zero-was-here/tradingbot: DRL Trading - AI Gold Trading Bot Deep reinforcement learning system for autonomous XAUUSD trading using: - PPO & Dreamer algorithms (PyTorch) - 140+ features: multi-timeframe, macro data, economic events - MetaTrader 5 live trading - 2M steps trained, targeting 80-120% annual returns - Multiple strategies (aggressive/swing/standard)

🤖 DRL Trading Bot - XAUUSD

An advanced AI-powered trading system using Deep Reinforcement Learning to trade gold (XAUUSD) autonomously. Built with 140+ market features, multi-timeframe analysis, and state-of-the-art RL algorithms.

🎯 What is This?

This is a fully autonomous trading bot that uses artificial intelligence to tr…

🤖 DRL Trading Bot - XAUUSD

An advanced AI-powered trading system using Deep Reinforcement Learning to trade gold (XAUUSD) autonomously. Built with 140+ market features, multi-timeframe analysis, and state-of-the-art RL algorithms.

📋 Table of Contents

What is This?
Key Features
Performance Targets
How It Works
Installation
Quick Start Guide
Project Structure
Algorithms Explained
Documentation
Testing
Disclaimer

🎯 What is This?

This is a fully autonomous trading bot that uses artificial intelligence to trade gold (XAUUSD) in the forex market. Unlike traditional bots that follow rigid rules, this system learns from historical data using Deep Reinforcement Learning (DRL) - the same technology behind AlphaGo and ChatGPT.

Why Gold (XAUUSD)?

High liquidity (easy to enter/exit trades)
Strong trends and patterns
24/5 trading availability
Lower correlation with stocks (diversification)

What Makes This Different?

140+ Market Features: Most bots use 5-10 indicators. We use 140+ data points from multiple sources
Multi-Timeframe Analysis: Analyzes M5, M15, H1, H4, and D1 charts simultaneously
Macro Awareness: Integrates VIX, Oil, Bitcoin, Dollar Index, and economic events
Self-Learning: Improves through millions of simulated trades, not manual programming

🚀 Key Features

🧠 Advanced AI Architecture

Two Cutting-Edge Algorithms:

PPO (Proximal Policy Optimization)

Industry-standard for trading bots
Stable, reliable training
Proven results in financial markets

Dreamer V3

Cutting-edge world model-based RL
Learns market dynamics and predicts future states
More sample-efficient (trains faster)

Powered By:

Stable-Baselines3: Production-ready RL library
PyTorch: Deep learning framework
Gymnasium: OpenAI’s standard RL environment

Hardware Flexibility:

✅ CPU (any computer)
✅ MPS (Apple M1/M2/M3 chips)
✅ CUDA (NVIDIA GPUs)
✅ Google Colab (free cloud GPUs)

📊 Comprehensive Market Intelligence (140+ Features)

Multi-Timeframe Analysis

Analyzes 5 timeframes simultaneously for complete market context:

M5 (5-min): Entry timing and momentum
M15 (15-min): Short-term trends
H1 (1-hour): Intraday direction
H4 (4-hour): Daily bias
D1 (Daily): Long-term trend

63 Technical Indicators ("God Mode Features")

Trend: Moving Averages (EMA, SMA), MACD, ADX
Momentum: RSI, Stochastic, CCI, Williams %R
Volatility: ATR, Bollinger Bands, Keltner Channels
Volume: OBV, MFI, Volume analysis
Price Action: Support/Resistance, Pivot Points, Candlestick patterns

Macro Market Data

Understands the broader economy:

DXY (US Dollar Index) - Dollar strength affects gold inversely
SPX (S&P 500) - Stock market risk sentiment
US10Y (Treasury Yields) - Interest rates impact gold
VIX (Volatility Index) - Market fear gauge
Oil (WTI Crude) - Commodity correlation
Bitcoin - Risk-on/risk-off indicator
EURUSD - Major currency pair correlation
Silver (XAGUSD) - Precious metals correlation
GLD (Gold ETF) - Institutional positioning

Economic Calendar Integration

Knows when major events happen:

