advanced-math-trading/execution-control

Execution optimization uses Almgren–Chriss dynamic programming to plan order schedules that minimize market impact and latency costs. This Skill applies control-theory framing and queueing dynamics to generate deterministic execution schedules tailored to your order size and time horizon.

Almgren–Chriss is an optimal-execution framework that balances execution urgency against market-impact costs. Use it when trading large orders where splitting execution across time reduces slippage more than delay costs accumulate, accounting for exchange microstructure and real-time feedback.

Yes. This Skill frames execution as a feedback control problem using MPC and queueing theory to adapt schedules to latency and order-book dynamics in real time, enabling closed-loop optimization beyond static Almgren–Chriss schedules.

This Skill covers fill modeling and latency-aware execution strategies using queueing theory and exchange microstructure analysis. It provides code snippets and references for estimating partial fills, queue positions, and execution delays in live trading.

Execution control requires numpy for numerical optimization, pandas for order and fill data handling, scipy for dynamic programming solvers, and matplotlib for visualizing execution schedules and latency trade-offs.

This Skill covers end-to-end deployment from DP scheduling through real-time feedback control. It includes control-theory and queueing toolchains designed for live trading, though implementation into production systems requires integration with your order-routing and risk infrastructure.