Amplify
The agentic FinTech platform for quantitative finance.
“Risk comes from not knowing what you're doing.”
Amplify enhances your existing portfolio management workflows with powerful insights. To keep you focused on the work that matters, our agents take all the context they need from the formats you already use - CSV, Excel, PDF and many more.
Noise Filtering
Filter market noise from financial data using Random Matrix Theory to identify truly significant market signals.
Efficient Frontier Analysis
Compare investment strategies across different time periods to understand how market conditions affect optimal portfolios.
Portfolio Optimization
Create optimized investment portfolios using advanced mathematical techniques to maximize returns while minimizing risk.
Random Matrix Theory (RMT) has a rich and surprising history. First developed in the early 20th century to study population dynamics, in the 1950s, RMT found new life in atomic physics, used by physicists like Wigner to understand the statistical structure of energy levels in heavy nuclei. By the 1970s, it appeared in number theory. It is an incredibly deep and interconnected mathematical field, straddling statistics, quantum mechanics, and pure number theory.
Unsurprisingly, RMT has found its way into quantitative finance in recent years. Sophisticated hedge funds like Capital Fund Management (CFM) have pioneering its tools the in portfolio optimization. It can be used to construct null models to distinguish meaningful signal from random noise in financial datasets in portfolio optimization. But the core mathematics remains elusive, difficult to interpret and harder to implement. And that’s a problem: in finance, decisions need to be explainable. Investors, analysts, and managers must understand the tools they use and ultimately be held accountable for the results.
Amplify solves this by doing the hard math for you. We use context-aware agentic AI to take your data in the formats you love (CSV, Excel, etc.) and apply cutting-edge theories such as RMT and explaining results in plain, transparent language. The result? More stable portfolios. Less overfitting. And the confidence to add more data without getting overwhelmed by the noise.