Hire Zihang N. - AI Engineer - New York

* Infrastructure Optimization: Architected and optimized a distributed time-series database pipeline handling 5 GB daily high-frequency ticks, reducing query latency for backtesting by 17% using ClickHouse. * Factor development: Ingesting millisecond-level quotes and executions for 4,800+ equities. Developed and validated 1,200+ cross-sectional and microstructure factors, improving out-of-sample IC by 15% and cutting factor turnover by >20%. * Model Development: Engineered state-of-the-art predictive models(FactorVAE, GHMM) for alpha discovery, achieving 27% Sharpe ratio uplift and ensuring statistical robustness through Cross-Validation and backtesting.

1. Quantitative strategy research and development: Based on Python libraries such as Pandas and Numpy, we analyze the effectiveness of various trading strategies (based on candlestick patterns) targeting SSE 50, CSI 300, and CSI 500 constituents, such as double-bottom strategy. Using TensorFlow framework, CNN and LSTM models are built to mine the latent effective factors in K-line charts, significantly improving the effectiveness of the strategies, with an average annualized Alpha of 13-16% over the period of 2011-2021. 2. VAR Calculation: Automate the updating of fund managers' weekly investment performance data using libraries such as Python Pandas, Numpy, etc. Calculate VARs for over 2,500 investment products using historical simulation methods to assist the department in post-investment risk management. 3. Deployment of AI Agent: Introduced RAG and LLM and assisted in the development of departmental intelligences, which automatically update the “Market Trends” and “Financial Hot Spots” sections on a daily basis.