The AotE Toolkit
A few readers have asked about the tools we use for modeling and backtesting. Python is the backbone of our work, and most of our tools are free and open-source. Here is a quick overview with links for the primary tools in our toolbox.
Foundations
pandas: Pandas is indispensable due to its powerful data manipulation and analysis capabilities. It allows traders to easily handle time series data, perform complex data transformations, and conduct exploratory data analysis. Its DataFrame structure is particularly suited for organizing and manipulating financial data, making it central to our work.
numpy: Numpy is fundamental for high-performance mathematical computations. It provides support for large, multi-dimensional arrays and matrices, along with a vast collection of mathematical functions to operate on these arrays. For algorithmic trading, Numpy accelerates calculations for statistical analysis and financial modeling, enabling efficient data processing and performance optimizations.
Calculations
VectorBT Pro: VectorBT Pro is a powerful backtesting library that leverages pandas and numpy to offer lightning-fast analysis of trading strategies over large datasets. It simplifies the process of strategy development, optimization, and evaluation, providing comprehensive tools for portfolio analytics, signal generation, and performance metrics. Its scalability and flexibility make it a powerhouse for developing and backtesting strategies
PyTorch: PyTorch is a deep learning framework that excels in flexibility and speed. In trading, PyTorch is used for developing sophisticated models like neural networks to predict market movements, optimize portfolio allocations, or generate trading signals based on complex patterns in financial data. Its dynamic computation graph enables rapid experimentation and iteration.
scikit-learn: Scikit-learn is a versatile machine learning library that provides a wide array of algorithms for classification, regression, clustering, and dimensionality reduction. It's useful for algorithmic trading because it can help identify patterns and relationships in financial data, enhance predictive models, and facilitate feature selection and strategy optimization with its easy-to-use interface and robust preprocessing tools.
Numba: Numba is a just-in-time compiler for Python that accelerates computational performance. It's especially useful in algorithmic trading for speeding up loop-heavy and data-intensive calculations, such as those found in backtesting and numerical analysis. By converting Python functions to optimized machine code, Numba enables incredible speed boosts for testing and simulation.
Visualizations
Plotly: Plotly is a graphing library that enables the creation of interactive, publication-quality graphs and dashboards. Plotly offers a dynamic way to visualize financial data, analyze trading strategy performance, and monitor markets in real-time. Its ability to generate detailed charts, including candlestick plots and performance heatmaps, is invaluable. Python has many visualization libraries, but Plotly is our go-to.