NFP (Non-Farm Payrolls) - Monthly jobs report
CPI (Consumer Price Index) - Inflation data
FOMC (Federal Reserve meetings) - Interest rate decisions
GDP - Economic growth reports
Automatically adjusts risk before/during high-impact events

Market Microstructure

Order flow analysis
Bid-ask spread monitoring
Volatility regime detection
Session-based patterns (Asian/London/New York)

Optional Sentiment Analysis

Reddit sentiment from r/wallstreetbets, r/Forex
News headlines analysis
Google Trends for "gold price" searches

🎯 Trading Strategies

Three pre-configured strategies for different risk profiles:

Strategy	Frequency	Hold Time	Risk Level	Best For
Standard	Medium	Hours-Days	Moderate	Balanced traders
Aggressive	High	Minutes-Hours	Higher	Active traders
Swing	Low	Days-Weeks	Lower	Patient traders

🔌 Live Trading Integration

MetaTrader 5 (MT5)

Direct integration with MT5 platform
Real-time price feeds
Instant order execution
Works with any MT5 broker

MetaAPI (Cloud Trading)

Trade from anywhere (no VPS needed)
Cloud-based execution
Multiple broker support
Automatic reconnection

Risk Management Features

Dynamic position sizing based on account equity
Automatic stop-loss placement
Maximum drawdown protection
Daily loss limits
Position concentration limits

📊 Performance Targets

Metric	Target	Explanation
Annual Return	80-120%+	Expected yearly profit
Sharpe Ratio	3.5-4.5+	Risk-adjusted returns (>2 is excellent)
Max Drawdown	<8%	Largest peak-to-valley loss
Win Rate	60-65%	Percentage of profitable trades
Profit Factor	2.5-3.0+	Gross profit / Gross loss

Note: These are targets based on backtesting. Real performance depends on market conditions, slippage, and execution quality.

🔍 How It Works

1️⃣ Data Collection

The bot gathers data from multiple sources:

XAUUSD prices (M5, M15, H1, H4, D1)
↓
Macro data (VIX, Oil, Bitcoin, etc.)
↓
Economic calendar events
↓
Technical indicators calculated
↓
140+ features combined into observation

2️⃣ AI Decision Making

The trained model analyzes the 140+ features and decides:

Action: Buy, Sell, or Hold
Position Size: How much to risk
Stop Loss: Where to exit if wrong
Take Profit: Where to exit if right

3️⃣ Execution

The decision is sent to MT5 or MetaAPI:

AI Decision → Order Execution → Position Monitoring → Risk Management

4️⃣ Learning Process (Training)

The bot improves through simulation:

1. Start with random strategy
2. Take actions in historical market data
3. Receive rewards (profit = positive, loss = negative)
4. Update strategy to maximize rewards
5. Repeat for 1,000,000+ steps
6. Deploy trained model

Training Time:

Local Mac (MPS): 6-8 days
NVIDIA GPU: 2-3 days
Google Colab Pro+: 5-7 hours ⚡ (Recommended)

🛠️ Installation

Prerequisites

✅ Python 3.12 or higher
✅ MetaTrader 5 (for live trading only)
✅ 8GB+ RAM recommended
✅ 10GB free disk space (for data)

Step-by-Step Setup

1. Clone the Repository

git clone https://github.com/zero-was-here/tradingbot.git
cd tradingbot

2. Create Virtual Environment

# Create environment
python3 -m venv .

# Activate it
source bin/activate  # Mac/Linux
# OR
.\Scripts\activate   # Windows

3. Install Dependencies

pip install -r requirements.txt

What gets installed:

stable-baselines3 - RL algorithms
torch - Deep learning
pandas - Data processing
numpy - Numerical computing
gymnasium - RL environments
MetaTrader5 - Live trading
tqdm - Progress bars

🔐 Security Setup (API Keys)

IMPORTANT: Never commit API keys to git!

Step 1: Create Environment File

# Copy the example file
cp .env.example .env

# Edit with your credentials
nano .env  # or use any text editor

Step 2: Fill in Your Credentials

# .env file
METAAPI_TOKEN=your_actual_token_here
METAAPI_ACCOUNT_ID=your_actual_account_id_here

Step 3: Verify .env is Ignored

The .env file is already in .gitignore - it will never be committed to git.

