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@dream-faster

Myalo

Financial Machine Learning with scientific rigour

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Myalo - Financial Machine Learning

Welcome 👋,

We are an Machine Learning Studio (Dream Faster), developing the Financial Machine Learning platform (Myalo).

Through Myalo's platform we are bringing much-needed transparency, speed and rigour to the process of building interpretable, reproducable time-series pipelines.

If you are a quant researcher, a fund manager or generally interested in modeling financial data get in touch with us at [email protected]

More details on our website: https://www.myalo.finance/

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  1. krisi krisi Public

    ⏳ Evaluation of Time-Series Predictions with powerful pdf and web Reporting. Tailored for evaluation of metrics over time!

    Python 22

  2. fold fold Public

    🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour. Thi…

    Python 101 6

  3. fold-wrappers fold-wrappers Public archive

    🧱 Wrappers for 3rd party models to be used with fold (https://github.com/dream-faster/fold)

    Python 6 2

  4. fold-models fold-models Public archive

    ⚙️ Speedy implementations of TS Models, built specifically for Fold (https://github.com/dream-faster/fold)

    Python 4

Repositories

Showing 10 of 12 repositories
  • fold Public

    🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour. This is an internal project - documentation is not updated anymore and substantially differ from the current API.

    dream-faster/fold’s past year of commit activity
    Python 101 6 7 1 Updated Feb 29, 2024
  • .github Public

    This is a repo for our public profile.

    dream-faster/.github’s past year of commit activity
    0 0 0 0 Updated Feb 20, 2024
  • krisi Public

    ⏳ Evaluation of Time-Series Predictions with powerful pdf and web Reporting. Tailored for evaluation of metrics over time!

    dream-faster/krisi’s past year of commit activity
    Python 22 0 4 0 Updated Jan 15, 2024
  • fold-wrappers Public archive

    🧱 Wrappers for 3rd party models to be used with fold (https://github.com/dream-faster/fold)

    dream-faster/fold-wrappers’s past year of commit activity
    Python 6 MIT 2 10 2 Updated Jul 20, 2023
  • arfs Public Forked from ThomasBury/arfs

    All Relevant Feature Selection, with Time Series CV

    dream-faster/arfs’s past year of commit activity
    Python 0 MIT 12 0 0 Updated Jul 12, 2023
  • fold-models Public archive

    ⚙️ Speedy implementations of TS Models, built specifically for Fold (https://github.com/dream-faster/fold)

    dream-faster/fold-models’s past year of commit activity
    Python 4 0 0 0 Updated May 8, 2023
  • licenseheaders Public Forked from johann-petrak/licenseheaders

    Simple python script to add/replace license headers in a directory tree of source files

    dream-faster/licenseheaders’s past year of commit activity
    Python 0 BSD-3-Clause 82 0 0 Updated Apr 19, 2023
  • modular-pipelines Public archive

    🐾 A lightweight & extensible library to create complex multi-model and multi-modal pipelines, including ``Ensembles`` and ``Meta-Models``

    dream-faster/modular-pipelines’s past year of commit activity
    Python 3 0 19 1 Updated Sep 23, 2022
  • research-hate-speech-api Public archive

    🎙 Hate-speech detection Demo API built with Modular Pipelines: https://github.com/applied-exploration/modular-pipelines

    dream-faster/research-hate-speech-api’s past year of commit activity
    Python 0 MIT 0 0 0 Updated Sep 16, 2022
  • laplace-gnn-recommendation Public archive

    ⛓️ laplace is an end-to-end ML framework to train and predict on neurally-enhanced graphs for recommendation. The pipeline is designed for self-supervised edge prediction on heterogenous graphs.

    dream-faster/laplace-gnn-recommendation’s past year of commit activity
    Python 2 0 21 1 Updated May 31, 2022

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