name: Sai Vamshi Miryalkar
role: [Quantitative Developer , Data Scientist, Data Analyst]
education: "Bachelors in Data Science & Applications — IIT Madras"
focus:
- Quantitative & Algorithmic Trading Strategy Development
- Statistical Modeling & Time-Series Forecasting
- Risk Analytics & Portfolio Optimization
- Business Intelligence & Predictive Analytics
currently_building: "Backtesting engine + factor-based alpha models"
currently_learning: ["Stochastic Calculus", "Market Microstructure", "C++ for Low-Latency Systems"]
fun_fact: "I'd rather backtest a strategy 50 times than trade it once blind."- 📊 Skilled in turning raw, noisy market & business data into actionable, risk-adjusted insights
- 📈 Experience building dashboards, KPI systems, customer analytics, and predictive/statistical models
- 🧮 Deepening expertise in quantitative finance: derivatives pricing, backtesting, portfolio risk, and systematic strategy design
- 🌱 Actively learning: options pricing (Black-Scholes, Binomial trees), GARCH/ARIMA modeling, and DSA for interview-grade quant roles
- 🎯 Goal: Break into Quantitative Analyst / Quant Developer roles bridging data science rigor with financial markets
| Domain | Skills |
|---|---|
| Derivatives & Pricing | Black-Scholes-Merton, Binomial/Trinomial Trees, Monte Carlo Simulation, Greeks (Delta, Gamma, Vega, Theta), Implied Volatility Surfaces |
| Quantitative Trading | Strategy Backtesting, Signal Generation, Mean Reversion & Momentum Models, Factor Investing, Pairs Trading, Portfolio Rebalancing |
| Risk & Portfolio Management | Value at Risk (VaR/CVaR), Sharpe/Sortino Ratios, Modern Portfolio Theory, Covariance & Correlation Modeling, Position Sizing |
| Time Series & Econometrics | ARIMA/SARIMA, GARCH/EGARCH (Volatility Modeling), Cointegration, Stationarity Testing (ADF/KPSS), Kalman Filters |
| Mathematical Foundations | Stochastic Calculus, Linear Algebra, Probability Theory, Optimization (Convex & Linear Programming), Numerical Methods |
| Systems & Infrastructure | Low-Latency C++ Fundamentals, Vectorized Python (NumPy/Pandas), API Integration for Market Data, Event-Driven Backtest Architecture |
| Project | Description | Stack |
|---|---|---|
| 📉 Telecom Customer Churn Analysis | End-to-end churn prediction pipeline with feature engineering and business dashboards | Python · SQL · Power BI |
| 🏦 Banking Customer Analytics Dashboard | Customer segmentation, KPI reporting, and behavioral analytics for retail banking | SQL · Power BI |
| 📈 Apple Stock Market Analysis | Financial trend analysis, volatility, and returns modeling on historical equity data | Python · Pandas · NumPy |
| 🧮 SQL Analytics Projects | Advanced querying, window functions, and business analytics case studies | SQL |
| 🧩 DSA in C++ | Core data structures & algorithms implementation for interview and systems readiness | C++ |
| 💹 Options Pricing Engine (in progress) | Black-Scholes & Binomial Tree pricer with Greeks calculator and volatility surface plots | Python · NumPy · SciPy |
| 🔁 Systematic Strategy Backtester (in progress) | Event-driven backtesting framework for momentum and mean-reversion strategies | Python · Pandas · Matplotlib |
Indian Institute of Technology Madras (IIT Madras) Bachelor of Science in Data Science & Applications (Pursuing)
Relevant Coursework
Python Programming Statistics Machine Learning Database Management Systems
Business Analytics Data Structures & Algorithms SQL & Database Design
Aspiring Quantitative Developer and Data Scientist seeking to apply statistical rigor, programming proficiency, and financial market knowledge to build data-driven trading strategies and risk models — bridging the gap between analytics and quantitative finance.
💼 LinkedIn: sai-vamshi-miryalkar 📧 Email: saivamshimiryalkar@gmail.com 🐙 GitHub: svm-knocks
Research with mathematics. Validate with data. Engineer with code.