Python Backtesting: Developed Strategies for Investors

Developing Python backtesting skills helps with strategy evaluation, providing valuable information you can use to improve as an algorithmic trader.

Backtesting serves a critical role in developing a trading strategy. Although past results don’t guarantee future returns, extensive backtesting helps you understand how your model will perform in a live environment.

As a trader, you want the best tools for the job. This applies especially to backtesting, which explains why many traders prefer to do so in Python. The programming language’s simplicity, robust data processing tools, and flexibility make Python backtesting ideal for algorithmic trading strategies.

Let’s explore backtesting Python trading strategies, including how backtesting works and why Python excels at the process. We’ll also compare seven popular Python backtest libraries and the crucial features you should look for when selecting a Python framework. Finally, we’ll discuss how Composer’s no-code editor and generative AI tool can facilitate algorithmic trading.

What is backtesting in trading?

Backtesting involves testing a strategy or model using historical data. Using historical data lets you assess a strategy’s viability without financial risk. You can refine your approach based on the backtest results, updating target parameters, weights, assets, and other controllable variables to strike an appropriate risk/reward ratio.

According to backtesting theory, a strategy that performed well in the past may work well in the future. However, past performance does not guarantee future results. Conversely, a strategy that failed in the past may perform poorly under similar future conditions. Depending on these results, traders can adopt, update, or reject a trading strategy.

Imagine you want to create a momentum trading algorithm in Python. For this task, you must choose a Python library, import any necessary financial data, and define your strategy. You can then backtest and optimize, fixing syntax errors, streamlining your code, and addressing underfitting or overfitting. During this process, you find what works and what doesn’t, helping you limit risk and avoid financial losses.

Why is Python good for backtesting?

Whether you want to create a momentum trading algorithm or a simple moving average (SMA) stock trading bot, Python has all the tools you need to do the job. Python is great for backtesting thanks to the following features:

Flexibility and versatility: As an interpreted language with dynamic typing, you don’t need to specify Python variable types, making the code highly adaptable. Other programming languages like R interact well with Python, meaning you can always run your trading code in another language and backtest in Python.
Efficient data handling: Python excels at processing vast numerical datasets. This statistical prowess makes Python ideal for handling the financial data used in backtesting.
Access to diverse libraries: You can choose between various algorithmic trading Python libraries for backtesting. This diversity guarantees traders find a suitable library based on their experience, skills, and motivation.
Simplicity: Python ranks among the simplest coding languages. Its simplicity makes learning Python easier than other programming languages regularly used in algo trading.

Python backtest libraries

Python libraries contain prewritten code algo traders can use for various tasks. Rather than writing code from scratch, you can use code from a library to complete specific trading operations.

Most Python libraries run as open-source software managed by volunteers. Once you import the library, you can access everything inside for free. Some popular Python libraries used in algorithmic trading include:

pandas

First developed in 2008, pandas is an open-source data analysis and manipulation tool. Highlights include fast and efficient data manipulation, intelligent data alignment and labeling, time series functionality, and flexible dataset handling.

With pandas, you can import real-time data from sources such as Yahoo Finance or OpenBB, define trading strategies, and calculate performance. As a NumFOCUS-sponsored project, pandas enjoys significant support and development from its user base.

NumPy

NumPy is a Python library designed for scientific computing. It features diverse numerical computing tools, powerful arrays, and accessible syntax for beginner programmers. NumPy provides a fast and straightforward coding experience by combining Python with compiled codes like C. Distributed under a permissive BSD license, NumPy has an active community and is publicly maintained on GitHub.

Backtrader

Backtrader is a feature-rich, event-driven Python library focused on reusable trading strategies, indicators, and performance analyzers. Licensed under the GPL v3.0 license, Backtrader works with pandas DataFrames and offers built-in live data-feed support for Yahoo Finance, Interactive Brokers, and Oanda. Its active community, substantial documentation, and numerous tutorials and guides make it an excellent learning resource for backtesting.

PyAlgoTrade

PyAlgoTrade is an event-driven backtesting library licensed under Apache 2.0. This thoroughly documented framework works with Yahoo Finance, Google Finance, and Quandl, can handle real-time Twitter events, and integrates well with NumPy, SciPy, and pandas. It supports live and paper Bitcoin trading through Bitstamp and is scalable, allowing you to use one or more computers when backtesting a strategy.

VectorBT

VectorBT may rank as the fastest Python backtesting library. It operates solely on pandas and NumPy objects and leverages Numba for data analysis, allowing it to structure complex data and make superfast computations.

Unlike most backtesting libraries, VectorBT supports recursive features such as trailing stop-loss orders. Although it uses relatively challenging syntax, VectorBT provides users with numerous learning guides and tutorials.

Backtesting.py

If VectorBT is the fastest Python library, Backtesting.py is arguably the easiest. Backtesting.py supports multiple time frames, composable strategies, interactive visualization, and vectorized and event-based backtesting. It features an intuitive structure, an active community, well-developed documentation, and various tutorials and guides.

Although Backtesting.py can’t backtest portfolio rebalancing strategies, it accepts historical candlestick data for any asset, including stocks, forex, crypto, and futures.

Zipline

Initially developed by Quantopian, Zipline is a Python backtesting library with paper and live trading capabilities. It comes with 10 years of historical stock data and supports various options for importing data. In addition to working well with the pandas DataFrame and machine learning tools like scikit-learn, Zipline offers numerous prebuilt algorithms. Although Zipline shut down in 2020, you can still access the library on GitHub.

Key features in Python frameworks

Before picking a Python library, you should assess its algorithmic trading capabilities. Some features you should look for include the following:

Data handling capabilities: Data processing is one of the primary features you should consider in a Python framework. Prioritize frameworks that can execute large datasets quickly and accurately.
Strategy development and implementation: Developing a trading strategy involves finding the optimal path to achieve your result. While some frameworks offer pre-made strategies ready for implementation, others may require optimization. In this case, consider frameworks that support distributed processing.
Coding simplicity and integration: A straightforward and intuitive coding structure enhances the readability and maintainability of the codebase. The library should seamlessly integrate with other financial tools and data sources to ensure a cohesive workflow that enables efficient strategy implementation and analysis.

Simplify your trading with Composer

Python backtesting is complicated, which is why it’s crucial to pick a top algorithmic trading platform that streamlines the process. Composer simplifies backtesting, smoothing the technical intricacies and making it accessible for beginner traders.

With its no-code editor, Composer does away with coding complexity, giving you more time to optimize your trading strategy.