (config.yaml now git-ignored) - requirements.txt: added anthropic / requests / psutil / beautifulsoup4, capped majors - .gitignore: secrets, *.set, scratch screenshots, ea_registry.yaml - demo/run_demo.py: one-command offline demo runner - 10 polished screenshots for README + judge review
208 lines
8.8 KiB
Markdown
208 lines
8.8 KiB
Markdown
# APEX — AI‑Powered MT5 EA Optimizer
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> **An AI trader thinking out loud while it tests, fails, and improves a strategy.**
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APEX is an autonomous optimizer for MetaTrader 5 Expert Advisors. Instead of brute‑forcing
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parameters with grid search, an LLM reads each backtest result, decides which parameter to
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change and why, then runs the next backtest — iterating toward profit‑factor / drawdown /
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Calmar targets you set. Every reasoning step streams live to a dashboard.
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---
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## Why this is different
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| Traditional optimizers | APEX |
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| --- | --- |
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| Brute‑force grid / genetic search | AI reads each result, **decides** what to change |
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| Black box — see only final winner | Live **thinking feed** + per‑iteration param diffs |
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| No notion of *why* a config works | Stores AI analysis next to every run |
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| Stops after N iterations | Stops when **quality targets are met** (early exit) |
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| One‑shot validation | Out‑of‑sample **+ sensitivity** with live progress |
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---
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## Demo
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The dashboard shows three live phases — **Exploration → Iteration → Validation** — with the
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AI's reasoning streaming on the right, parameter changes per iteration in the centre, and an
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out‑of‑sample/sensitivity validation panel that updates as MT5 finishes each test.
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Other views: [setup wizard](screenshots/setup.png) · [settings modal](screenshots/settings_modal.png)
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---
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## How it works
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```
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┌──────────────────────────────────────────────────────────────────────────┐
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│ APEX OPTIMIZATION LOOP │
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│ │
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│ Phase 1: EXPLORATION │
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│ Latin‑Hypercube sample N parameter sets → run in MT5 Strategy Tester │
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│ → score with Calmar / PF / MFE / session‑stability / recovery │
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│ │
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│ Phase 2: AI ITERATION (autonomous loop) │
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│ ┌──► Claude reads full history + targets + parameter schema │
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│ │ ↓ │
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│ │ Claude returns: { changes:[{param,value,reason}], confidence } │
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│ │ ↓ │
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│ │ Apply changes (clamped to schema bounds), dedupe, run backtest │
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│ │ ↓ │
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│ │ Stream `ai_thinking` + `param_changes` events to UI │
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│ │ ↓ │
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│ └──── Targets met? → exit early. Stuck? → random escape. │
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│ │
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│ Phase 3: VALIDATION │
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│ Out‑of‑sample run on unseen dates + ±20% sensitivity probe on the │
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│ top parameter → verdict: RECOMMENDED / RISKY / NOT_RELIABLE │
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│ │
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│ Output: ranked .set file + per‑run report folder + final verdict │
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└──────────────────────────────────────────────────────────────────────────┘
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```
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The AI loop lives in [`optimizer/ai_guided_loop.py`](optimizer/ai_guided_loop.py); the
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reasoner contract is in [`analysis/ai_reasoner.py`](analysis/ai_reasoner.py); event emission
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to the UI flows through [`optimizer/pipeline.py`](optimizer/pipeline.py) via SocketIO.
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---
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## Quick start
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### 1. Clone + install
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```bash
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git clone https://github.com/<your-user>/MT5_Optimizer.git
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cd MT5_Optimizer
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python -m venv .venv
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.venv\Scripts\activate # Windows
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# source .venv/bin/activate # macOS/Linux
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pip install -r requirements.txt
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```
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### 2. Configure
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Copy the example config and fill it in:
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```bash
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cp config.example.yaml config.yaml
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```
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Set your Anthropic API key (get one at <https://console.anthropic.com/>):
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```bash
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# Option A — environment variable (recommended)
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setx ANTHROPIC_API_KEY "sk-ant-..." # Windows
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export ANTHROPIC_API_KEY="sk-ant-..." # macOS/Linux
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# Option B — paste into config.yaml under ai.anthropic_api_key
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```
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Edit `config.yaml` to match your local MT5 install paths under `mt5:` (terminal exe,
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AppData path, MQL5 Files path).
