SoftwareComplete
A reinforcement-learning execution agent, graded against Almgren–Chriss closed forms. The agent works a parent order in a simulated market; an independently derived oracle defines what optimal is, and every threshold is written down before training starts. Under linear impact it rediscovers the analytic schedule to four decimal places. Under a 0.6-power impact law — where the closed form is no longer solving the right problem — it captures 99.4% of the available difference against a certified optimum. Given an invented price signal explaining one part in ten thousand of the next bin’s return variance, it captures 93% of the available advantage while paying 1.05× the optimum’s own execution premium. The same agent then worked a real order on a live matching engine over a wire, as that venue’s third independent client, with every fill predicted in closed form before anything was sent. Python, PyTorch, single-file PPO, CPU only.
PythonPyTorchPPOReinforcement LearningOptimal ExecutionAlmgren–ChrissMarket Impact+4