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Reinforcement learning for trade execution optimization including order splitting, adaptive timing, and impact minimization

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# RL Execution Optimization

Reinforcement learning (RL) for trade execution teaches an agent to split and
time large orders so that total market impact is minimized. Instead of following
a fixed schedule (TWAP, VWAP), an RL agent observes real-time market state and
adapts its trading rate on the fly.

## Why Execution Optimization Matters

Every trade has a cost beyond the quoted spread:

| Cost Component | Cause | Typical Magnitude |
|---|---|---|
| Spread cost | Crossing the bid-ask | 5-50 bps on DEXs |
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