Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance

By Adir Saly-Kaufmann, Kieran Wood, Jan Peter-Calliess, Stefan Zohren

Published 2026-03-02

Everscope rating

1745.8

Relevance to quantitative trading

10 / 10

Implementation complexity

7 / 10

Reproducibility

3 / 5

About this paper

Methodology: End-to-End Sharpe Ratio Optimization with Rolling Window Backtesting. Problem types: Time Series Forecasting, Portfolio Optimization, Risk Management, Algorithmic Trading, Sequence Modeling.

arXiv:2603.01820 · Paper rankings

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