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 (297 strategies) or the data feeds directory.