quantlab

A personal research repository exploring quantitative finance, model risk, and machine learning for derivatives pricing and hedging.

🔍 Note: This project is unrelated to QuantLib.org. It is a private learning and experimentation space by AlmostAnna.

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What’s Inside

This repo contains:

• Classical models: Black-Scholes, Heston, local volatility
• Exotic derivatives: Autocallables, Asian options, structured notes
• Hedging analysis: Error decomposition, stop-loss replication, discrete trading
• Machine learning: Buehler-style deep hedging with transaction costs
• Stress testing: Sensitivity to volatility misspecification, rebalancing frequency

All code is organized to support reuse, clarity, and diagnostics—not just one-off experiments.

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Structure

quantlab/ 
├── notebooks/  
│ ├── models/ # Stochastic volatility, Dupire, etc. 
│ ├── model_risk/ # Hedging errors, replication failure, Greeks 
│ └── ml/ # Deep hedging, training diagnostics 
├── src/ # Reusable quant primitives (installable as 'quantlab')
|   ├── quantlab/
│      ├── hedging/ # Greeks, naive strategies
|      ├── instruments/ 
│      ├── market_data/ 
|      ├── ml/ # Models, metrics
|      ├── models/
|      ├── pricing/
|      ├── sim/ # MC simulations
│      └── utils/  
├── ml/ # ML-specific training and evaluation
├── tests/ # Tests
├── pyproject.toml # For editable install 
└── environment.yml

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Getting Started

1. Clone and install:

   git clone https://github.com/AlmostAnna/quantlab.git
   cd quantlab
   pip install -e .[dev]

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Philosophy

• Clarity over cleverness: Code should speak for itself.
• Model risk matters: Every assumption is surfaced and tested.
• ML as a tool, not a black box: Diagnostics, baselines, and stress tests are first-class citizens.

© 2025 AlmostAnna — For learning, reflection, and professional growth.