RL-Exec: Impact-Aware Reinforcement Learning for Opportunistic Optimal Liquidation, Outperforms TWAP and a Book-Liquidity VWAP on BTC-USD Replays
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 0 of its 6 references that are in the map. First posted on arXiv 30 Oct 2025.
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