AI-powered financial intelligence and automated market analysis system

ZenMarket AI is a professional-grade financial intelligence system that combines real-time market data, news analysis, sentiment tracking, and AI-powered insights to deliver actionable market intelligence. Built with production-quality code, comprehensive testing, and robust risk management.

• Automated News Aggregation: Multi-source financial news fetching
• AI-Powered Analysis: OpenAI GPT-4 and Anthropic Claude integration
• Sentiment Analysis: Market sentiment tracking with lexicon-based and AI methods
• Daily Financial Brief: Comprehensive market overview reports

• Technical Analysis: MA20/50, RSI(14), Bollinger Bands, ATR, MACD
• Trading Signals: AI-generated BUY/SELL/HOLD signals with confidence scores
• Visual Charts: Professional matplotlib charts with indicators
• Market Bias Detection: Overall market sentiment analysis

• Paper Trading: Realistic order execution simulator
• Position Sizing: Fixed fractional, Kelly criterion, percent of equity, R-multiples
• Risk Management: Circuit breakers, position limits, drawdown protection
• Performance Metrics: Sharpe, Sortino, CAGR, Max DD, Win rate, Profit factor
• Backtesting Engine: Historical simulation with detailed reports

• Type Safety: Full mypy type checking
• Test Coverage: ≥90% coverage with unit and integration tests
• Security: Bandit security scanning, pip-audit vulnerability checks
• CI/CD: Automated testing and quality gates
• Documentation: Comprehensive docs with MkDocs
• Code Quality: Black, isort, ruff linting

• Python 3.11 or 3.12
• API keys for:
  • NewsAPI (optional, for news fetching)
  • OpenAI or Anthropic (required for AI features)

# Clone the repository
git clone https://github.com/TechNatool/zenmarket-ai.git
cd zenmarket-ai

# Install dependencies
make setup
# or manually:
pip install -e ".[dev]"

# Copy and configure environment
cp .env.example .env
# Edit .env with your API keys

# Verify installation
make test

# Generate financial brief
python -m src.cli brief --symbols AAPL,MSFT,GOOGL

# Run trading simulator
python -m src.cli simulate --symbol AAPL --strategy conservative

# Run backtest
python -m src.cli backtest --symbol AAPL --start 2024-01-01 --end 2024-12-31

Full documentation available at: docs/

• Installation Guide
• Configuration
• Quick Start
• CLI Reference
• Contributing Guide
• Security Policy

# Install development dependencies
make setup

# Install pre-commit hooks
make install-hooks

make help          # Show all available commands
make fmt           # Format code (black + isort)
make lint          # Lint code (ruff)
make type          # Type check (mypy)
make test          # Run tests
make test-fast     # Run tests in parallel
make cov           # Generate coverage report
make audit         # Security audit (bandit + pip-audit)
make docstrings    # Check docstring coverage
make docs          # Build documentation
make docs-serve    # Serve docs locally
make precommit     # Run all checks (recommended before commit)
make ci            # Run all CI checks
make clean         # Clean build artifacts

# Format code
make fmt

# Run all checks (recommended before commit)
make precommit

# Individual checks
make lint       # Linting
make type       # Type checking
make test       # Tests
make audit      # Security

zenmarket-ai/
├── src/                    # Source code
│   ├── advisor/           # Trading signals and indicators
│   ├── cli/               # Command-line interface
│   ├── core/              # Core analysis engine
│   ├── execution/         # Trading execution and simulation
│   ├── reports/           # Report generation
│   └── utils/             # Utilities
├── tests/                 # Test suite
│   ├── execution/         # Execution module tests
│   └── ...
├── docs/                  # Documentation
├── reports/               # Generated reports
├── data/                  # Data files
├── pyproject.toml         # Project configuration
├── Makefile               # Development commands
├── .pre-commit-config.yaml # Pre-commit hooks
└── README.md              # This file

# Run all tests
make test

# Run with coverage
make cov

# Run unit tests only
make test-unit

# Run integration tests only
make test-integration

# Run fast (parallel)
make test-fast

Current coverage: ≥90% (lines and branches)

View coverage report:

make cov
# Open htmlcov/index.html in browser

ZenMarket AI is designed for educational and research purposes. It operates in paper trading mode by default.

• API key protection via .env
• Circuit breakers and risk limits
• Input validation with Pydantic
• Regular security audits (bandit, pip-audit)
• Automated dependency updates (Renovate)

Please report security vulnerabilities to: security@technatool.com

See SECURITY.md for details.

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run quality checks: make precommit
5. Commit with conventional commits
6. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.

ZenMarket AI tracks comprehensive performance metrics:

• Returns: CAGR, Total Return
• Risk-Adjusted: Sharpe Ratio, Sortino Ratio, Calmar Ratio
• Risk: Maximum Drawdown, Volatility, VaR, CVaR
• Trading: Win Rate, Profit Factor, Expectancy, Average Win/Loss
• Activity: Number of Trades, Trade Frequency, Holding Period

graph TB
    A[Market Data] -->|yfinance| B[Data Collection]
    C[News Sources] -->|feedparser| B
    B --> D[Analysis Engine]
    D --> E[AI Processing]
    E -->|OpenAI/Anthropic| F[Insights]
    D --> G[Technical Indicators]
    G --> H[Signal Generator]
    H --> I[Execution Engine]
    I --> J[Risk Manager]
    J --> K[Broker Simulator]
    K --> L[Position Manager]
    L --> M[Performance Tracker]
    M --> N[Reports & Visualization]

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

• 📧 Email: contact@technatool.com
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 🔒 Security: security@technatool.com

• Built with OpenAI GPT-4 and Anthropic Claude
• Market data from yfinance
• Technical analysis with pandas and numpy
• Visualization with matplotlib and seaborn

This software is for educational and research purposes only. It is not financial advice.

• Trading involves substantial risk of loss
• Past performance does not guarantee future results
• Paper trading results may not reflect real trading
• Always conduct thorough testing before live trading
• Consult with financial professionals before making investment decisions

Use at your own risk.