🚀 Quick Start

1. Clone & Setup Environment

git clone https://github.com/Lockdentrack3/EA_BOT_TRADING
cd EA_BOT_TRADING

# Create virtual environment
python -m venv venv
source venv/bin/activate          # Linux/Mac
venv\Scripts\activate             # Windows

# Install dependencies
pip install -r requirements.txt

2. Configure Environment

cp config/.env.example .env
# Edit .env with your credentials:
#   MT5_ACCOUNT, MT5_PASSWORD, MT5_SERVER
#   TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID
#   SECRET_KEY (generate with: python -c "import secrets; print(secrets.token_hex(32))")

3. Start the API Server

python main.py
# API available at: http://localhost:8000
# Swagger docs:     http://localhost:8000/docs

4. Install MT5 EA

1. Copy mql5/Experts/EA_BotTrading.mq5 to your MT5 Experts folder
2. Copy all .mqh files from mql5/Include/ to MT5 Include folder
3. Open MetaEditor → Compile EA_BotTrading.mq5
4. Attach EA to chart (any symbol, H1 or H4 recommended)
5. Enable "Allow algorithmic trading" and "Allow DLL imports"

5. Start MT5 Bridge (Windows only)

# On the Windows machine running MetaTrader 5:
python python/core/mt5_bridge.py

6. Start Telegram Bot

# Add to main.py startup or run standalone:
from telegram.bot import build_bot
app = build_bot(token=settings.TELEGRAM_BOT_TOKEN, admin_ids=settings.TELEGRAM_ADMIN_IDS)
app.run_polling()

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🐳 Docker Deployment

cd docker

# Development (SQLite):
docker compose up -d api

# Production (PostgreSQL + Nginx):
docker compose --profile postgres --profile prod up -d

# View logs:
docker compose logs -f api

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📊 Backtesting

import pandas as pd
from backtest.engine import BacktestEngine, BacktestConfig, WalkForwardAnalyzer, MonteCarloSimulator

# Load your OHLCV data
df = pd.read_csv("backtest/data/EURUSD_H1.csv", parse_dates=["datetime"], index_col="datetime")

config = BacktestConfig(
    symbol="EURUSD",
    timeframe="H1",
    start_date="2020-01-01",
    end_date="2024-01-01",
    initial_balance=10_000,
    risk_pct=1.0,
    sl_atr_mult=1.5,
    tp_atr_mult=3.0,
    min_confidence=85.0,
)

# Run backtest
engine = BacktestEngine(config)
result = engine.run(df)
print(result.metrics["summary"])

# Walk-Forward Analysis
wf = WalkForwardAnalyzer(n_windows=5, oos_ratio=0.3)
wf_result = wf.run(df, config, param_grid={
    "sl_atr_mult": [1.2, 1.5, 1.8],
    "tp_atr_mult": [2.5, 3.0, 3.5],
    "min_confidence": [80.0, 85.0, 90.0],
})
print(f"WF Efficiency: {wf_result['wf_efficiency']}")

# Monte Carlo Simulation
mc = MonteCarloSimulator()
mc_result = mc.simulate(result.trades, 10_000, n_simulations=1000)
print(f"Ruin Probability: {mc_result['ruin_probability']}%")

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📈 API Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/api/v1/signals/generate	Generate AI signal
GET	/api/v1/signals/history	Signal history
POST	/api/v1/risk/check	Risk pre-trade check
GET	/api/v1/analytics/performance	Full performance report
POST	/api/v1/analytics/trades	Record new trade
PUT	/api/v1/analytics/trades/{ticket}/close	Close trade
POST	/api/v1/backtest/run	Run backtest
POST	/api/v1/backtest/walkforward	Walk-forward analysis
POST	/api/v1/backtest/montecarlo	Monte Carlo simulation

Full interactive docs: http://localhost:8000/docs

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🤖 Telegram Commands

Command	Description
/status	Full system status + account
/start	Enable trading
/stop	Pause trading (keeps open positions)
/profit	Profit summary (daily/weekly/monthly/all-time)
/loss	Loss & drawdown summary
/opentrades	List all open positions
/performance	Full analytics report
/help	Show all commands

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⚙️ Risk Parameters

Parameter	Default	Description
MAX_RISK_PER_TRADE	1.0%	Maximum account risk per trade
MAX_DAILY_LOSS_PCT	3.0%	Daily loss circuit breaker
MAX_WEEKLY_LOSS_PCT	8.0%	Weekly loss circuit breaker
MAX_OPEN_TRADES	3	Max simultaneous positions
MAX_CONSECUTIVE_LOSSES	3	Losses before circuit breaker
MIN_CONFIDENCE	85%	Minimum AI signal confidence

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🔒 Security Checklist

• Change SECRET_KEY in .env (64 random chars minimum)
• Never commit .env to git (it's in .gitignore)
• Use strong MT5_PASSWORD
• Set TELEGRAM_ADMIN_IDS to your Telegram user ID only
• Use PostgreSQL in production (not SQLite)
• Enable Nginx with SSL in production
• Run containers as non-root user (already configured in Dockerfile)
• Rotate API keys periodically
• Enable firewall: only expose port 80/443 externally

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🔬 AI Upgrade Roadmap

Phase 1 — Current (v2.0)

• Multi-factor scoring (Trend + Momentum + Volume + Liquidity + Volatility)
• Smart Money Concepts (BOS, CHoCH, OB, FVG)
• Deterministic rule-based confidence scoring

Phase 2 — ML Integration (v2.5)

• Train LSTM/GRU on historical signals + outcomes
• Feature engineering pipeline (100+ features)
• Ensemble: XGBoost + Neural Net + Rule-based
• Online learning (model updates every week)

Phase 3 — Reinforcement Learning (v3.0)

• PPO/SAC agent for trade management (BE, trail, partial)
• Multi-agent system (one agent per symbol)
• Reward function: risk-adjusted returns (Sortino)

Phase 4 — Advanced (v3.5)

• NLP news sentiment analysis (real-time)
• Orderbook microstructure features (L2 data)
• Cross-asset correlation ML model
• Regime-adaptive strategy selection

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📝 License

Proprietary — For personal trading use only. Not financial advice. Trade at your own risk.