Persistent, Data-Aware Caching for Financial Machine Learning Workflows
Summary
This article presents a caching architecture for speeding up repeated financial machine-learning computations during feature development, parameter searches, and backtests. It explains why a basic in-memory cache is insufficient for Pandas and NumPy data: these objects are not directly hashable, results vanish between sessions, and code changes can leave stale outputs. The proposed design uses disk persistence, custom keys that account for dataset structure and time ranges, and function source tracking to invalidate affected cached results when implementations change.
The examples contrast generic caching failures with persistent caching and show mechanisms for hashing data and tracking function revisions. The article also describes broader workflow components such as backtest-result caching, performance monitoring, experiment tracking, and a Python-to-MQL5 bridge. Its claims about faster iteration are presented as motivation and design goals, not as independently documented benchmark evidence. Sampled content hashing for large datasets can miss unsampled changes, so correctness depends on the key design and the data used.
Key ideas
- Disk-backed caching preserves computation results between Python sessions.
- Custom cache keys can account for data structure, content, and temporal indexes.
- Function source tracking can invalidate cached outputs after relevant code changes.
- Caching can support parameter comparisons and machine-learning research workflows.
- Sampling large datasets for hashes may fail to detect changes outside sampled rows.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.