Machine Learning Applied to Stock Crypto Trading Python

Machine Learning Applied to Stock & Crypto Trading – Python, available at $79.99, has an average rating of 4.63, with 111 lectures, based on 535 reviews, and has 4787 subscribers.

You will learn about Understand hidden states and regimes for any market or asset using Hidden Markov Models Discover optimum assets for pairs trading in ETFs, Stocks, Forex or Crypto using K-Means Clustering Condense information from a vast array of indicators with PCA Make objective future predictions on financial data with XGBOOST Train an AI Reinforcement Learning agent to trade stocks with PPO Test for market efficiency on any given asset Become familiar with Python Libraries including Pandas, PyTorch (for deep learning) and sklearn This course is ideal for individuals who are Retail traders who are looking to gain an objective edge in the financial markets or Enthusiasts who are looking for a practical and fun application of Machine Learning It is particularly useful for Retail traders who are looking to gain an objective edge in the financial markets or Enthusiasts who are looking for a practical and fun application of Machine Learning.

Enroll now: Machine Learning Applied to Stock & Crypto Trading – Python

Summary

Title: Machine Learning Applied to Stock & Crypto Trading – Python

Price: $79.99

Average Rating: 4.63

Number of Lectures: 111

Number of Published Lectures: 111

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 111

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand hidden states and regimes for any market or asset using Hidden Markov Models

Discover optimum assets for pairs trading in ETFs, Stocks, Forex or Crypto using K-Means Clustering

Condense information from a vast array of indicators with PCA

Make objective future predictions on financial data with XGBOOST

Train an AI Reinforcement Learning agent to trade stocks with PPO

Test for market efficiency on any given asset

Become familiar with Python Libraries including Pandas, PyTorch (for deep learning) and sklearn

Who Should Attend

Retail traders who are looking to gain an objective edge in the financial markets

Enthusiasts who are looking for a practical and fun application of Machine Learning

Target Audiences

Retail traders who are looking to gain an objective edge in the financial markets

Enthusiasts who are looking for a practical and fun application of Machine Learning

Gain an edge in financial trading through deploying Machine Learning techniques to financial data using Python. In this course, you will:

Discover hidden market states and regimes using Hidden Markov Models.

Objectively group like-for-like ETF’s for pairs trading using K-Means Clustering and understand how to capitalise on this using statistical methods like Cointegration and Zscore.

Make predictions on the VIX by including a vast amount of technical indicators and distilling just the useful information via Principle Component Analysis (PCA).

Use one of the most advanced Machine Learning algorithms, XGBOOST, to make predictions on Bitcoin price data regarding the future.

Evaluate performance of models to gain confidence in the predictions being made.

Quantify objectively the accuracy, precision, recall and F1 score on test data to infer your likely percentage edge.

Develop an AI model to trade a simple sine wave and then move on to learning to trade the Apple stock completely by itself without any prompt for selection positions whatsoever.

Build a Deep Learning neural network for both Classification and receive the code for using an LSTM neural network to make predictions on sequential data.

Use Python libraries such as Pandas, PyTorch (for deep learning), sklearn and more.

This course does not cover much in-depth theory. It is purely a hands-on course, with theory at a high level made for anyone to easily grasp the basic concepts, but more importantly, to understand the application and put this to use immediately.

If you are looking for a course with a lot of math, this is not the course for you.

If you are looking for a course to experience what machine learning is like using financial data in a fun, exciting and potentially profitable way, then you will likely very much enjoy this course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome and Course Introduction

Lecture 2: Where to Ask Questions

Lecture 3: Resources Folder Overview

Lecture 4: Plan of Attack – Course Structure

Chapter 2: Resources and Disclaimer

Lecture 1: Resources and Disclaimer

Lecture 2: Updated Resources (2023)

Lecture 3: 2024 Update: StratManager

Chapter 3: Primer Theory

Lecture 1: What is Machine Learning?

