A finance quant by day that writes about Julia and other things at night. You can read my ramblings at https://dm13450.github.io/
This talk will demonstrate the use of the MLJ.jl package in building a machine-learning pipeline for a dataset of property loans. The goal is to predict which loans might default and build a strategy to minimize losses. Several machine learning models such as ElasticNet, XGBoost, and KNN will be explored, and then combined into a stacked model. I will also show how the output of these models can be used to drive investment decisions and the final results of the strategy. This talk will provide a
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.