trade-execution
3 free lessons tagged trade-execution across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
The Simulator Problem: Why a Backtest Cannot Answer This
Reinforcement learning needs an environment that responds to the agent, and historical market data is a fixed recording that does not. This lesson shows that replaying the same day gives a fill rate anywhere from 38 to 73 percent depending on an assumption the data cannot settle, and covers what to do about it.
Reward Design: Where Execution Agents Go Wrong
An agent optimises the reward you wrote, not the objective you meant, and in execution the gap between those is unusually easy to open. This lesson computes how a mis-sized penalty makes leaving part of the order unexecuted rationally optimal, and covers the shaping that provably does not change the policy.
Framing Execution as a Markov Decision Process
A static execution schedule is an open-loop policy: it commits to a plan before seeing anything. This lesson formulates execution as an MDP so the plan can react, and computes the ceiling on what any adaptive policy could win, which turns out to collapse as market impact grows.