Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies

Overview

G-Research Crypto Forecasting

Over $40 billion worth of cryptocurrencies are traded every day. They are among the most popular assets for speculation and investment, yet have proven wildly volatile. Fast-fluctuating prices have made millionaires of a lucky few, and delivered crushing losses to others. Could some of these price movements have been predicted in advance?

In this competition, you'll use your machine learning expertise to forecast short term returns in 14 popular cryptocurrencies. We have amassed a dataset of millions of rows of high-frequency market data dating back to 2018 which you can use to build your model. Once the submission deadline has passed, your final score will be calculated over the following 3 months using live crypto data as it is collected.

The simultaneous activity of thousands of traders ensures that most signals will be transitory, persistent alpha will be exceptionally difficult to find, and the danger of overfitting will be considerable. In addition, since 2018, interest in the cryptomarket has exploded, so the volatility and correlation structure in our data are likely to be highly non-stationary.

This repo constitutes an attempt on the following Kaggle competition: https://www.kaggle.com/c/g-research-crypto-forecasting/overview/description

Owner
Panagiotis (Panos) Mavritsakis
Water Management M.Sc., TU Delft Civil Engineering Diploma, NTU Athens
Panagiotis (Panos) Mavritsakis
Customers Segmentation with RFM Scores and K-means

Customer Segmentation with RFM Scores and K-means RFM Segmentation table: K-Means Clustering: Business Problem Rule-based customer segmentation machin

5 Aug 10, 2022
Climin is a Python package for optimization, heavily biased to machine learning scenarios

climin climin is a Python package for optimization, heavily biased to machine learning scenarios distributed under the BSD 3-clause license. It works

Biomimetic Robotics and Machine Learning at Technische Universität München 177 Sep 02, 2022
A Python step-by-step primer for Machine Learning and Optimization

early-ML Presentation General Machine Learning tutorials A Python step-by-step primer for Machine Learning and Optimization This github repository gat

Dimitri Bettebghor 8 Dec 01, 2022
TensorFlow implementation of an arbitrary order Factorization Machine

This is a TensorFlow implementation of an arbitrary order (=2) Factorization Machine based on paper Factorization Machines with libFM. It supports: d

Mikhail Trofimov 785 Dec 21, 2022
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 03, 2023
Scikit-Garden or skgarden is a garden for Scikit-Learn compatible decision trees and forests.

Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.

260 Dec 21, 2022
A Python implementation of FastDTW

fastdtw Python implementation of FastDTW [1], which is an approximate Dynamic Time Warping (DTW) algorithm that provides optimal or near-optimal align

tanitter 651 Jan 04, 2023
Implementation of the Object Relation Transformer for Image Captioning

Object Relation Transformer This is a PyTorch implementation of the Object Relation Transformer published in NeurIPS 2019. You can find the paper here

Yahoo 158 Dec 24, 2022
Project to deploy a machine learning model based on Titanic dataset from Kaggle

kaggle_titanic_deploy Project to deploy a machine learning model based on Titanic dataset from Kaggle In this project we used the Titanic dataset from

Vivian Yamassaki 8 May 23, 2022
Predicting India’s COVID-19 Third Wave with LSTM

Predicting India’s COVID-19 Third Wave with LSTM Complete project of predicting new COVID-19 cases in the next 90 days with LSTM India is seeing a ste

Samrat Dutta 4 Jan 27, 2022
Hierarchical Time Series Forecasting using Prophet

htsprophet Hierarchical Time Series Forecasting using Prophet Credit to Rob J. Hyndman and research partners as much of the code was developed with th

Collin Rooney 131 Dec 02, 2022
Time Series Prediction with tf.contrib.timeseries

TensorFlow-Time-Series-Examples Additional examples for TensorFlow Time Series(TFTS). Read a Time Series with TFTS From a Numpy Array: See "test_input

Zhiyuan He 476 Nov 17, 2022
Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)

sklearn-compatible Random Bits Forest Scikit-learn compatible wrapper of the Random Bits Forest program written by Wang et al., 2016, available as a b

Tamas Madl 8 Jul 24, 2021
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Microsoft 14.5k Jan 07, 2023
A handy tool for common machine learning models' hyper-parameter tuning.

Common machine learning models' hyperparameter tuning This repo is for a collection of hyper-parameter tuning for "common" machine learning models, in

Kevin Hu 2 Jan 27, 2022
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows.

An open-source, low-code machine learning library in Python 🚀 Version 2.3.5 out now! Check out the release notes here. Official • Docs • Install • Tu

PyCaret 6.7k Jan 08, 2023
Avocado hass time series vs predict price

AVOCADO HASS TIME SERIES VÀ PREDICT PRICE Trước khi vào Heroku muốn giao diện đẹp mọi người chuyển giúp mình theo hình bên dưới https://avocado-hass.h

hieulmsc 3 Dec 18, 2021
Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM

Joaquín Amat Rodrigo 297 Jan 09, 2023
Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

Puesta en Producción de un modelo de aprendizaje automático con Flask y Heroku L

Jesùs Guillen 1 Jun 03, 2022
Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python Open access and Code This repository contains the open access version of the text and the code examples in

Bayesian Modeling and Computation in Python 339 Jan 02, 2023