How do I use quant-trading scripts with my own data?

Modify the data loading functions in the scripts to read your CSV or API data; most assume a pandas DataFrame with OHLC columns. Ensure your data matches the expected format for accurate backtesting.

Is quant-trading better than backtrader for backtesting?

Quant-trading focuses on specific strategy implementations with explanations, while backtrader is a full-featured framework with more built-in tools. For learning strategy logic, quant-trading is good, but for structured backtesting, backtrader might be better.

Can I use this for live trading?

No, it's designed for historical data backtesting only. For live trading, you'd need to integrate with broker APIs, handle real-time data, and add risk management features not provided here.

How accurate are the backtesting results?

Results are optimistic due to frictionless assumptions; real-world performance would be lower with costs and slippage. Use it as a starting point, but validate with more realistic simulations.

What Python libraries are needed to run these scripts?

Common libraries like pandas, numpy, and matplotlib are required. Some scripts may need additional packages for data sources (e.g., yfinance) or calculations, so check import statements.

Does quant-trading support options trading?

Yes, it includes options strategies like straddle and a VIX calculator, but these are backtest-focused and may require custom data for options prices and Greeks.

Open-Awesome

quant-trading

Apache-2.0Python

A collection of Python scripts for backtesting quantitative trading strategies, including technical indicators, options strategies, and quantamental analysis.

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10.7k stars1.9k forks0 contributors

What is quant-trading?

Quant-trading is a Python-based repository for developing and backtesting quantitative trading strategies. It provides a collection of scripts covering technical indicators, options strategies, and statistical arbitrage, helping traders and researchers evaluate algorithmic approaches without expensive proprietary platforms. The project focuses on historical data backtesting with assumptions of frictionless trading.

Target Audience

Algorithmic traders, quantitative researchers, and finance students who want to experiment with trading strategies using Python. It's suitable for those looking to understand or implement common technical indicators, options strategies, or statistical arbitrage techniques.

Value Proposition

It offers a comprehensive, open-source toolkit for quantitative trading strategy development, combining well-documented implementations of popular strategies with unique quantamental research projects. Unlike commercial platforms, it provides full transparency and customization while being accessible to Python developers.

Overview

Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

Use Cases

Best For

Backtesting common technical indicators like MACD and RSI
Implementing statistical arbitrage with pair trading strategies
Developing options trading strategies such as straddles
Researching quantamental analysis projects like Monte Carlo simulations
Exploring intraday breakout strategies like London Breakout
Studying portfolio optimization and agricultural market modeling

Not Ideal For

High-frequency trading (HFT) strategies requiring low-latency execution and expensive market data
Production trading systems needing real-time execution with robust risk management and slippage modeling
Teams that demand comprehensive documentation, support, and maintenance for enterprise use
Projects where accurate simulation of transaction costs, illiquidity, and market frictions is critical

Pros & Cons

Pros

Broad Strategy Library

Covers a wide range from common technical indicators like MACD and RSI to options strategies and statistical arbitrage, providing a one-stop resource for diverse trading approaches.

Practical Backtesting Setup

Each script includes a main function for easy integration into trading systems, allowing straightforward historical data backtesting with minimal setup.

Educational and Transparent

Strategies are explained with references to sources like TradingView and Investopedia, making it accessible for learning quantitative trading concepts without black-box implementations.

Open-Source Flexibility

Offers full code transparency and customization, unlike proprietary platforms, enabling users to modify and extend strategies to fit specific needs.

Cons

Unrealistic Trading Assumptions

Assumes frictionless trades with no slippage, transaction costs, or illiquidity, which oversimplifies real market conditions and can lead to inflated backtest results.

Sparse Documentation

Author admits being 'too lazy to write docstring,' making code harder to understand, extend, or integrate for new users without diving deep into the scripts.

Complex Data Integration

Relies on varied data sources like Bloomberg/Eikon and web scraping, requiring additional setup, proprietary access, or custom adaptations for consistent data feeds.

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