Market making and execution
3 readings
Learn the trading state and execution trade-off before evaluating reinforcement-learning market makers.
- Who this is for
- Readers with probability, basic optimisation, and some Python simulation experience.
- What you will work towards
- Specify a market simulator, distinguish inventory risk from execution cost, and compare a learned policy with simple baselines.
Before you start
Use these SMQ lessons to check the ideas the readings assume. Lesson pages show the outline and how to access the full lesson.
- Order Books
Understand quotes, queue priority, and the difference between an order and a fill.
- Order Flow and Adverse Selection
Explain why a filled limit order can be bad news for a market maker.
Read in this order
Optimal Execution of Portfolio Transactions
Understand the cost–risk trade-off in executing an existing position.
Market Making via Reinforcement Learning
Move from a specified execution model to a learned quoting policy.
Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Ask how other dealers change the environment.
Continue your study on SMQ
- Optimal Execution: VWAP, TWAP, and Implementation Shortfall
Distinguish execution benchmarks from an execution policy and its costs.
- Order Flow and Adverse Selection
Explain why a filled limit order can be bad news for a market maker.
Follow the connected lessons, worked examples, and exercises in the app. Full lesson access depends on the topic and your membership.
Study market microstructure and execution