Optimization of market making strategies to enhance trading outcomes and manage risk (Python, SQL, Wind): - Performed statistical/ quantitative analysis to recommend improvements for trading and hedging strategies- Developed a strategy backtesting framework from scratch to facilitate daily backtesting- Streamlined trading strategy improvement process- Engaged with potential suppliers and explored methods to evaluate the performance and reliability of their productsDeveloped a Value at Risk (VaR) calculator to enable portfolio monitoringDelivered equity research reports with quantitative assessment of key components

Buy-side investment style analysis using statistical methods for data-driven decision making (Python, SQL, JavaScript, R, Wind): - Designed and implemented an analytical framework from scratch to track and analyze the evolution of each buy-side fund’s investment style across multiple dimensions- Built a real-time analytics tool from scratch by incorporating the visual representation of research results into an internal website in Django Framework- Streamlined stored SQL procedures and data acquisition process from various data sources including data vendors and open data sources; Daily data acquisition- Contacted various data providers to find the most suitable data sources- Coordinated meetings with other departmentsInvestigated approaches to assess the performance and reliability of potential suppliers' products and generated reports on the findings

Statistical/quantitative research and ad-hoc analysis for alpha searching (Python, SQL, R, Bloomberg, Wind): - Built reliable leading indicators on market sentiment, macroeconomic and fundamentals to improve trading outcomes- Constructed effective trading strategies with entry and exit plans for A-shares, commodity futures and options- Backtesting of various quantitative trading/ FOF strategies; 80%+ accuracy in judgement based on the research- Statistical analysis on macroeconomic risk exposure of stock fundamentals- Performance evaluation of mutual funds in various dimensions (return, risk, volatility, investment style, etc.) to identify the sources of return and alpha-generating ability of fund managersIncorporated the research result into the firm’s internal platform in Django Framework (Python, JavaScript) which serves institutional clients (mutual funds, asset management companies, hedge funds, etc.) as real-time analytics and key indicators monitoring tools for data-informed decision makingProvided regular reports, conducted conference calls, and maintained communication with institutional clients to deliver strategies for profit improvement and risk management

- Study of a particle filtering/ sequential Monte-Carlo model for real-time estimation of corporate bond reference prices (full-time internship)- Implemented the algorithm and applied the model to heterogeneous data including RFQ and trading data (Python, Q kdb+)- Ensured a timely delivery of the project by providing prompt solutions to var- Collaborated closely with traders and developers

- Market risk projection in Risk Management Department (full-time internship)- Reviewed and improved the estimation methodology for key indicators (the provisions and Short Term Economic Capital (STEC)) under Solvency II- Enhanced the risk monitoring tool by integrating sensitivity analysis, stress tests and automatic dashboard reporting on main risk indicators into the existing tool (Excel/VBA, R)- Increased calculation speed by 6 times with the introduction of new functions- Regularly monitored and reported on key risk indicators to the team leader; Edited user documentation

- Churn analysis using R, SQL and Excel in Business Intelligence Department- Statistical modeling to identify patterns indicative of churn and optimize proactive customer retention incentives by cohorts- Primary test resulted in cost saving with similar customer retention improvement