ggTrader

A sleek, high-performance algorithmic trading bot built for Kraken and other major exchanges. Designed for professional researchers and quantitative traders who demand speed, reproducibility, and robust optimization.

🚀 High-Level Overview

ggTrader provides a modular framework for developing, testing, and optimizing trading strategies. It leverages TimescaleDB for efficient time-series data management and VectorBT for lighting-fast backtesting.

Key Features

• Modular Architecture: Clean separation between data ingestion, signal generation, and execution logic.
• High-Performance Backtesting: Integrated FastBacktest.py utilizing VectorBT and CuPy for GPU-accelerated simulations.
• Advanced Optimization: Built-in Walk-Forward Optimization (WFO) and sensitivity analysis to ensure strategy stability.
• Robust Data Layer: Uses TimescaleDB (PostgreSQL) for centralized, high-speed OHLCV storage and retrieval.
• Professional Analytics: Seamless integration with Jupyter Notebooks for deep-dive visualization and reporting.

📖 Documentation

• Architecture Guide: Deep dive into the project structure and data flow.
• Installation & Setup: How to get ggTrader running on your local machine.

📁 Project Structure

• src/ggTrader/: Core package including core engine, data adapters, and indicators.
• scripts/: Operational scripts for backtesting, WFO, and sensitivity analysis.
• notebooks/: Research and visualization tools.
• results/: Standardized output for all strategy executions.
• data/: Local storage for cached data and exports.

⚡ Quick Start

1. Clone & Setup:

   pip install -e .

2. Run a Backtest:

   python scripts/run_backtest.py --symbols BTC-USD

3. Execute WFO:

   python scripts/run_walk_forward_optimization.py

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For a more detailed breakdown, please refer to the Architecture Guide.