DBTB INT Martin Froehler
summary
Claims machine learning works on financial data and links that claim to a quantitative finance pedigree and examples connected to Quantiacs. Describes concrete practice methods: backtesting simple strategies, writing production code, and iterating on data-driven models rather than relying on theory alone. Emphasizes numbers from experiments and hands‑on results rather than vague promises, and urges practice by coding and working with data to get reproducible outcomes. Highlights quantitative finance background as a useful foundation.
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