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### 3. Launch
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```bash
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python app.py
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```
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Open <http://localhost:5000>. Register your EA on the **Setup** page, set thresholds,
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hit **Start**, and watch the AI think.
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### Demo mode (no MT5 required)
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Don't have MT5 installed? Run the offline demo that feeds synthetic backtest results
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through the same AI loop and dashboard:
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```bash
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python -m demo.run_demo
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```
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This is the path to use if you're a hackathon judge — you'll see the full thinking feed,
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parameter‑change panel, validation phase, and verdict screen without needing a Windows
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machine with MT5.
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---
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## Configuration cheatsheet
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| Key | What it does |
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| --- | --- |
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| `ai.enabled` | Master toggle for the AI reasoning layer. |
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| `ai.model` | `claude-opus-4-7` (best), `claude-sonnet-4-6` (balanced), `claude-haiku-4-5` (fast). |
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| `thresholds.min_profit_factor` / `min_calmar` | Quality gates a result must clear. |
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| `optimization.max_iterations` | Hard cap on AI loop iterations. |
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| `mt5.terminal_exe` | Full path to `terminal64.exe`. |
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| `periods.train_*` / `validate_*` / `oos_*` | Train + walk‑forward validation date ranges. |
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The full schema lives in [`config.example.yaml`](config.example.yaml) with comments.
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---
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## Project layout
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```
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MT5_Optimizer/
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├── app.py Flask + SocketIO server (entry point)
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├── config.example.yaml Configuration template
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├── analysis/
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│ └── ai_reasoner.py Claude API client (analyze + suggest_next_params)
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├── optimizer/
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│ ├── pipeline.py 3‑phase pipeline orchestrator
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│ ├── ai_guided_loop.py Autonomous AI iteration loop
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│ ├── result_ranker.py Scoring & ranking of runs
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│ └── session_config.py Per‑run config dataclass
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├── ea/
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│ └── schema.py EA parameter schema + clamp/validation
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├── mt5/ MT5 launcher, ini builder, html report parser
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├── reports/
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│ └── writer.py Per‑run HTML/CSV/JSON output
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├── ui/
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│ ├── templates/ dashboard.html, setup.html, reports_index.html
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│ └── static/js/dashboard.js All client‑side logic
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└── tests/
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```
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See [`PROJECT_HANDOFF.md`](PROJECT_HANDOFF.md) for a deeper architectural tour.
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---
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## Live events (SocketIO)
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The dashboard subscribes to these — useful if you want to plug a different UI on top:
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| Event | When it fires | Payload (key fields) |
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| --- | --- | --- |
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| `phase_start` | Each phase begins | `phase`, `total`, `mode` |
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| `run_complete` | Any backtest finishes | `run_id`, `phase`, `net_profit`, `profit_factor`, `calmar`, `max_drawdown`, `score`, `params` |
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| `ai_thinking` | AI narrates a decision | `msg`, `kind` (`info`/`reasoning`/`decision`/`success`/`warning`/`hypothesis`), `iteration`, `phase` |
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| `ai_iteration_start` / `ai_iteration_complete` | Each AI loop iteration | `iteration`, `analysis`, `change_records`, `confidence`, `goal_status` |
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| `param_changes` | Per‑iteration parameter diff | `iteration`, `changes:[{param, from, to, reason}]`, `confidence` |
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| `validation_start` / `validation_run_start` / `validation_run_complete` / `validation_done` | Phase 3 visibility | `kind` (`oos`/`sensitivity`), metrics, `passing` |
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| `early_termination` | Pipeline stops before max_iterations | `reason` (`targets_met`/`no_profit`/`budget_exhausted`/`stuck_escape`/`user_stop`), `message`, `details` |
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| `optimization_complete` | Run finished | `verdict`, `best_run_id`, `set_file_url`, full metrics |
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---
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## Contributing
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Bug reports + PRs welcome. The codebase is intentionally small enough to read in an hour:
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- `optimizer/pipeline.py` orchestrates phases.
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- `optimizer/ai_guided_loop.py` is the autonomous loop.
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- `ui/static/js/dashboard.js` is one file; no frontend build step.
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Run tests with `pytest`. There's no CI yet — fix that and we'll merge it.
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---
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## License
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MIT — see [LICENSE](LICENSE). Use it, fork it, ship it.
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Built with [Anthropic Claude](https://claude.com/) for the reasoning layer and
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[MetaTrader 5](https://www.metatrader5.com/) for the backtests. APEX is independent of and
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not endorsed by either.
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