Cleaning Financial Data for Reliable Analysis and Trading Models
Summary
The article presents data cleaning as a necessary stage between acquiring raw data and analyzing it or training machine learning models. It explains tidy data structure, variable types, and the importance of preserving the original source data alongside a cleaned dataset, a codebook, and a record of processing steps. It also defines common operations such as parsing, merging, appending, imputation, deduplication, aggregation, and scaling.
Its workflow is to inspect the dataset and its variables, identify and investigate problems, clean them, then repeat exploratory checks to catch issues introduced by processing. Examples include missing values, duplicates, outliers, inconsistent strings, and malformed source data; the article connects erroneous price observations to misleading model results. The guidance favors reproducible processing that takes raw data as input and produces documented outputs. It is a broad overview rather than a detailed treatment of financial data pitfalls: specific rules depend on the source and task, and cleaning alone cannot correct biases such as survivorship or look-ahead bias.
Key ideas
- Inspect raw data, variable types, missing values, and structure before analysis or modeling.
- Keep untouched raw data, document variables in a codebook, and record processing steps for reproducibility.
- Cleaning operations include parsing, merging, imputation, deduplication, aggregation, and scaling.
- Repeat exploratory checks after cleaning to find remaining problems or issues introduced by processing.
- Erroneous prices and other data defects can distort trading research and machine learning results.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.