quantix-trading 0.2.0

Fast and intuitive backtesting for Python

Quantix

Fast and intuitive backtesting for Python

Installation • Quick Start • Documentation • Community

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🚀 What is Quantix?

Quantix is a Python backtesting library that combines the speed of VectorBT with the simplicity of modern Python frameworks. No more choosing between performance and usability!

import quantix as qx
from datetime import date

# This is all you need for a complete backtest
data = qx.load("AAPL", start=date(2020, 1, 1))

@qx.strategy
def my_strategy(bar):
    if bar.rsi < 30:
        return qx.buy()
    elif bar.rsi > 70:
        return qx.sell()
    return qx.hold()

result = qx.backtest(data, my_strategy, cash=10000)
result.plot()  # Beautiful interactive charts

📈 Features

• Lightning Fast: Matches VectorBT's performance with 10x better API
• Intuitive API: Write strategies in plain Python - no PhD required
• Built-in Indicators: 50+ indicators included, easy to add custom ones
• Beautiful Visualizations: Interactive plots that actually make sense
• Risk Management: Built-in stop-loss, take-profit, and position sizing
• Multiple Assets: Backtest portfolios with ease

📦 Installation

pip install quantix-trading

🎯 Quick Start

Simple Moving Average Strategy

import quantix as qx
from datetime import date

# Load data
data = qx.load("AAPL", start=date(2020, 1, 1), end=date(2023, 12, 31))

# Define strategy
@qx.strategy(stop_loss=0.02, take_profit=0.05)
def sma_strategy(bar):
    # Clean access to indicators
    if bar.close > bar.sma(50) and bar.close_prev <= bar.sma_prev(50):
        return qx.buy()
    elif bar.close < bar.sma(20) and bar.close_prev >= bar.sma_prev(20):
        return qx.sell()
    return qx.hold()

# Run backtest
result = qx.backtest(
    data=data,
    strategy=sma_strategy,
    cash=10000,
    commission=0.001
)

# Analyze results
print(f"Total Return: {result.total_return:.2%}")
print(f"Sharpe Ratio: {result.sharpe_ratio:.2f}")
print(f"Max Drawdown: {result.max_drawdown:.2%}")

# Visualize
result.plot()

🛠️ Status

⚠️ Early Development: We're building this in public! Star the repo to follow our progress.

Roadmap

• Week 1: Project setup and basic structure
• Week 2: Data management and caching
• Week 3: Strategy engine and backtesting
• Week 4: Technical indicators
• Month 2: Visualization and optimization
• Month 3: Community and advanced features

🤝 Contributing

We'd love your help! Check out our Contributing Guide to get started.

# Clone the repo
git clone https://github.com/quantix-io/quantix.git
cd quantix

# Install with poetry
poetry install

# Run tests
poetry run pytest

# Run linting
poetry run ruff check .
poetry run ruff format .

💬 Community

• 🌟 Star the repo to support the project
• 💬 Join our Discord for discussions
• 🐛 Report issues
• 📧 Subscribe to updates

📄 License

MIT License - see LICENSE for details.

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Built with ❤️ by traders, for traders