Arnav Gupta Email & Phone Number

Proprietary hedging strategies in commodities and quant models for derivatives trading with focus on commodity players, family offices, hedge funds, and brokers. I specialize in risk hedging techniques using VaR and CVaR for commodities, developing trading strategies for managed futures funds and CTAs with AI-enabled quant models, generating backtesting metrics, and performing pre-trade risk analysis.As a quant professional my experience is in following areas: 1. Risk Analysis using VaR and CVaR: Use of historic VaR and CVaR, parametric CVaR (95%) under a normal distribution and non-parametric using gaussian kernels CVaR (95%) for effective risk management and hedging. 2. Comprehensive insight to metrics: includes standard deviation, downside risk, drawdown, return statistics, rolling returns, Sharpe ratio, cumulative returns, CAGR, z-score, limits3. Visual insights: including Rolling Beta plots, Distribution of Returns, Rolling volatility, Rolling Sharpe ratio, Return Quantiles, Cumulative returns on a log scale, Drawdown periods and Underwater Plot4. Quantitative expertise: using tools like yfinance, pandas datareader, matplotlib, numpy and various models such as Linear/Logistic Regression, SVM, XGBoost, Random Forest, and Decision Tree Learner on financial dataset.5. Volatility Analysis: Employing alpha and beta for combined performance and volatility analysis. Calculation of ADR, SDR, ATR.6. Cointegration Analysis: Augmented Dickey Fuller test, p-value heatmap, correlation matrix.7. Expertly identified trade within derivatives: futures & options in iron condor strategy and backwardation8. Captured detailed transaction specifics, including entry/exit dates, prices, profits, losses, and percentage returns. 9. Preprocessed financial data: null handling, one-hot encoding, ADASYN, metric derivation, continuous feature binning, outlier treatment.10. Model evaluation metrics: Confusion Matrix, ROC Curve, Regression, Recall, Precision, and Accuracy.Hands-on exposure in following tools for quant modeling:1. Python: Backwardation Hedging & Risk Analysis, Buy and hold Strategy Analysis with Benchmark, Iron Condor Strategy, Statistical Pair Trading, Put Option Strategy Backtesting.2. Power BI: Hedged vs Unhedged VaR of a Commodities Portfolio 3. Knime Analytics: Calculation of VaR and CVaR of a Unhedged and Hedged Commodities Portfolio4. MATLAB: SEIR model implementation, Model building, Equations, Jacobian, eigenvalues/vectors, determinants, trajectory plotting using GraphCode.Please refer to the Projects section for a full description