Data Quality and Provenance Framework

HRT has developed a framework for evaluating and acquiring datasets for algorithmic trading. This framework focuses on heuristics such as relevance, uniqueness, avoiding lookahead bias, and assessing sample size and noise. It also outlines sources of data, including market data and alternative data, and discusses the creation of new datasets through data transformations. The importance of robust engineering practices for handling large datasets in production is also highlighted. This post elaborates on the heuristics for evaluating data quality, specifically focusing on relevance, uniqueness, avoiding lookahead bias, and assessing sample size and noise. It also details the sources of data, including market data and alternative data, and discusses the creation of new datasets through data transformations.