Using multiple time series observations, split time into regimes using an array of representation learning techniques with explainability and robustness techniques for the transitions
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Updated
Aug 22, 2026 - Python
Using multiple time series observations, split time into regimes using an array of representation learning techniques with explainability and robustness techniques for the transitions
A regime-aware reinforcement learning workbench for synthetic trading research. It combines a hidden-regime market simulator, multiple agent baselines, a live terminal dashboard, and experiment tooling for ablations, OOD sweeps, and artifact-driven analysis.
Personal regime-aware trading dashboard, lots of tweaking left
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