Data Science
Many traders use brokerage statements to create a verifiable track record. Unfortunately they are not credible for this purpose. A better alternative exists.
Some analysts spend months building backtests that no-one is willing to trust. Learn why, and what to do about it.
The data marketplace suffers from severe and costly information asymmetries. These reduce the quality and value of available data.
Financial analysis will often yield incorrect results if the underlying data is not point-in-time.
Ensuring integrity and trust: learn how blockchains can and should be used for data validation, data integrity and data provenance systems.
Knowing where data comes from is critical to understanding the data. This is particularly true for financial data due to its time sensitive nature.