About QuantBot

QuantBot is an advanced cryptocurrency trading backtesting system I developed over four months to implement and test sophisticated trading strategies. This project showcases my expertise in quantitative analysis, backend engineering, and production-grade system deployment. While visitors can explore and test the system with default settings through the web interface, the core software architecture is developed with significantly greater depth and complexity than what's presented in the frontend showcase.

Key Features

  • Advanced Trading Strategies: Supports dynamic position sizing and price-range-based take-profit/stop-loss settings for both long and short positions with comprehensive risk management.
  • Multi-Timeframe Backtesting: Comprehensive support for all standard timeframes from 1-minute to daily intervals, enabling granular strategy execution and cross-timeframe analysis.
  • Technical Indicators: Includes RSI (7 and 14 periods), MACD, Bollinger Bands, EMA (50 and 200), VWMA (20 and 50), ATR, and relative volume indicators with pre-calculated values for optimal performance.
  • Performance Analytics: Calculates comprehensive metrics including win rate, profit factor, Sharpe ratio, maximum drawdown, expectancy, and indicator effectiveness analysis.
  • Risk Management: Features leverage control, concurrent trade limits, commission/slippage modeling, and sophisticated position management with margin calculations.
  • Dual-Configuration Testing: Enables side-by-side comparison of long and short strategies with combined portfolio insights and performance analysis.

Data Coverage

Currently operational with over four years of historical data across five major cryptocurrency trading pairs: AVAXUSDT, SOLUSDT, SUIUSDT, XRPUSDT, and BTCUSDT.

High-frequency 1-minute execution data with support for all standard timeframes (1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d) for comprehensive cross-timeframe strategy validation.

Optimized data storage and retrieval using Parquet format, achieving 70% size reduction compared to traditional CSV storage.

Technical Architecture

QuantBot's architecture demonstrates my capabilities in building scalable, efficient trading systems:

Backend: Built with Python 3.12+, utilizing Flask for the web framework, Pandas and NumPy for high-performance data processing, and a modular design with clear separation of concerns across configuration, backtesting, and visualization components.
Frontend: Developed with HTML5, CSS3, Bootstrap 5, and JavaScript for a responsive interface that demonstrates core backend capabilities with simplified configuration options.
Visualization: Leverages Matplotlib and Plotly for interactive charts, including candlestick patterns, equity curves, trade markers, and comprehensive performance analytics.
Deployment: Hosted on a Strato Ubuntu server with Apache reverse proxy configuration, secured by SSL encryption via Let's Encrypt. After exploring different deployment approaches, I implemented the reverse proxy architecture for optimal performance and maintainability.

Competitive Advantages

  • Bidirectional Strategy Testing: Unlike most backtesting platforms that focus on single-direction strategies, QuantBot enables simultaneous long and short position testing with inverted logic and separate risk parameters for comprehensive market analysis.
  • Multi-Asset Portfolio Testing: Supports comprehensive backtesting across multiple cryptocurrency assets simultaneously, unlike single-asset-focused platforms.
  • Production-Ready Architecture: Fully operational system with rigorous testing, SSL security, and performance optimizations suitable for professional trading analysis.

System Access

QuantBot is accessible at codefoliox.de, where visitors can explore the system's capabilities and run backtests with default configurations. The web interface provides a streamlined portfolio showcase experience where visitors can explore the system's capabilities using default configurations. This frontend demonstration represents a simplified version of the underlying trading engine - the non-hosted backend software operates with significantly more advanced features, extensive customization options, and sophisticated analysis tools designed for professional quantitative trading research.

This project exemplifies my ability to design, build, and deploy sophisticated quantitative trading tools from conception to production, integrating complex backend logic, efficient data processing, and user-facing interfaces seamlessly.

You can contact me at info@codefoliox.de.

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