I'm a quant-oriented developer building at the intersection of
finance, statistics, and machine learning.
- π Core focus: Stochastic modeling, Monte Carlo simulation, timeβseries forecasting, and risk analytics
- π€ ML focus: Probabilistic inference, anomaly/fraud detection, imbalanced classification
- π Data stack: Feature engineering, financial dashboarding, backtesting pipelines
- π Currently exploring: LLMs applied to financial NLP, deep learning for Ξ±βsignal generation
- π€ Open to collaborate on: Quant research, neural network training, financial data pipelines
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Core inference experiments: MLE, Bayesian estimation, and probabilistic modeling workflows foundational to quant research. |
End-to-end financial modeling: stochastic price simulation, forecasting, risk analysis, and strategy backtesting. |
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Fraud classification on highly imbalanced transactional data. Focus on precision-recall tradeoffs, SMOTE, and ensemble methods. |
Personal finance tracker with embedded analytics, trend visualizations, and spending pattern dashboards. |
- 𧩠Inference-learning β Core inference experiments (MLE/Bayesian ideas) used in my modeling workflow.
https://github.com/AaryanAnand10/Inference-learning - πΉ Finance-Modeling β Financial modeling experiments (forecasting, risk analysis, backtesting).
https://github.com/AaryanAnand10/Finance-Modeling - π‘οΈ Credit Card Fraud Detection β Fraud/anomaly classification on transaction data (precisionβrecall focus).
https://github.com/AaryanAnand10/Credit_Card_Fraud_Detection- - π° Income-Tracker β Personal finance tracker with analytics and visualizations.
https://github.com/AaryanAnand10/Income-Tracker-
"In God we trust; all others must bring data." β W. Edwards Deming