option_pricing

C++/Python quantitative finance engine for option pricing, implied-volatility analysis, and market-data ingestion.

What is included

• cpp/: core C++ pricing library (Monte Carlo + Black-Scholes closed form), DB ingestion hooks, and pybind bindings.
• qengine/: Python package exposing the native extension (import qengine).
• src/ImpliedVolatility/: SVI calibration and implied-volatility tooling.
• src/data/: data ingestion, SQL schema, and analytics helpers.
• tests/: C++ unit tests (GoogleTest).
• scripts/: operational scripts, including PostgreSQL setup.
• docs/: Doxygen configuration and generated API docs (ignored in git for publication).

Quickstart

1) Clone and create a Python environment

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -e .
pip install pandas yfinance sqlalchemy psycopg2-binary matplotlib scipy

2) Configure environment variables

cp .env.example .env

Then edit .env with your local database credentials.

3) Create database and schema

Use the idempotent setup script:

source .env
python scripts/setup_postgres.py

This script creates/updates:

• database role (DB_USER)
• database (DB_NAME)
• tables/indexes from src/data/sql/schema.sql

4) Build C++ extension and run tests

cmake -S . -B build
cmake --build build -j
ctest --test-dir build --output-on-failure

5) Run Yahoo options ingestion

source .env
python src/data/ingestion/ingest_yahoo_options.py

PIPELINE_SYMBOLS in .env controls which symbols are ingested (comma-separated, e.g. SPY,AAPL,QQQ).

Security and publication notes

• No credentials are stored in source code.
• .env files are git-ignored; only .env.example is committed.
• Before publishing, rotate any credentials that were ever committed in the past.
• Prefer least-privilege DB users for runtime ingestion jobs.

Generating C++ API docs

cmake --build build --target docs

Generated output goes to docs/html/ and is ignored in version control.