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176 lines
6.2 KiB
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176 lines
6.2 KiB
Markdown
# Option Pricing Engine with Market Data Pipeline
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## 📌 Project Description
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This repository implements a **production-style quantitative valuation pipeline** for equity options, combining high-performance pricing models with a full data and calibration workflow.
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The system goes beyond a standalone pricer: it integrates **market data ingestion, structured storage, numerical pricing, and volatility surface calibration** into a single reproducible framework.
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### The goal of this project
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The goal of this project is to serve as a **modular foundation for quantitative modeling and experimentation** in option pricing and financial time series.
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Rather than implementing a single model, the system is designed to support:
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- benchmarking different pricing approaches (analytical, simulation-based, and data-driven),
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- comparing numerical methods under realistic market data conditions,
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- and extending toward more advanced workflows such as statistical learning and model calibration.
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A key objective is to create an environment where **new ideas from research can be implemented, tested, and evaluated within a consistent pipeline**, rather than in isolated scripts or notebooks.
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This includes:
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- integrating alternative pricing methodologies into a shared framework,
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- analyzing model behavior across time and market regimes,
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- and building reproducible pipelines for both numerical and data-driven approaches.
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Ultimately, the project aims to bridge:
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- **theoretical models** (e.g. stochastic processes, volatility parameterizations),
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- **numerical methods** (simulation, calibration),
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- and **data-driven techniques** (time-series analysis, machine learning),
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within a single, extensible system. Moving closer to a production-grade pipeline.
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### What the system does
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The system supports the following workflow:
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- Ingest listed option market data (Yahoo Finance)
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- Normalize and store it in a relational database (PostgreSQL)
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- Compute implied volatilities from observed prices
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- Calibrate parametric volatility surfaces (SVI)
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- Run pricing models (Black-Scholes, Monte Carlo)
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- Expose fast pricing routines via Python for analysis and research
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---
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This project aims to **unify these components into a coherent system**, with clear interfaces between:
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- **Data layer** (ingestion, storage, schema)
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- **Model layer** (C++ pricing engines)
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- **Analytics layer** (Python calibration and diagnostics)
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- **Execution layer** (reproducible pipelines)
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---
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### Technology choices
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The architecture deliberately combines multiple technologies, each chosen for a specific role:
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- **C++ (C++20)**
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Used for performance-critical pricing components (Monte Carlo, closed-form models) and clean domain modeling.
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- **Python**
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Used for orchestration, data processing, calibration (SVI), and rapid experimentation.
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- **pybind11**
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Bridges C++ and Python, enabling high-performance models to be used in flexible workflows.
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- **PostgreSQL + SQLAlchemy**
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Provides structured, queryable storage for market data and supports reproducible calibration pipelines.
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---
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### Key challenges addressed
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This project tackles several non-trivial challenges:
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- **Bridging performance and usability**
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Integrating a C++ pricing engine into a Python-driven research pipeline.
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- **Data consistency and reproducibility**
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Designing a schema and ingestion process that supports reliable downstream calibration.
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- **Implied volatility inversion and calibration**
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Implementing stable numerical inversion and robust SVI fitting under noisy market data.
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- **System design over isolated models**
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Ensuring that data, models, and workflows interact cleanly as a unified system.
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---
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### Future directions
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Planned improvements focus on moving further toward production-grade systems:
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- Arbitrage-free implied volatility surface construction
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- More robust calibration and smoothing techniques
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- Performance optimization (parallel Monte Carlo, batching)
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- Extension to additional data sources and APIs
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- Improved testing of end-to-end data and calibration pipelines
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- comparing classical stochastic models vs data-driven approaches for pricing or volatility forecasting
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## What is included
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- `cpp/`: core C++ pricing library (Monte Carlo + Black-Scholes closed form), DB ingestion hooks, and pybind bindings.
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- `qengine/`: Python package exposing the native extension (`import qengine`).
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- `src/ImpliedVolatility/`: SVI calibration and implied-volatility tooling.
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- `src/data/`: data ingestion, SQL schema, and analytics helpers.
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- `tests/`: C++ unit tests (GoogleTest).
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- `scripts/`: operational scripts, including PostgreSQL setup.
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- `docs/`: Doxygen configuration and generated API docs (ignored in git for publication).
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## Quickstart
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### 1) Clone and create a Python environment
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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pip install --upgrade pip
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pip install -e .
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pip install pandas yfinance sqlalchemy psycopg2-binary matplotlib scipy
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```
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### 2) Configure environment variables
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```bash
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cp .env.example .env
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```
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Then edit `.env` with your local database credentials.
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### 3) Create database and schema
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Use the idempotent setup script:
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```bash
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source .env
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python scripts/setup_postgres.py
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```
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This script creates/updates:
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- database role (`DB_USER`)
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- database (`DB_NAME`)
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- tables/indexes from `src/data/sql/schema.sql`
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### 4) Build C++ extension and run tests
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```bash
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cmake -S . -B build
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cmake --build build -j
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ctest --test-dir build --output-on-failure
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```
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### 5) Run Yahoo options ingestion
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```bash
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source .env
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python src/data/ingestion/ingest_yahoo_options.py
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```
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`PIPELINE_SYMBOLS` in `.env` controls which symbols are ingested (comma-separated, e.g. `SPY,AAPL,QQQ`).
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## Generating C++ API docs
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```bash
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cmake --build build --target docs
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```
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## 📚 Further Analysis
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A more detailed discussion of numerial stability, implied volatility inversion, and calibration challenges is available here
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👉 [Project blog](https://notes.ddoebel.de/public-folder/Option-Pricing-Engine)
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This includes deeper analysis of:
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- implied volatility instability from raw market data
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- calibration challenges under noisy inputs
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- numerical experiments and diagnostics
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(see in particular [Observations and further analysis](https://notes.ddoebel.de/public-folder/Option-Pricing-Engine#-observations-and-further-analysis))
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