AST investments refer to analyzing and processing code, specifically Python AST, to improve software used in financial analysis. This technique allows customizing Python functions for better performance across platforms and environments like Jupyter notebooks. The AST investments approach has great potential to enhance utility libraries used by investors and traders.
Applying AST investments to build robust trading tools
The AST investments technique can help construct reusable components for trading system development. For example, one can design a timeout decorator that works smoothly across Windows, Linux, and MacOS. This decorator can restart frozen processes or stop runaway tasks that overload memory and crash notebooks. Such robust utility functions are essential for deploying trading strategies safely in live markets.
In summary, AST investments involve hacking and modifying Python AST to overcome common issues faced when building financial applications. This allows creating resilient libraries for trading, modeling, and analytics.