RL for Trade Execution

Fixed execution schedules (TWAP, VWAP) ignore real-time market conditions. An RL agent adapts its execution rate based on order book state, reducing implementation shortfall.

The Problem

Executing a large order at a fixed rate creates predictable market impact. A 100K-share TWAP sell ignores favorable liquidity bursts and pushes through thin books. The result: 20-50 bps of avoidable shortfall on institutional orders, compounding across thousands of trades per year.

The Pattern

Model execution as a finite-horizon MDP. State: remaining shares, time left, volume, spread. Action: execution rate. Reward: negative implementation shortfall.

WRONG

# Static TWAP - ignores market conditions entirely
def twap_execute(total_shares: int, n_slices: int) -> list[int]:
    base = total_shares // n_slices
    remainder = total_shares % n_slices
    return [base + (1 if i < remainder else 0) for i in range(n_slices)]