{"ID":2848253,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07434","arxiv_id":"2511.07434","title":"RL-Exec: Impact-Aware Reinforcement Learning for Opportunistic Optimal Liquidation, Outperforms TWAP and a Book-Liquidity VWAP on BTC-USD Replays","abstract":"We study opportunistic optimal liquidation over fixed deadlines on BTC-USD limit-order books (LOB). We present RL-Exec, a PPO agent trained on historical replays augmented with endogenous transient impact (resilience), partial fills, maker/taker fees, and latency. The policy observes depth-20 LOB features plus microstructure indicators and acts under a sell-only inventory constraint to reach a residual target. Evaluation follows a strict time split (train: Jan-2020; test: Feb-2020) and a per-day protocol: for each test day we run ten independent start times and aggregate to a single daily score, avoiding pseudo-replication. We compare the agent to (i) TWAP and (ii) a VWAP-like baseline allocating using opposite-side order-book liquidity (top-20 levels), both executed on identical timestamps and costs. Statistical inference uses one-sided Wilcoxon signed-rank tests on daily RL-baseline differences with Benjamini-Hochberg FDR correction and bootstrap confidence intervals. On the Feb-2020 test set, RL-Exec significantly outperforms both baselines and the gap increases with the execution horizon (+2-3 bps at 30 min, +7-8 bps at 60 min, +23 bps at 120 min). Code: github.com/Giafferri/RL-Exec","short_abstract":"We study opportunistic optimal liquidation over fixed deadlines on BTC-USD limit-order books (LOB). We present RL-Exec, a PPO agent trained on historical replays augmented with endogenous transient impact (resilience), partial fills, maker/taker fees, and latency. The policy observes depth-20 LOB features plus microstr...","url_abs":"https://arxiv.org/abs/2511.07434","url_pdf":"https://arxiv.org/pdf/2511.07434v1","authors":"[\"Enzo Duflot\",\"Stanislas Robineau\"]","published":"2025-10-30T20:25:49Z","proceeding":"q-fin.ST","tasks":"[\"q-fin.ST\",\"cs.LG\",\"q-fin.TR\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}