QuantEx

Welcome to QuantEx, a minimal yet powerful Python library for building, back-testing and documenting quantitative trading strategies.

QuantEx provides five key building blocks:

1. Market Data Sources (quantex.sources) – consistent interfaces for historical (back-testing) or live data feeds.
2. Core Data Models (quantex.models) – immutable market-data records and stateful helpers such as Position and Portfolio for tracking P&L.
3. Strategy Harness (quantex.strategy) – a thin, extendable base-class that ties data sources and portfolio management together.
4. Event Bus & Execution Simulator (quantex.engine, quantex.execution) – dispatches bars to the strategy, converts Orders into Fills, and keeps the portfolio in sync.
5. Backtest Runner (quantex.backtest) – one-stop helper that wires everything together and returns results/metrics ready for analysis.

The package is intentionally lightweight; it aims to give you just the right abstractions so you can focus on research rather than plumbing.

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Installation

git clone https://github.com/dangreen07/quantex.git
cd quantex
poetry install

(QuantEx is published for illustrative purposes only – adjust according to your own distribution method.)

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Quick-Start

Get up and running with QuantEx in less than five minutes.

1. Prepare Some Data

QuantEx does not bundle market data. For a first test, create a tiny CSV named prices.csv:

timestamp,open,high,low,close,volume
2024-01-01 09:30:00,100,101,99,100.5,1000
2024-01-01 09:31:00,100.5,101,100,100.7,500
2024-01-01 09:32:00,100.7,100.8,100.2,100.3,750
2024-01-01 09:33:00,100.3,100.5,99.8,99.9,1200
2024-01-01 09:34:00,99.9,100.2,99.7,100.1,900

2. Create a Strategy

from quantex.strategy import Strategy
from quantex.sources import CSVDataSource
from quantex.backtest import BacktestRunner

class BuyAndHold(Strategy):
    """Buys on the first bar and holds."""

    def run(self):
        if self.index == 0:
            self.buy("TEST", 100)

# --- Back-test Execution ---
if __name__ == "__main__":
    data_source = CSVDataSource("prices.csv", symbol="TEST")
    strategy = BuyAndHold(symbols=["TEST"], initial_cash=100_000)
    result = BacktestRunner(strategy, {"csv": data_source}).run()

    print("--- METRICS ---")
    print(f"Total Return: {result.metrics['total_return']:.2%}")
    print(f"Total Cash Return: {result.metrics['total_return']*100_000:.2f}")
    print("--- NAV ---")
    print(result.nav)

3. Run It

python my_strategy.py

You should see output similar to:

--- METRICS ---
Total Return: -0.04%
Total Cash Return: -40.00
--- NAV ---
2024-01-01 09:30:00    100000.0
2024-01-01 09:31:00    100020.0
2024-01-01 09:32:00     99980.0
2024-01-01 09:33:00     99940.0
2024-01-01 09:34:00     99960.0
Name: NAV, dtype: float64

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Next Steps

• Browse the Guides for tutorials and patterns.
• Dive into the API Reference for full docstrings.
• Contribute ideas or improvements on GitHub.

Happy trading 🐍💹

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