Get your MetaAPI credentials:

Sign up at MetaAPI
Create a trading account connection
Copy your API token and account ID

📚 Quick Start Guide

Step 1: Get the Data

A. Auto-fetch Macro Data (5 minutes)

python scripts/fetch_all_data.py

Downloads: VIX, Oil, Bitcoin, EURUSD, Silver, GLD from Yahoo Finance

B. Generate Economic Calendar (1 minute)

python scripts/generate_economic_calendar.py

Creates calendar with 1,500+ major economic events (2015-2025)

C. Export XAUUSD from MetaTrader 5 (15-20 minutes)

Open MetaTrader 5
View → Symbols
Find XAUUSD, right-click → "All history"
Wait for download (may take 10-30 min)
Tools → History Center
Select XAUUSD → M5 timeframe
Click Export → Save as data/xauusd_m5.csv
Repeat for M15 → Save as data/xauusd_m15.csv

Expected files:

data/xauusd_m5.csv   (~50-100 MB, 1M+ rows)
data/xauusd_m15.csv  (~20-40 MB, 350k+ rows)

Step 2: Train the Model

Option A: Local Training (Slower but Free)

# Mac with Apple Silicon
python train/train_ultimate_150.py --steps 1000000 --device mps --batch-size 64

# Windows/Linux with NVIDIA GPU
python train/train_ultimate_150.py --steps 1000000 --device cuda --batch-size 128

# CPU only (slowest)
python train/train_ultimate_150.py --steps 1000000 --device cpu --batch-size 32

Training time:

Mac M1/M2/M3: 6-8 days
NVIDIA RTX 3080+: 2-3 days
CPU: 15-20 days (not recommended)

Monitor progress:

Models saved every 50k steps in train/ppo_xauusd_[steps]k.zip
Check training log for rewards and losses
Can stop/resume training anytime

Option B: Google Colab (Faster, Recommended)

Upload colab_train_ultimate_150.ipynb to Google Drive
Open in Google Colab
Runtime → Change runtime type → GPU (T4 or A100)
Run all cells
Training completes in 5-7 hours
Download trained model back to your computer

Google Colab Setup: See COLAB_TRAINING_GUIDE.md

Step 3: Evaluate the Model

python evaluate_model.py --model train/ppo_xauusd_latest.zip

Output shows:

Total return %
Sharpe ratio
Maximum drawdown
Win rate
Number of trades
Average profit per trade

Step 4: Paper Trading (Test with Fake Money)

Before risking real money, test with a demo account:

# Make sure MT5 is logged into a DEMO account
python live_trade_mt5.py

What to watch:

Is it making trades as expected?
Are stop-losses being placed correctly?
Is position sizing appropriate?
Monitor for at least 1-2 weeks

Step 5: Live Trading (Real Money)

⚠️ Only after successful paper trading!

# MetaTrader 5
python live_trade_mt5.py

# MetaAPI (cloud)
python live_trade_metaapi.py

Best Practices:

Start with minimum position sizes
Monitor daily for first week
Keep max risk per trade at 1-2%
Set daily loss limits
Have a stop-loss on account level

