In these Python and R scripts, markdown and notebooks, I provide a basis for understanding how the cryptocurrencies market works, as well as how we can divide it into categories, supported by a hierarchical clustering. Other contributions regard the accurate prediction of Bitcoin and Ether Prices one day ahead and nice data bases with which to play around and test my results, if wanted. This repository contains part of the code used for the development of my TFG (Graduate thesis). It consists on developing a trading strategy for cryptocurrencies, by using different ML and AI methods. Although more research needs to be carried out, the algorithms provided here were proved to be able to beat the market, selecting the correct trading strategy based on them. If anyone finds this repository of help, you can always donate to the following wallets :) I will appreciate it very much.

BTC: 3AixGeUWqghtnHua4ZPPPxMr8n6Wkv3wbo

BCH: qrzckxv02jzarvchjch9npsetafxz560uua8g94gvk

ETH: 0xA6C5b8C58e0e4Ed82081A1eABb40a686dB02F073

LTC: MJGdPCxr5mR6EQz7SqGjvHjAQoR8sTZguu

NEO: APjW37QXaqwexR6FQEMn7t2h3sV4KHU3Si

LSK: 7121073847307755132L

ZEC: t1ZPBDeJBexvx9wW5gCHsKQo1obezq5JA3S