rl-execution
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 |
| Temporary impact | Consuming liquidity | Scales with trade rate |
| Permanent impact | Information leakage | Scales with total size |
| Timing risk | Price drifts while waiting | Scales with volatility and time |
A 100 SOL market buy on a thin pool can move the price 2-5%. Splitting it into
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