Datasets, Models, and Papers for ML Research in Finance
Curated datasets, pretrained models, and literature reviews.
Become a member of our modern research environment.
Practical components for real world research workflows
From raw data to trained models, every layer of ML research stack to retreive.
Data Pipelines
Access curated public and original datasets across market microstructure, macroeconomics, equities, and alternative data. Get ready notebooks to load and inspect the datasets locally.
Model Codebase
Clean implementations of ML and deep learning models for finance from NN architectures to quantitative methods for factor construction, and model training pipelines.
Research Papers
Curated literature across volatility modeling, market microstructure, and deep learning in finance. Topics are updated so you can track active research threads and find relevant ideas early.
Research Notebooks
Curated notebooks providing data pipelines, feature engineering, experimental setup, and paper replications. Each notebook is a self-contained starting point to explore how existing methods work in practice so you can adapt them to new datasets, or build on top of proven pipelines to accelerate your own research workflow.
Curated for efficient
research workflows.
LOB & tick data
FI-2010, LOBSTER, and Binance LOB datasets with train/test splits and multi-regime labels — ready to load in one line.
Research notebooks
Pre-built Jupyter-compatible notebooks for LOB benchmarking, strategy backtesting, factor analysis, and paper replication.
Literature review
140+ papers across limit order books, deep learning for finance, market microstructure, and factor investing — organized by topic and updated regularly.
Guided analysis
Live analysis modules across U.S. macro, market ETFs, and individual companies — updated daily and monthly with reproducible outputs.
Strategy backtests
MA crossover, ATR exit, Bollinger Band breakout, mean-reversion, and BAB factor — self-contained strategy reports with full equity curves.
Developer utilities
Scripts and reference guides for environment setup, SSH, Docker, Git workflows, and Python patterns — built for researchers who manage their own infrastructure.
A growing platform for applied research
Researchers from leading universities and quantitative finance teams use FinanceLab to accelerate their work.