mlsanityData to 14 Oct 2026

FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management

arXiv 2510.02986 ↗First posted 3 Oct 2025q-fin.TR cs.LGcited by 0

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Abstract (arXiv)

Transaction costs and regime shifts are major reasons why paper portfolios fail in live trading. We introduce FR-LUX (Friction-aware, Regime-conditioned Learning under eXecution costs), a reinforcement learning framework that learns after-cost trading policies and remains robust across volatility-liquidity regimes. FR-LUX integrates three ingredients: (i) a microstructure-consistent execution model combining proportional and impact costs, directly embedded in the reward; (ii) a trade-space trust region that constrains changes in inventory flow rather than logits, yielding stable low-turnover updates; and (iii) explicit regime conditioning so the policy specializes to LL/LH/HL/HH states without fragmenting the data. On a 4 x 5 grid of regimes and cost levels with multiple random seeds, FR-LUX achieves the top average Sharpe ratio with narrow bootstrap confidence intervals, maintains a flatter cost-performance slope than strong baselines, and attains superior risk-return efficiency for a given turnover budget. Pairwise scenario-level improvements are strictly positive and remain statistically significant after multiple-testing corrections. We provide formal guarantees on optimality under convex frictions, monotonic improvement under a KL trust region, long-run turnover bounds and induced inaction bands due to proportional costs, positive value advantage for regime-conditioned policies, and robustness to cost misspecification. The methodology is implementable: costs are calibrated from standard liquidity proxies, scenario-level inference avoids pseudo-replication, and all figures and tables are reproducible from released artifacts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 51 references that are in the map. First posted on arXiv 3 Oct 2025.

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11 of 51 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  5. 2017Constrained Policy Optimizationcited by 983
  6. 2015Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approachcited by 170
  7. 2014Algorithms for CVaR Optimization in MDPscited by 99
  8. 2013Projection onto the probability simplex: An efficient algorithm with a simple proof, and an applicationcited by 67
  9. 2022Efficient Risk-Averse Reinforcement Learningcited by 43
  10. 2019Deep Learning in Asset Pricingcited by 41
  11. 2024A Review of Reinforcement Learning in Financial Applicationscited by 13

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