A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management
We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior financial RL work: 1) ticker lock-in, 2) monolithic objectives , and 3) static user models. Phase 1 pretrains a ticker-identity-free cross asset encoder via self-supervised learning on a multi-asset corpus, augmented by a frozen parallel branch using Chronos, a T5-based time series foundation model, fused via a learned gating mechanism. To our knowledge, this is the first application of a time series foundation model to portfolio management RL. The encoder generalizes to any publicly traded asset via a 50-dimensional observable metadata vec...
| Authors | Ramin Pishehvar |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 22 July 2026 |
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