rl-executionlisted

# 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 ten 10 SOL slices over a few minutes can cut that cost by 30-60%. The question is **how** to split optimally — and that is where execution algorithms and RL come in. ## The RL Framework for Execution ### State Space The agent observes at each decision step: ``` state = [ remaining_qty, # How much is left to trade (0-1 normalized) time_remaining, # Fraction of allowed horizon remaining current_price, # Current mid-price (normalized to arrival price) spread, # Current bid-ask spread volatility, # Recent realized volatility volume, # Recent trading volume (normalized) ] ``` ### Action Space Discrete actions controlling how m