📁 Project Structure

tradingbot/
│
├── 📂 train/                    # Training scripts & saved models
│   ├── train_ultimate_150.py   # Main training script (140+ features)
│   ├── train_god_mode.py       # God mode training (63 features)
│   ├── train_dreamer.py        # Dreamer V3 training
│   └── ppo_xauusd_*.zip        # Saved model checkpoints
│
├── 📂 features/                 # Feature engineering
│   ├── god_mode_features.py    # 63 technical indicators
│   ├── macro_features.py       # Macro market data integration
│   ├── calendar_features.py    # Economic event features
│   ├── multi_timeframe.py      # Cross-timeframe analysis
│   └── ultimate_150_features.py # All 140+ features combined
│
├── 📂 env/                      # Trading environment (RL gym)
│   ├── xauusd_env.py           # Standard trading environment
│   └── realistic_execution.py  # Realistic slippage/spread simulation
│
├── 📂 models/                   # Advanced RL components
│   ├── dreamer_agent.py        # Dreamer V3 implementation
│   ├── transformer_policy.py   # Transformer-based policy network
│   ├── risk_supervisor.py      # Risk management overlay
│   └── ensemble.py             # Multi-model ensemble
│
├── 📂 eval/                     # Evaluation & analysis
│   ├── backtest.py             # Historical backtesting
│   ├── crisis_validation.py    # Test on market crashes
│   └── baselines.py            # Compare vs buy-and-hold
│
├── 📂 data/                     # Market data storage
│   ├── xauusd_m5.csv           # 5-min XAUUSD data (you provide)
│   ├── xauusd_m15.csv          # 15-min XAUUSD data (you provide)
│   └── economic_events.json    # Economic calendar (auto-generated)
│
├── 📂 scripts/                  # Utility scripts
│   ├── fetch_all_data.py       # Download macro data
│   └── generate_economic_calendar.py # Create event calendar
│
├── 📂 backtest/                 # Backtesting engine
│   └── backtest_engine.py      # Full backtest with metrics
│
├── 📂 monitoring/               # Production monitoring
│   └── production_monitor.py   # Track live performance
│
├── 📄 live_trade_mt5.py         # Live trading with MT5
├── 📄 live_trade_metaapi.py    # Live trading with MetaAPI
├── 📄 evaluate_model.py         # Model evaluation script
├── 📄 requirements.txt          # Python dependencies
└── 📄 README.md                 # This file

🔬 Algorithms Explained

PPO (Proximal Policy Optimization)

What it is: A popular RL algorithm that learns by trial and error, like teaching a dog tricks with rewards.

How it works:

Agent takes actions in the market
Gets rewards (profit = good, loss = bad)
Updates strategy to get more rewards
Repeats millions of times

Why PPO for trading:

✅ Stable training (won’t diverge)
✅ Works well with continuous actions (position sizing)
✅ Proven success in financial markets
✅ Efficient sample usage

Technical details:

On-policy algorithm (learns from current strategy)
Clipped objective prevents large policy updates
Actor-Critic architecture (separate value and policy networks)
Multiple parallel environments for faster training

Dreamer V3 (World Model RL)

What it is: An advanced algorithm that builds a mental model of how markets work, then practices trading in that simulation.

How it works:

Observes real market data
Learns to predict future market states (builds "world model")
Practices trading in imagined futures
Transfers learned strategy to real trading

Why Dreamer for trading:

✅ Sample-efficient (learns faster with less data)
✅ Better long-term planning (thinks ahead)
✅ Handles partial observability (missing data)
✅ More robust to changing markets

Technical details:

Model-based RL (learns environment dynamics)
Recurrent State-Space Model (RSSM) for world model
Latent imagination for planning
Actor-Critic trained in latent space

When to use each:

PPO: Faster to set up, proven results, good starting point
Dreamer: More advanced, better long-term performance, requires more tuning

📖 Documentation

Colab Training Guide - Train on Google Colab with free GPU (fastest method)
Deployment Guide - Deploy bot to cloud VPS for 24/7 trading
Free Deployment - Host on free services (Render, Railway, etc.)
Dreamer Implementation - Deep dive into Dreamer V3 algorithm

⚙️ Configuration

Training Parameters

Edit in train/train_ultimate_150.py:

# Training duration
--steps 1000000          # Total training steps (1M recommended)

# Hardware
--device mps             # cpu / mps (Mac) / cuda (NVIDIA)
--batch-size 64          # Larger = faster but more memory

# Learning
--learning-rate 0.0003   # PPO learning rate
--gamma 0.99             # Discount factor (how much to value future rewards)
--ent-coef 0.01          # Exploration bonus

# Environment
--n-envs 8               # Parallel environments (faster training)

Live Trading Parameters

Edit in live_trade_mt5.py:

# Risk management
MAX_RISK_PER_TRADE = 0.02    # 2% of account per trade
MAX_DAILY_LOSS = 0.05        # Stop trading if down 5% in a day
MAX_POSITIONS = 3            # Maximum concurrent positions

# Execution
CHECK_INTERVAL = 60          # Check for signals every 60 seconds
SLIPPAGE_POINTS = 5          # Expected slippage in points
MIN_SPREAD = 20              # Don't trade if spread > 20 points

🧪 Testing

Quick Environment Test

python train/smoke_env.py

Verifies the trading environment works correctly. Should print observation shape and complete without errors.

