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Neural networks and other machine-learning systems are used to create automatic financial forecasting and trading systems. To aid comparison of such systems, there is a need for reliable performance metrics. One such metric that may be considered is the win rate. We show how in certain circumstances the win-rate statistic can be very misleading, and to counter this, we propose and define baseline win rates for comparison. We develop empirical and closed-form models for such baselines and validate them against financial data and a neural forecaster.
Original languageEnglish
Publication statusPublished - 19 Jul 2020
EventIEEE WCCI 2020 - Glasgow
Duration: 19 Jul 2020 → …


ConferenceIEEE WCCI 2020
Period19/07/20 → …

Cite this

Krause, A., & Fairbank, M. (2020). Baseline win rates for neural-network based trading algorithms. Paper presented at IEEE WCCI 2020 , Glasgow, .