Overview
At Guangfa Futures, one of China’s leading futures brokerage firms, I worked as a Quantitative Analyst Intern. My role involved applying statistical and machine learning techniques to identify patterns in futures markets and support trading strategy decisions.
Key Projects
Predictive Pattern Identification
Applied multiple modeling approaches to identify predictive patterns in futures markets:
Regression Analysis
- Built linear and non-linear regression models
- Identified relationships between market indicators and price movements
- Validated models using out-of-sample testing
Time Series Analysis
- Analyzed historical price data for temporal patterns
- Implemented ARIMA and related models for trend analysis
- Captured seasonality and cyclical patterns in commodity futures
K-Means Clustering
- Segmented market conditions into distinct regimes
- Identified similar historical periods for pattern matching
- Used clustering to inform regime-switching trading strategies
Sensitivity Analysis
Performed comprehensive sensitivity analyses to:
- Quantify volatility impacts on portfolio positions
- Stress-test strategies under various market scenarios
- Understand model behavior under different parameter assumptions
Forecasting Models
Developed forecasting models at multiple time horizons:
- Monthly forecasts for tactical trading decisions
- Quarterly forecasts for strategic positioning
- Backtested all models against historical data
Reporting & Communication
Delivered actionable reports to support trading strategy decisions:
- Translated complex quantitative findings into clear insights
- Presented recommendations to senior traders and analysts
- Created visualizations to communicate market patterns
Technical Skills Applied
- Statistical Methods: Regression, Time Series Analysis
- Machine Learning: K-Means Clustering
- Tools: Python, R, SQL
- Domain: Futures Markets, Commodities, Financial Derivatives
Impact
My analyses contributed to the trading desk’s understanding of market dynamics and helped inform their positioning strategies during my internship period.