bquant 0.0.0

Quantitative research toolkit for financial markets

BQuant - Quantitative Research Toolkit

BQuant is a universal toolkit for quantitative research of financial markets. The project starts with MACD zone analysis as the first use case, but the architecture is designed for exploring various aspects: technical indicators, chart patterns, candlestick formations, time series, and machine learning applications.

🔧 Key Features

• Universal configuration system - support for multiple data sources and brokers
• Multi-level analysis - technical, statistical, graphical, candlestick, time series
• ML readiness - structure for machine learning (stubs)
• Visualization tools - charts and reports
• Research environment - notebooks and experiments
• Automated pipelines - ready-to-use analysis scripts

🚀 Quick Start

Installation

# Install in development mode
pip install -e .

# Install with optional dependencies
pip install -e .[dev,notebooks]

Basic Usage

from bquant.data import load_symbol_data
from bquant.indicators import MACDAnalyzer

# Load data
data = load_symbol_data('XAUUSD', '1h')

# Analyze MACD zones
analyzer = MACDAnalyzer(data, fast=8, slow=21)
zones = analyzer.identify_zones()

print(f"Found {len(zones)} zones")

Command Line

# Analyze single instrument
bquant-analyze XAUUSD

# Batch analysis
bquant-batch EURUSD GBPUSD XAUUSD

📋 Project Structure

This is a monorepo that contains:

• bquant/ - Python package (for PyPI)
• research/ - Jupyter notebooks and experiments
• scripts/ - Automation scripts
• data/ - Data storage
• tests/ - Test suite
• docs/ - Documentation

🛠️ Development

Setting up development environment

# Create virtual environment
python -m venv venv_bquant_dell

# Activate (Windows)
venv_bquant_dell\Scripts\activate

# Activate (Linux/Mac)
source venv_bquant_dell/bin/activate

# Install dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .[dev]

Running tests

pytest tests/ -v

📚 Documentation

• API Documentation
• Tutorials
• Examples

🎯 Roadmap

• Phase 1: Core functionality (data, MACD analysis, statistics)
• Phase 2: Extended visualization, time series, other indicators
• Phase 3: Full ML, chart patterns, automation

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests.

📞 Contact

• Author: kogriv
• Email: [e-mail]
• Repository: https://github.com/kogriv/bquant