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Hands-on Machine Learning – Cryptocurrency Trading [Python]

Learn how to use Machine Learning and Intermarket Analysis to trade Crypto

  • Free tutorial
  • Rating: 4.7 out of 54.7 (30 ratings)
  • 2,427 students
  • 50min of on-demand video
  • Created by Genbox Trading
  • English

What you’ll learn

  • How to apply Rule Induction Algorithms in Python
  • How to perform intermarket analysis
  • How to download historical data from Python
  • How to validate Machine Learning Models

Requirements

  • None

Description

In this course you are going to learn how to take your trading to the next level applying different techniques to analyze the market and create powerful trading signals.

This course is mainly divided into two concepts.

Intermarket Analysis: Since Cryptocurrencies don’t have much historical data, we need to gain insights by being creative.

In order to analyze one security, we are going to use data from that security and many others.

Let’s say we are analyzing Bitcoin, in that case we will use Bitcoin data, and also Ethereum and even stock indexes.

Once we have mastered that concept, it’s time to roll up our sleeves and start coding.

Using Python you are going to learn how to download historical data and create indicators to gain insights from that data.

Then you are going to learn how to create biased labels to optimize what our algorithm needs to learn.

Finally, once we have everything prepared, we are going to use RIPPER (Repeated Incremental Pruning to Produce Error Reduction) as our rule induction algorithm in order to create readable rules that will result in powerful trading systems.

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At the end of this course, you will have your own trading system generator.

Who this course is for:

  • Traders and coders who want to learn new techniques to analyze the cryptocurrency markets

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Course content

1 section β€’ 8 lectures β€’ 49m total length

Content8 lectures β€’ 50min

  • Introduction00:47
  • Concepts04:36
  • Data Acquisition07:17
  • Technical Analysis Indicators13:13
  • Biased Data Labelling04:20
  • Rule Induction with RIPPER05:23
  • Backtesting our Model07:59
  • Reading and Trading our Rules06:19

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