Simulating RFQ Trading in Julia

This talk will explore using Julia to simulate the Request for Quote (RFQ) trading method. RFQ is a trading method that puts counterparties in competition by asking banks for prices to buy or sell an asset. I will simulate the Executing in an Aggregator model (Oomen 2017) and demonstrate why Julia's high performance and ease of use make it a perfect choice for simulating this type of trading. I'll finally show how we can learn from these simulations, educate clients and guide pricing strategies.

The The 'Request for Quote' (RFQ) is a trading method that puts counterparties in competition. You will ask banks for the price that they would buy or sell an amount of some asset. For example, you might ask three banks for their price to buy or sell 1 million Apple shares and you trade with whoever gives you the best price. The more banks you ask, the better price you get, but you are showing many people your interest, so leaking information and once you trade the price moves against you.

In this talk, I'll show you how you can simulate this problem in Julia and build a framework of RFQ trading using the popular Executing in an Aggregator Model (Oomen 2017). Julia provides the best experience from converting the equations in the paper to working code and the speed both in building and using the simulator.

I'll use the framework to show how the price does improve when you ask more counterparties, but at the cost of adverse market movements post trade. I'll then illustrate how we use these simulations to guide our thought process when designing quoting strategies and also using the results to help educate clients.