MLCF - Machine Learning Toolkit for Cryptocurrency Forecasting


Keywords
finance, machine-learning, ai, deep-learning, time-series, toolkit, cryptocurrency, neural-networks, forecasting, trading-algorithm, investment, trading-agent, mlcf, python, pytorch, trading-algorithms
License
GPL-3.0
Install
pip install mlcf==2.2.6

Documentation

MLCF - Machine Learning Toolkit for Cryptocurrency Forecasting

This library provides tools for cryptocurrency forecasting and trade decision making.
Among which :

  • data acquisition to download historical cryptocurrency data.

  • shaping and preprocessing data for time series machine learning.

  • directory and file management to help machine learning training (models versionning, checkpoint, logs, visualization, etc.).

This library doesn't provide models or an end-to-end trade bot. It is only providing tools to make easier the data acquisition, the data analysis, the forecasting and decision making training. However, MLCF provide a file management which allows to make an end-to-end model easier.

In addition to using modules' functions, we can use MLCF as a python module :

python -m mlcf <list_of_arguments>

Installation

A simple pip install is require to install MLCF.

pip install mlcf

MLCF library

In this part is introduced all the current tools of MLCF.

Datatools

The datatools library provides :

  • WTSeries
  • WTSeriesTraining
  • WTSeriesTensor
  • WTSeriesPreProcess
  • Indice
  • datasetools

AiTools

The aitools library provides :

  • SuperModule
  • TrainingManager

EnvTools

The envtools library provides :

  • ProjectHome