My current research focus on modeling market microstructure model using traditional and deep neural point process. The goal is to build a probabilistic generative model that takes variable-length sequences in some time interval and outputs attention-weighted representations. These latent representations will then be aggregated to summarize market impact functions.
An introductory slide is made to give an intense briefing about Variational AutoEncoding, an important method that is used in our project
Recent TPP and NeuralTPP research topics are reviewed and an extensive Temporal Point Process framework is implemented based on this paper. I also made a slide to give you a sense of general framework in modeling irregularly spaced event-associated time series data.
My most recent internship is to collaborate with Nodejs engineers to develop arbitrage strategies in algorithmic and high-frequency trading.
Advance team understandings of cutting-edge research and white papers in Decentralized Finance and Web3 Project
Use data-driven approach with Geometric Brownian Motion model to find near-optimal call-put strangle that hedges against impermanent losses