Danxuan Han

Designed and operated a 100TB+ tick-level market data pipeline across AWS, SFTP, and on-prem NAS, organizing ingestion by trading date, exchange, and security. Built parallel Python ingestion and Parquet workflows with performance profiling and concurrency tuning. Implemented checkpointing, validation, and selective reprocessing for reliable historical migrations, plus daily incremental ingestion with monitoring notifications. Developed alternative-data ETL into partitioned Parquet and loaded results into BigQuery, supporting downstream querying and analytics.

Built a large Python pricing and diagnostics library for fixed-income instruments, covering thousands of bonds and complex structures. Implemented pricing-related analytics and error-sensitive yield computations used across teams in the U.S. and U.K. Developed Python analytics for Rates Treasury PnL attribution to support profitability analysis across hedging and trading flows.

Developed a portfolio selection approach for stock hedging, using regression-based methods to match a benchmark input. Created supporting data visualization and table formatting utilities to streamline trading analysis reporting. Worked on risk and execution-focused analysis tasks under a portfolio trading workflow.

Built a C++ pipeline to automatically collect Russell 1000 stock data from Yahoo Finance via libcurl. Processed EPS-related inputs and computed event-study statistics using bootstrap resampling. Used gnuplot to generate and present results, focusing on reducing total runtime from data collection to visualization.