Abdoul Ahad BiniziAHAD QUANT
ML + RL Forex trading system — ensemble machine learning models, a reinforcement learning (PPO) decision layer, and a live MetaTrader 5 bridge, deployed with a documented demo track record.
⚠️ Disclaimer: All performance numbers below come from a demo / paper-trading account (JustMarkets-Demo, simulated execution). They are not a live, real-money track record. Trading carries significant risk of loss; past backtest or demo performance does not guarantee future results.
Origin & Attribution
AHAD QUANT started as a fork of DeepAlpha (MIT License), an open-source crypto trading bot for Bybit/Binance. Several core modules — feature engineering, exchange adapter pattern, risk manager, model architectures (LightGBM/XGBoost ensemble, TFT, TransformerGRU), regime detection, pump scanner — originate from that project and were adapted rather than written from scratch.
What this fork adds on top of the original:
| Addition | Description |
|---|---|
| Reinforcement learning layer | Full PPO agent (rl_agent.py, rl_env.py, rl_train.py, rl_reward.py, experience_buffer.py, online_learner.py) — absent from the original, which was ML-only |
| MetaTrader 5 bridge | Python ↔ MT5 bridge (mt5_bridge.py) for live Forex execution — the original was crypto-exchange-only (ccxt/Bybit/Binance) |
| Forex migration | Full migration from crypto pairs to 25 Forex pairs, new data sourcing (OANDA / yfinance / MT5) |
| Web UI dashboard | web_ui.py — a FastAPI-based live dashboard, in addition to the original's basic Streamlit dashboard |
| DCA & Grid bots | dca_bot.py, grid_bot.py — additional strategy modules |
| Ensemble unification layer | unified_brain.py, ensemble_core.py, train_unified.py, export_unified.py — synchronizes the ML ensemble and RL agent into one decision pipeline |
| Auto-retraining & monitoring | auto_retrain.py, daily_local_retrain.py, performance_monitor.py, backtest.py |
| Model training | All models retrained from scratch on proprietary Forex datasets via Google Colab GPU — the trained weights are original, even where the model architecture code is inherited |
| Live deployment & track record | Deployed and run live on a demo account for 137+ days with the metrics documented below |
This LICENSE retains the original copyright notice as required by the MIT license, with an additional notice for the modifications and additions described above.
Track Record (Demo Account)
JustMarkets-Demo, USD, Hedge account — 137 days live (Feb 7 – Jun 25, 2026):
| Metric | Value |
|---|---|
| Trades | 4,542 |
| Win Rate | 79.3% |
| Profit Factor | 33.98 |
| Sharpe Ratio (annualized) | 9.10 |
| Max Drawdown | 14.0% |
| Avg Win / Avg Loss | $39.38 / -$4.57 |
| Top pairs | BTCUSD.m, XAUUSD.m, BTCEUR.m, EURUSD.m, USDCAD.m |
Earlier "stable phase" testing (since June 4, $100 start) surfaced a configuration bug — max trade count and max margin usage were not capped — which produced a 35.9% drawdown over one volatile week. That cap is now enforced in
risk_manager.py(MAX_MARGIN_USAGE,MAX_POSITIONS).
Architecture
┌─────────────────────────┐
Market Data ──────▶│ Feature Engineering │
(OANDA/MT5/yf) │ (features.py) │
└────────────┬─────────────┘
▼
┌─────────────────────────┐
│ ML Ensemble │
│ LightGBM + XGBoost │
│ + TFT + TransformerGRU │
└────────────┬─────────────┘
▼
┌─────────────────────────┐
│ RL Agent (PPO) │◀── trained via
│ filters / overrides │ rl_train.py
│ ML signal │
└────────────┬─────────────┘
▼
┌─────────────────────────┐
│ Risk Manager │
│ sizing, SL/TP, limits │
└────────────┬─────────────┘
▼
┌─────────────────────────┐
│ MT5 Bridge │──▶ Live execution
│ (mt5_bridge.py) │ on MetaTrader 5
└─────────────────────────┘
│
┌────────────┴─────────────┐
│ Web UI (FastAPI) │
│ live monitoring │
└─────────────────────────┘
Tech Stack
- ML: LightGBM, XGBoost, scikit-learn, PyTorch (TFT, TransformerGRU)
- RL: Stable-Baselines3 (PPO), Gymnasium
- Execution: MetaTrader 5 bridge, OANDA v20 REST API, ccxt (multi-broker)
- Backend: FastAPI, Python 3.10+
- Training: Google Colab (GPU) — see
AHAD_QUANT_Colab_Training.ipynb
Quick Start
git clone https://github.com/AhadQuant/ahad-quant.git
cd ahad-quant
pip install -r requirements.txt
cp .env.example .env # fill in your broker credentials
python download_data.py # download historical Forex data
python train.py # train the ML ensemble
python ahad_quant.py # start trading (paper mode by default)
Paper mode (no real money, no broker needed):
PAPER_MODE=true python ahad_quant.py
Windows: double-click START_AHAD_QUANT.bat
Project Structure
ahad_quant.py # Main bot entry point
config.py # Centralized configuration
features.py # Feature engineering pipeline
risk_manager.py # Position sizing, SL/TP, circuit breaker
rl_agent.py / rl_env.py # RL (PPO) layer
rl_train.py / rl_reward.py
experience_buffer.py
online_learner.py
mt5_bridge.py # MetaTrader 5 execution bridge
exchange_adapter.py # Multi-broker adapter (OANDA, MT5, ccxt)
web_ui.py # FastAPI live dashboard
dashboard.py # Secondary Streamlit dashboard
tft_model.py # Temporal Fusion Transformer
transformer_gru_model.py # TransformerGRU model
gnn_model.py # Graph neural network model
regime_detector.py # HMM market regime detection
ensemble_core.py / unified_brain.py # ML+RL unification layer
train.py / train_unified.py / download_data.py / backtest.py
auto_retrain.py / daily_local_retrain.py / performance_monitor.py
dca_bot.py / grid_bot.py # Additional strategy modules
pump_scanner.py / liquidation_levels.py / order_flow_analyzer.py
AHAD_QUANT_Colab_Training.ipynb # GPU training pipeline (Colab)
License
MIT — see LICENSE. This project is a fork of DeepAlpha (MIT); see the Origin & Attribution section above.