Summer Quantitative Analyst (R&D)
- Designed new energy-based machine learning model for time series prediction, which improved the old baseline model by 20% in terms of negative log-likelihood- Built LSTM model with attention in J language to enhance return on a $300M algorithmic fund- Vectorized existing neural network models by J language and shortened training time by 50% of an algorithm running on Kdb and Jd database- Implemented variations of LSTM, RNN, VAR models in J and Python to facilitate algo research- Developed a web scrapper in Ruby to collect stock price data and calculate momentum indicators