Logistic‑Normal Actor‑Critic für optimale Trade‑Ausführung in einem realistischen Limit‑Order‑Book‑Simulator (Noise/Tactical/Strategic); PyTorch‑Training inkl. TWAP/SL‑Baselines & Evaluation.
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Updated
Sep 30, 2025 - Python
Logistic‑Normal Actor‑Critic für optimale Trade‑Ausführung in einem realistischen Limit‑Order‑Book‑Simulator (Noise/Tactical/Strategic); PyTorch‑Training inkl. TWAP/SL‑Baselines & Evaluation.
Reinforcement Learning for Optimal Trade Execution
Literature survey of order execution strategies implemented in python
We consider the execution of portfolio transactions with the aim of minimizing a combination of risk and transaction costs arising from permanent and temporary market impact.
This is for the capstone project "Optimal Execution of a VWAP order".
Reinforcement learning environment for optimal trade execution — Gymnasium + Stable-Baselines3 + Almgren-Chriss market impact model
Optimal trade execution using Deep Q-Networks (DQN) and PyTorch. Simulates an Almgren-Chriss market environment to outperform TWAP benchmarks.
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