Yichen Wang is a Quantitative Associate with five years of experience in quantitative finance, currently seeking opportunities in tech companies, fintech firms, and banks. They specialize in quantitative research, data analysis, software engineering, and front office roles including sales and trading, with particular expertise in trading algorithms, risk management systems, and financial modeling.

Wang holds a Master's degree in Financial Mathematics from New York University and has built extensive experience across major financial institutions including SMBC Group, S&P Global, and hedge funds. Their professional focus encompasses trading and market making, structuring and hedging, risk and portfolio management, with demonstrated success in implementing sophisticated trading strategies including statistical arbitrage, trend following, and momentum-based approaches.

Their technical expertise spans multiple programming languages and platforms, including Java, Python, R, SQL, and cloud infrastructure on AWS and Linux. Wang has developed over 20 cryptocurrency exchange integrations using APIs, websockets, and web scraping techniques, and has built comprehensive InfluxDB databases for market data management. Their quantitative skills include advanced statistical modeling, machine learning algorithms, time series analysis, volatility modeling for derivatives, and stochastic differential equations.

Wang's academic background combines rigorous mathematical training with practical financial applications, including coursework at The Wharton School in actuarial statistics, international financial markets, and risk management. They have been recognized with the Anna Pell Wheeler Prize in Mathematics and graduated Magna Cum Laude. Their research projects demonstrate proficiency in Monte Carlo simulations, options pricing frameworks, and machine learning applications to high-frequency trading, with documented success in developing predictive models for limit order book dynamics.