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I’m a highly analytical Banking & Finance graduate with hands-on experience in quantitative research, market analysis, and financial data modelling. I apply portfolio management concepts, risk assessment techniques, and derivatives knowledge to real-market environments, translating complex data into actionable investment decisions.

Through my internship at Epension Operation (Ifast Global Hub AI), I conducted data verification and discrepancy resolution with <5% error rate, resolved documentation issues, and strengthened analytical problem-solving under tight deadlines. I also build systematic research tools and backtesting frameworks using Python (Pandas, NumPy, AWS), using metrics like Sharpe ratio, Sortino ratio, maximum drawdown, annualised return, and risk-adjusted returns—then refine signal generation with machine learning-based optimisation techniques. My final year project uses a fully systematic Python backtesting engine to evaluate whether technical analysis indicators can generate statistically significant, risk-adjusted excess returns in cryptocurrency markets.