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Gaze31/README.md

Hi, I'm Sumedha 👋

Quant-Minded ML Engineer | Python Full-Stack Developer

I build models that survive walk-forward testing — then ship them as real APIs and dashboards. Not notebooks. Production.

📈 Quant / Finance work

  • 🔀 3 merged PRs to yfinance — a 22k★ production library, actual maintainer-reviewed code
  • 📊 Alpha Engine — multi-factor model on Nifty 50, Sharpe 5.87, walk-forward validated with Deflated Sharpe Ratio
  • 🎲 Derivatives Pricing Engine — Black-Scholes, Binomial CRR, Monte Carlo (100K paths); put-call parity verified to 0.000000
  • 🧠 FinBERT Sentiment Pipeline — 0.96 Macro F1, 4-stage NLP pipeline, stress-tested on live headlines

🖥️ Full-stack / ML engineering work

  • 🏦 Credit Risk API — gradient boosting (0.77 AUC), calibrated probabilities, serverless FastAPI on Vercel, React dashboard
  • 🔗 Live, working endpoint — hit it right now, it's real
  • 🐳 Model training → REST API → Postgres → React UI → containerized deploy

🛠️ Stack

📊 GitHub Stats

🔍 Currently

Open to quant/ML engineering roles — remote, globally.

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  1. alpha-engine alpha-engine Public

    Multi-factor alpha engine on Nifty 50 — momentum & mean-reversion factors. Sharpe 5.87, 146% return. Walk-forward validation, Deflated Sharpe Ratio.

    Jupyter Notebook

  2. credit-risk-api credit-risk-api Public

    End-to-end credit risk ML system — gradient boosting model (0.77 AUC) with calibrated probabilities, numpy-only serverless inference on FastAPI/Vercel, React dashboard.

    Python