Quantitative#090

RL Execution Agent for Order Slicing

Instead of dumping a large order into a thin book and moving the price against yourself, train an AI agent (reinforcement learning — it learns by trial and error in simulation) to break the order into smaller pieces placed at smart times, beating a simple even-spread (TWAP/VWAP) benchmark. The edge isn't a market view at all; it's cutting trading costs on Polymarket's thin books, which adds measurable profit to whatever signal you're already trading.

What you need to run it

  • Market-replay simulator with realistic impact/fill model
  • RL training stack (env, reward = shortfall vs benchmark)
  • Live book state ingestion for inference
  • Guardrails/kill-switch on out-of-distribution book states

Where this applies

Markets on Polymarket where rl execution agent for order slicing is the natural play:

  • Will ETH close above $7k by Dec 31, 2026? (large position to accumulate)
  • Will the Democrats win the 2026 House?
  • Will Bitcoin be up at the end of today? (recurring, executed at size)

Capabilities this demands

Model / quantCustom code / APILow latencyRisk management

At a glance

CategoryQuantitative
Requirements4
CapabilitiesModel / quant, Custom code / API, Low latency, Risk management
VenuePolymarket (CLOB, Polygon)

Build it

Related quantitative strategies

This is documentation, not advice. Poly Research & Robotics publishes how these strategies work because the method should be checkable — not as a recommendation to trade them. See the full strategy database (147 strategies) or the data resources directory.
Join Discord