Production-ready implementations of advanced financial machine learning for institutional asset managers.
Hudson & Thames mlfinlab is a high-performance Python framework designed to bridge the gap between academic theory—specifically the work of Marcos López de Prado—and institutional production environments. The technical architecture focuses on solving the unique challenges of financial data, such as non-stationarity, low signal-to-noise ratios, and backtest overfitting. By 2026, the suite has evolved into a comprehensive 'Quant Stack' that includes specialized modules for fractional differentiation (preserving memory in time-series), hierarchical risk parity (HRP), and meta-labeling techniques that separate the 'buy/sell' decision from the 'size/confidence' decision. Its market positioning is unique: it acts as the industrial-grade implementation of the 'Advances in Financial Machine Learning' and 'Machine Learning for Asset Managers' methodologies. The library provides standardized pipelines for labeling (Triple Barrier Method), cross-validation (Purged and Embargoed CV), and feature importance (Mean Decrease Impurity and Accuracy), ensuring that asset managers can apply ML without falling into the common traps of data leakage and selection bias.
Hudson & Thames mlfinlab is a high-performance Python framework designed to bridge the gap between academic theory—specifically the work of Marcos López de Prado—and institutional production environments.
Explore all tools that specialize in portfolio optimization. This domain focus ensures Hudson & Thames (mlfinlab) delivers optimized results for this specific requirement.
A labeling technique that assigns labels based on the first of three barriers touched: a profit-taking barrier, a stop-loss barrier, or a time-out barrier.
Iterative stationary transformation of time-series that allows for a non-integer order of differentiation, preserving long-term memory.
Portfolio optimization method that uses graph theory and hierarchical clustering to allocate weights without needing a covariance matrix inversion.
Specialized cross-validation that removes training observations that overlap with test observations in time or have correlation due to the labeling process.
Feature importance ranking that accounts for feature substitutions and multi-collinearity in financial datasets.
Generative models (GANs and VAEs) tailored to produce synthetic financial time-series that mimic real asset distributions and correlations.
Implements CUSUM and Chow tests to detect changes in market regimes or price dynamics.
Obtain a Research Membership from Hudson & Thames to access the private Python Package Index (PyPI).
Configure environment variables with your H&T API Key for authentication.
Install via pip: 'pip install mlfinlab' targeting the private repository.
Load financial time-series data into a structured Pandas DataFrame with UTC timestamps.
Apply Fractional Differentiation to transform non-stationary price data while preserving maximum memory.
Execute the Triple Barrier Method to label data based on profit-taking, stop-loss, and time-exhaustion limits.
Implement Purged and Embargoed K-Fold Cross-Validation to prevent data leakage between training and testing sets.
Train a Meta-Labeling model to filter the primary signals and determine optimal position sizing.
Construct a portfolio using Hierarchical Risk Parity (HRP) or Nested Clustered Optimization (NCO).
Run the Probability of Backtest Overfitting (PBO) test to validate the statistical significance of results.
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