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MlFinLab is a collection of production-ready algorithms (from the best journals and graduate-level textbooks), packed into a python library that enables portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
SPEND LESS TIME BUILDING, USE IT
SPEND LESS TIME BUILDING, USE IT
SPEND LESS TIME BUILDING, USE IT
MlFinLab is built for teams who drive innovation.
PRODUCTION-READY
IMPLEMENTATIONS
DOCUMENTATION
EXAMPLE
NOTEBOOKS
RUNS LOCALLY
TIERS
Suiting your needs
MODULES
Encompassing the latest research
PERSON HOURS
In research and development
COVERAGE
Production-ready code
MlFinlab Version 1.7.1
Financial Data Structures
When analyzing financial data, unstructured data sets are commonly transformed into a structured format referred to as bars, where a bar represents a row in a table. MlFinLab implements tick, volume, and dollar bars using traditional standard bar methods as well as the less common information driven bars.
Labelling Techniques
This module features a very comprehensive set of labeling techniques such as Triple-Barrier, Meta-Labeling, Trend Scanning, Tail Sets, and Matrix Flags.
Feature Engineriing
Feature engineering is the process of using domain knowledge to transform data into features for a model which are informative. We include famous techniques derived from market-microstructure, structural breaks, filters, and fractionally differentiated features.
Machine Learning
This module focuses on important modeling techniques needed for financial data science. Including feature importance, cross-validation, position-sizing, sampling, and sequentially bootstrapped ensembles.
Codependance and Networks
This module includes a comprehensive collection of codependence metrics, which can be used in portfolio construction, risk management, pairs trading, and informative features to a machine learning model.
Synthetic Data Generation
This module includes implementations of synthetic data generation algorithms as they relate to correlation matrices and financial time series. We include the well-known CORGAN model as well as all the landmark papers such as vine, extended onion, and HCBM.
Backtest Overfitting
Backtests are well known to overfit and promote false investment discoveries. The module provides award-winning frameworks – which minimize the chances of a false discovery. It includes algorithms such as the deflated and hair cut sharpe ratios, profit hurdles, minimum track record length, and more.
Clustering Techniques
Finance has its own set of clustering algorithms such as the Optimal Number of Clusters (ONC) algorithm which can be used to filter out false investment strategies, cluster similar assets for portfolio optimisation, and help select important features for modeling.
Networks Modelling
The Networks module creates beautiful and informative visualizations of financial data, using network theory to describe complex systems such as financial markets. In addition to this, portfolios can be constructed which outperform during market crashes, such as the COVID19 pandemic. Long short portfolios with their own unique alpha, may also be leveraged.
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BUSINESS
£100 (+VAT) per month, per user
LICENSE FOR COMMERCIAL USE
DOCUMENTATION & TUTORIAL NOTEBOOKS
Unlock access to documentation and dozens of example notebooks by subscribing to this tier.
ENTERPRISE
Individual plans for commercial clients
SUITABLE FOR MULTIPLE USERS
BULK DISCOUNTING
LICENSE CUSTOMIZATION
LICENSE FOR COMMERCIAL USE
DOCUMENTATION & TUTORIAL NOTEBOOKS
Unlock the access to documentation and dozens of example notebooks by subscribing to this tier.