Validitybase

validityBase was sparked by a critical question: if I build an algorithm that makes daily buy/sell predictions, and six months later its track record looks amazing, how do I prove those were the actual predictions my algorithm made at the time? And, without a trustworthy way to verify that history, how can I expect anyone to believe me, let alone invest in it? This problem isn’t limited to trading, it’s everywhere in financial markets. The lack of verifiable historical data undermines the value of everything from investment strategies and risk models to research, ESG scores, and AI outputs. It affects data vendors, investment analysts, hedge funds, alt data providers, model builders, and anyone else whose business relies on the predictive power of their data. If you can't prove what you knew when, you're just asking people to take your word for it — and that doesn’t scale. Existing solutions, like emailing predictions to potential consumers, long data trials, or launching and auditing a fund, index, or ETF, are expensive, clunky, and don’t scale. Worse, they’re impractical for most valuable forms of predictive data. validityBase solves this problem at scale with lightweight, easy-to-use tools for generating, publishing, and verifying validation metadata. Our system creates tamper-proof, third-party verifiable records on public blockchain infrastructure, making historical predictive data independently auditable, with verifiable guarantees of the data's timestamps, authorship and completeness. Portfolio managers and quant researchers use validityBase to timestamp signals and build 3rd party verified trading track records. Others use it to publish live backtests, freeze model outputs before deployment, or create immutable audit trails for compliance and investor transparency. If you're involved in building investment track records, signals, predictive models, financial research, or anything related, please reach out.