🧠 OpenQuantKit

The open-source data pipeline toolkit for modern quant research.
Clean data. Modular pipelines. Real financial signals.

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📈 What is OpenQuantKit?

OpenQuantKit (OQK) is a modular data pipeline framework designed for developers, analysts, and researchers working with financial data.
It connects historical and real-time market data to a clean, scalable analytics stack — using Dagster, dbt, and PostgreSQL.

From ingestion to transformation to dashboard-ready metrics — everything is reproducible, inspectable, and cloud-ready.

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🔧 Features

✅ Modular pipelines powered by Dagster
✅ Clean transformations using dbt
✅ PostgreSQL-first architecture — no DuckDB
✅ Fast ingestion from Yahoo Finance via yfinance
✅ Built-in quality metrics for tickers
✅ Plug-in ready for signals, strategies, and models
✅ Ready for local or cloud deployment
✅ Works with Streamlit, Jupyter, and backtesting frameworks

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💡 Why Use It?

Most quant research starts with messy CSVs and ad-hoc scripts.

OpenQuantKit gives you:

• A clean, SQL-based foundation
• Transparent, testable data transformations
• Production-grade pipelines
• No vendor lock-in

Whether you're:

• 🧑‍💻 Building a personal quant system
• 🎓 Working on a research thesis
• 🧠 Experimenting with alpha factors
• 🚀 Launching a new fintech prototype

...OQK gives you structure without sacrificing flexibility.

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⚡ Quickstart (PostgreSQL)

1. Build and run the stack

make build        # Build containers
make postgres     # Start PostgreSQL
make dagster      # Start Dagster + dbt

2. Ingest raw price data

To fetch all available price data from Yahoo Finance and store in PostgreSQL:

docker compose run --rm dagster python -m open_quant_kit.raw.raw_price

This reads tickers from dim_ticker, downloads full price history, and stores it in the raw_price table.

3. Run dbt models

To compute data quality metrics:

docker compose run --rm dagster dbt build --select fct_ticker_data_quality

The model will create or update a table with per-ticker statistics such as gaps, duplicates, volatility, and completeness.

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📚 Components Overview

🔄 Ingestion

• raw_price.py — Fetches historical price data from Yahoo Finance
• Modular, resumable, and ticker-aware

📊 Transformation

• fct_ticker_data_quality.sql — dbt model computing:
  • Data duration and coverage
  • Largest gaps and completeness
  • Volatility and recentness checks

🛠️ Resources

• Dagster orchestrates asset materialization and scheduling
• dbt handles transformations declaratively in SQL
• PostgreSQL stores all raw + modeled data

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🤝 Contributing

PRs, issues, and ideas welcome.
This project is early-stage and evolving. Feedback makes it better!

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