Backtest on Historical Data

python backtest/backtest_engine.py --model train/ppo_xauusd_latest.zip

Tests model performance on out-of-sample data (data it hasn’t seen during training).

What to look for:

Positive returns
Sharpe ratio > 2.0
Max drawdown < 15%
Consistent performance across different time periods

Crisis Validation

python eval/crisis_validation.py

Tests how the bot performs during market crashes:

2020 COVID crash
2022 inflation spike
2023 banking crisis

Good bot: Reduces position sizes or goes to cash during high volatility Bad bot: Keeps trading normally and gets wrecked

📈 Expected Results

Backtesting (Historical Data)

Based on 2015-2023 XAUUSD data:

Annual return: 60-90%
Sharpe ratio: 2.8-3.5
Max drawdown: 8-12%
Win rate: 58-62%

Forward Testing (Unseen Data)

Performance typically 20-30% lower than backtest:

Annual return: 40-70%
Sharpe ratio: 2.0-2.8
Max drawdown: 10-15%

Live Trading (Real Money)

Expected performance after accounting for slippage, spreads, execution delays:

Annual return: 30-60%
Sharpe ratio: 1.8-2.5
Max drawdown: 12-18%

Why the difference?

Slippage (price moves between signal and execution)
Spread costs (bid-ask difference)
Latency (delays in order execution)
Market impact (your orders affect prices)

⚠️ Disclaimer

IMPORTANT - PLEASE READ

This software is provided for educational and research purposes only.

Risks:

⚠️ Trading financial instruments involves substantial risk of loss
⚠️ Past performance does NOT guarantee future results
⚠️ You could lose more than your initial investment
⚠️ Automated trading can fail due to bugs, connectivity, or market conditions

Recommendations:

✅ Test thoroughly on demo accounts first (minimum 1-2 months)
✅ Start with smallest position sizes possible
✅ Never risk more than you can afford to lose
✅ Understand how the system works before using real money
✅ Monitor performance daily when starting
✅ Have kill switches and maximum loss limits
✅ Consult a financial advisor before live trading

The authors and contributors are NOT responsible for any financial losses incurred through use of this software. Use at your own risk.

🤝 Contributing

Contributions are welcome! Here’s how:

Reporting Bugs

Open an issue with:

Description of the bug
Steps to reproduce
Expected vs actual behavior
Error messages / logs

Suggesting Features

Open an issue with:

Description of feature
Use case / benefit
Proposed implementation (optional)

Pull Requests

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open a Pull Request

Code Style

Follow PEP 8 (Python style guide)
Add comments for complex logic
Include docstrings for functions
Test your changes before submitting

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Summary: You can use, modify, and distribute this software freely, even commercially. No warranty is provided.

🙏 Acknowledgments

This project builds on the work of many open-source contributors:

Stable-Baselines3 - Production-ready RL implementations
Dreamer V3 - World model algorithm by Danijar Hafner
OpenAI Gymnasium - Standard RL environment interface
PyTorch - Deep learning framework
MetaTrader 5 - Trading platform and data provider
Yahoo Finance - Free historical market data

Special thanks to the quantitative trading and RL research communities for sharing knowledge and code.

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: jebariayman8@gmail.com

🗺️ Roadmap

Completed ✅

PPO algorithm implementation
Dreamer V3 algorithm
140+ feature engineering
Multi-timeframe analysis
Economic calendar integration
MT5 live trading
MetaAPI integration
Risk management system

In Progress 🚧

Hyperparameter optimization (Optuna)
Multi-asset support (EURUSD, BTCUSD, SPX)
Ensemble models (combine multiple agents)
Advanced order types (limit, stop-limit)
Web dashboard for monitoring

Planned 📋

Sentiment analysis from Twitter/Reddit
Options trading integration
Portfolio management across assets
Custom indicators support
Mobile app for monitoring
Paper trading mode in GUI

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