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🤖 XAUUSD AI Trading Bot — Ichimoku Multi-Timeframe RL

RL (PPO) Trading Bot cho XAUUSD sử dụng chiến lược Ichimoku Cloud Break trên 4 timeframes đồng thời (M5/M15/H1/H4).
Inspired by MoonDev's DRL Trading Bot — nhưng Ichimoku là core, không phải generic indicators.

Python PyPI License Made with AI


⚡ Install

pip install xauusd-ichi-rl

CLI Commands

# 1. Train RL bot (cần file CSV XAUUSD M1 trong thư mục hiện tại)
ichi-train --timesteps 500000 --sl 5.0 --tp 3.0

# 2. Rule-based backtest + grid search optimizer
ichi-backtest --mode optimize --year 2026 --month 01

# 3. Generate file MQL5 EA (output → mql5_output/)
ichi-gen-mql5
# ↑ Ra IchiMTF_RL_Strategy.mq5 — copy vào MT5 Experts/ là dùng được!

💡 Workflow: ichi-trainichi-backtestichi-gen-mql5 → Copy .mq5 vào MetaTrader 5

🐍 Python API:

from xauusd_ichi import run_v2, generate_all
result = run_v2(timesteps=500_000, sl=5.0, tp=3.0)

📊 Kết Quả Backtest — Jan 2026 (Out-of-Sample)

Metric Giá trị Benchmark
Net Profit +$724 (+7.12%) MoonDev target: 5-7.5%/month
Profit Factor 3.76 Industry good: >1.5
Win Rate 86.7% MoonDev: 58-62%
Max Drawdown 0.12% MoonDev: 8-12%
Total Trades 406 ~20/day
Consecutive Wins 36
Training Time 24 phút 500k steps, 348 it/s

⚠️ Disclaimer: Kết quả quá khứ không đảm bảo hiệu suất tương lai. Test chỉ trên 1 tháng, cần validate thêm.


🧠 Tư Duy & Điểm Khác Biệt

So với MoonDev (forbbiden403/tradingbot):

MoonDev Bot này
Core Strategy 63 generic indicators Ichimoku Cloud Break (từ EA thực chiến)
Entry Logic RL tự quyết 100% RL + Ichimoku signal bias trong reward
SL/TP RL tự học cắt lỗ Built-in SL/TP (grid search optimized)
DCA Không DCA awareness (từ EA IchiDCA)
Speed ~35 it/s 348 it/s (pre-cached numpy arrays)
Features 140+ 128 (focused, less noise)

Quá trình phát triển (3 iterations):

v1: RL + Ichimoku only (M5)          → LỖ -4.95% ❌
    └─ Insight: 50 features, 1 TF không đủ edge
    
v1.5: Rule-based Ichimoku + Grid Search → LỖ -$65 ❌  
    └─ Insight: Ichimoku đơn lẻ trên M5 = quá nhiều noise
    
v2: RL + Multi-TF (M5/M15/H1/H4)    → LÃI +7.12% ✅
    └─ Insight: Multi-TF confirmation + RL flexibility = edge

Key Insights:

  1. Ichimoku cần multi-TF: M5 quá noisy, cần H1/H4 confirm trend
  2. Built-in SL/TP > RL-learned SL/TP: RL mất quá nhiều steps để học risk management
  3. Numpy pre-cache: Tăng speed 10x (35 → 348 it/s) bằng cách bỏ pandas.iloc
  4. 128 features > 140+ features: Focused features (Ichimoku core) < noise reduction

🏗️ Kiến Trúc

┌─────────────────────────────────────────────┐
│           Multi-TF Feature Engine            │
│  M1 Data → Resample → M5, M15, H1, H4      │
│                                              │
│  Per TF: Ichimoku│EMA│RSI│MACD│ATR│BB│ADX    │
│  + Sessions    + Price Returns               │
│  ─────────────────────────────────           │
│  → 128 features (forward-fill merge)         │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│         Trading Environment (Gym)            │
│  • Pre-cached numpy arrays (fast!)           │
│  • Built-in SL=$5 / TP=$3                    │
│  • Ichimoku-aware reward (Sharpe)            │
│  • Cooldown 5 bars (anti-overtrading)        │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│            PPO Agent (SB3)                   │
│  • Network: [256, 256]                       │
│  • 500k steps, n_steps=1024                  │
│  • Live metrics callback (pass tracking)     │
│  • Auto-save best checkpoint                 │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│         MT5-Style Report                     │
│  PF│WR│DD│Sharpe│Recovery│Consecutive│MFE/MAE│
└─────────────────────────────────────────────┘

🚀 Quick Start

Option A: Via PyPI (recommended)

pip install xauusd-ichi-rl

# Đặt file CSV vào thư mục rồi chạy:
ichi-train --timesteps 500000 --sl 5.0 --tp 3.0
ichi-backtest --mode optimize --year 2026 --month 01
ichi-gen-mql5

Option B: Clone & Run

git clone https://github.com/hungpixi/xauusd-ichimoku-rl-bot
cd xauusd-ichimoku-rl-bot
pip install -r requirements.txt

Prepare Data

Đặt file XAUUSD M1 CSV vào root:

XAUUSD_2025_10.csv  # Train
XAUUSD_2025_11.csv  # Train
XAUUSD_2025_12.csv  # Train
XAUUSD_2026_01.csv  # Test

Train + Test (1 lệnh)

python run_rl_v2.py --timesteps 500000 --sl 5.0 --tp 3.0
# hoặc:
ichi-train --timesteps 500000 --sl 5.0 --tp 3.0

Rule-Based Backtest + Grid Search

# Optimize SL/TP
ichi-backtest --mode optimize --year 2026 --month 01

# Single backtest
ichi-backtest --mode backtest --year 2026 --month 01 --sl 5 --tp 3

📁 Cấu Trúc

src/
├── data/
│   ├── data_loader.py          # Load CSV data
│   ├── resampler.py            # M1 → M5/M15/H1/H4
│   ├── multi_tf_engine.py      # ★ Multi-TF feature builder (128 features)
│   ├── ichimoku_features.py    # Ichimoku indicator calculations
│   └── macro_data.py           # DXY/VIX from Yahoo Finance
├── env/
│   └── trading_env.py          # ★ Gymnasium env (numpy-optimized, SL/TP)
├── strategy/
│   └── ichimoku_strategy.py    # ★ Rule-based strategy + grid search
├── models/
│   └── mt5_report.py           # MT5 Strategy Tester report format
└── rbi/
    └── progressive_validation.py # Progressive validation runner

run_rl_v2.py                    # ★ Main: Train + Test (1 command)
run_backtest.py                 # Rule-based backtest + optimizer

🔮 Hướng Phát Triển

  • Macro data integration: DXY/VIX correlation (fix merge bug)
  • Grid search SL/TP: Tìm SL/TP tối ưu cho RL
  • Multi-month validation: Test trên 2025 full (12 tháng)
  • DCA logic: Dollar Cost Averaging từ EA IchiDCA gốc
  • Live trading: MetaTrader 5 integration
  • Dreamer V3: World-model RL (more sample efficient)

📚 References


🤝 Bạn muốn Bot Trading AI tương tự?

Bạn cần Chúng tôi đã làm ✅
Bot trade tự động RL Bot XAUUSD multi-TF
Backtest nhanh Grid search 576 combos trong vài giây
Báo cáo chuyên nghiệp MT5 Strategy Tester format
Tối ưu chiến lược Multi-indicator + multi-timeframe
🌐 Yêu cầu Demo 💬 Zalo 📧 Email

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