Adaptive Crypto Trading Bot

Regime-adaptive cryptocurrency trading system achieving 11.6% returns with 80% win rate, validated via 1,000 Monte Carlo simulations.

🎯 Key Results

Metric	Value
Total Return	+11.6% (536 days)
Win Rate	80% (16 wins, 4 losses)
Sharpe Ratio	0.61
Max Drawdown	-5.05%
Monte Carlo Validation	100% profitable

🚀 What This Does

This system adapts to market conditions by:

1. Detecting Market Regimes (Trending/Ranging/Volatile using ADX & ATR)
2. Selecting Optimal Strategy (Momentum for trends, Mean Reversion for ranges)
3. Rigorous Validation (Monte Carlo, Walk-Forward, Parameter Optimization)

📊 Visual Results

Mean Reversion Strategy achieving 11.6% returns with controlled drawdowns

Market regime classification: Trending (blue), Ranging (green), Volatile (red)

View Complete Analysis → | See All Visualizations →

🏗️ Architecture

Data Pipeline → Regime Detection → Strategy Selection → Backtesting → Results
     ↓               ↓                    ↓                 ↓            ↓
  PostgreSQL    ADX/ATR Analysis    5 Strategies      Monte Carlo   Visualizations

🛠️ Tech Stack

• Python 3.11 - Core language
• PostgreSQL + Redis - Data storage & caching
• CCXT - Exchange API integration
• Pandas/NumPy - Data analysis
• Matplotlib/Seaborn - Visualization
• Docker - Containerization

📈 Quick Start

# 1. Setup environment
python -m venv venv
venv\Scripts\activate  # Windows
pip install -r requirements.txt

# 2. Start database
cd docker && docker-compose up -d

# 3. Run analysis notebook
jupyter notebook ANALYSIS.ipynb

🎓 What I Learned

• Mean Reversion works in ranging markets (80% win rate)
• Transaction costs matter - Careful modeling prevents overfitting
• Monte Carlo validates robustness - 100% profitable across 1,000 simulations
• Regime detection reduces risk - Avoid using wrong strategy in wrong conditions

📁 Project Structure

crypto-trader/
├── src/                    # Production code
│   ├── regime/            # Market regime detection
│   ├── strategies/        # 5 trading strategies
│   ├── backtesting/       # Monte Carlo, Walk-Forward
│   └── visualization/     # Professional charts
├── scripts/               # Analysis scripts
├── tests/                 # Unit tests
├── ANALYSIS.ipynb         # Main analysis notebook
└── MASTERPLAN.md          # Complete project documentation

📝 Resume Bullet Points

• Built adaptive crypto trading bot achieving 11.6% returns validated via 1,000 Monte Carlo simulations
• Implemented regime detection using ADX/ATR to classify market conditions and select optimal strategies
• Engineered backtesting framework with realistic transaction costs achieving 80% win rate
• Optimized 5 strategies across 175 parameter combinations using grid search
• Designed data pipeline processing 735 days of OHLCV data with 16 technical indicators

🔮 Future Improvements

• ML-based regime detection (replace rule-based ADX)
• Multi-asset portfolio with correlation analysis
• Live paper trading deployment
• Real-time monitoring dashboard
• Dynamic position sizing based on drawdown

📚 Documentation

• MASTERPLAN.md - Complete project guide (setup, testing, presentation)
• ANALYSIS.ipynb - Interactive analysis notebook
• tests/ - Test suite

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For detailed implementation guide, see MASTERPLAN.md