Skip to main content

FAMLAFL Aren’t Machine Learning And Financial Laboratory

Project description

FAMLAFL: FAMLAFL Aren’t Machine Learning And Financial Laboratory

Installation

For users:

pip install famlafl

Or with poetry:

poetry add famlafl

Project Structure

famlafl/
├── backtest_statistics/    # Backtesting tools and statistics
├── bet_sizing/            # Position sizing and bet sizing tools
├── clustering/            # Clustering algorithms for financial data
├── codependence/          # Codependence and correlation metrics
├── cross_validation/      # Cross-validation for financial data
├── data_structures/       # Financial data structures
├── datasets/              # Sample datasets and loaders
├── ensemble/              # Ensemble methods
├── feature_importance/    # Feature importance analysis
├── features/             # Feature engineering tools
├── filters/              # Financial data filters
├── labeling/             # Financial data labeling tools
├── microstructural_features/  # Market microstructure features
├── multi_product/        # Multi-product analysis
├── online_portfolio_selection/  # Online portfolio selection
├── portfolio_optimization/  # Portfolio optimization tools
├── sample_weights/       # Sample weight generation
├── sampling/             # Financial data sampling
├── structural_breaks/    # Structural break detection
└── tests/               # Unit tests

Development

Running Tests

# Run all tests
poetry run pytest

# Run tests with coverage
poetry run pytest --cov=famlafl --cov-report=html --cov-report=term

# Run specific test file
poetry run pytest famlafl/tests/test_specific_file.py

Contributing

We welcome contributions from the community! Please see our Contributing Guidelines for more details on how to get involved.

License

This is a fork of mlfinlab (ArbitrageLab), developed by Hudson & Thames Quantitative Research.

Important

  • All mlfinlab-derived code here remains under Hudson & Thames’s “all rights reserved” license.
  • Any new or original code that I (Vadim Surin) wrote from scratch (and does not derive from mlfinlab code) is released under the BSD-3-Clause License. However, usage in combination with mlfinlab code is still governed by Hudson & Thames’s restrictions.

Licensing Overview

  1. Hudson & Thames License (All Rights Reserved)
    The original mlfinlab portion of this repository is subject to the Hudson & Thames license (or see the license text included in this repo’s LICENSE file).

    Their license overrides any open-source terms with respect to the mlfinlab files.

  2. BSD-3-Clause (for My Independent Code)
    Purely original files that do not include or derive from mlfinlab logic can be used under BSD-3-Clause terms.

    Note: If these files are used in conjunction with mlfinlab code, the combined work is effectively subject to Hudson & Thames’s license to the extent of mlfinlab’s portion.

Usage

Feel free to experiment with my additions, but remember mlfinlab’s license requires you to comply with Hudson & Thames’s terms for the original (and derived) code.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

famlafl-0.1.8.tar.gz (745.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

famlafl-0.1.8-py3-none-any.whl (859.9 kB view details)

Uploaded Python 3

File details

Details for the file famlafl-0.1.8.tar.gz.

File metadata

  • Download URL: famlafl-0.1.8.tar.gz
  • Upload date:
  • Size: 745.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.0.0 CPython/3.12.8 Linux/6.8.0-1017-azure

File hashes

Hashes for famlafl-0.1.8.tar.gz
Algorithm Hash digest
SHA256 354550d9c81583968f4256b48e9541290a0e26b632afb43dd901f7bdd24cbf68
MD5 eb0560b3322b6af79bd83fedf82f5fa1
BLAKE2b-256 b78cc6eee199c6398b4c4eedb08719a5fff135d15d35759438548e92d13b0e36

See more details on using hashes here.

File details

Details for the file famlafl-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: famlafl-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 859.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.0.0 CPython/3.12.8 Linux/6.8.0-1017-azure

File hashes

Hashes for famlafl-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 1555eb031e02f21f280131cb8cbbc6fe9fbd596078b97bba073619cce9e80c3b
MD5 b8d8c67d48ba5673bc344fdd20055edc
BLAKE2b-256 e4222cab7d08a8054f85e33160023f542c3b8f3f4c7c66b24d70ac8f75604bd7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page