Lecture 2: A Brief Overview of Machine Learning

Lecture 3: Stage 1 – Data Ingestion

Lecture 4: Stage 2 – Feature Engineering

Lecture 5: Stage 3 – Model Selection and Training

Lecture 6: Stage 4 – Performance Evaluation

Lecture 7: Stage 5 – Model Deployment

Chapter 4: Environment Setup and Data Retrieval

Lecture 1: Option 1 – Google Colab

Lecture 2: Option 1 – Google Colab Reading Existing Notebooks

Lecture 3: Option 1 – Google Colab Solving for Pandas Datareader (with YFinance)

Lecture 4: Option 2 – Notebooks Installing Python and Anaconda

Lecture 5: Option 2 – Notebooks Creating a Conda Environment

Lecture 6: Where to Get Data

Lecture 7: Bonus: Getting Poloniex and Binance Data

Chapter 5: Primer Practical

Lecture 1: Python 101 – Variables and Arrays

Lecture 2: Python 101 – Dictionaries

Lecture 3: Python 101 – If Statements and Loops

Lecture 4: Python 101 – Functions and Classes

Lecture 5: Pandas 101 – Retrieve Data and Calculate Returns

Lecture 6: Pandas 101 – Structure Conditions and Iterations

Lecture 7: Pandas 101 – Value Extraction, Multiple Adj, Save and Load

Lecture 8: Backtesting 101 – Calculations and Strategy Returns

Lecture 9: Backtesting 101 – Metrics and Equity Curve

Lecture 10: Feature Engineering 101 – Data Preprocessing Part I

Lecture 11: Feature Engineering 101 – Data Preprocessing Part II

Lecture 12: Feature Engineering 101 – Applied Machine Learning

Lecture 13: Statistics – Testing for Market Efficiency Code Walkthrough

Chapter 6: Unsupervised Machine Learning – Hidden Markov Models

Lecture 1: Theory – Unsupervised Machine Learning Introduction

Lecture 2: Theory – Hidden Markov Models Intuition

Lecture 3: HMM – Initial Data Structuring

Lecture 4: HMM – Model Training

Lecture 5: HMM – Viewing Hidden States

Lecture 6: HMM II – Data Structuring

Lecture 7: HMM lI – Model Predictions

Lecture 8: HMM II – Structuring Backtest

Lecture 9: HMM II – Initial Metrics

Lecture 10: HMM II – Making Use of Hidden States

Lecture 11: HMM II – Saving Outputs

Chapter 7: Unsupervised Machine Learning – K-Means Clustering

Lecture 1: Theory – K-Means Clustering Intuition

Lecture 2: K-Means Setup

Lecture 3: K-Means Data Extraction

Lecture 4: K-Means Feature Engineering

Lecture 5: K-Means Applied and Visualized

Lecture 6: K-Means Removing Outliers

Lecture 7: Pairs Trading – Calculating Cointegrated Pairs

Lecture 8: K-Means – (Optional) – Visualizing TSNE Plot

Lecture 9: Pairs Trading – Calculating Spread and ZScore

Chapter 8: Unsupervised Learning – Principle Component Analysis

Lecture 1: Theory – Principle Component Analysis

Lecture 2: PCA – Data Extraction

Lecture 3: PCA – Data Preprocessing – Handling Stationarity

Lecture 4: PCA – Train Test Split

Lecture 5: PCA – Completion with Visualization

Lecture 6: Random Forest Classification – Results

Lecture 7: Unsupervised Learning – Summary

Chapter 9: Supervised Machine Learning

Lecture 1: Theory – Random Forests vs XGBOOST

Lecture 2: XGB Preprocessing – Data Ingestion

Lecture 3: XGB Preprocessing – Feature Expansion

Lecture 4: XGB Preprocessing – Stationarity

Lecture 5: XGB Preprocessing – Train Test Split

Lecture 6: XGB – Hyperparameter Optimization

Lecture 7: XGB – Initial Model Training

Lecture 8: XGB – Feature Selection

Lecture 9: XGB II – Train Test Split

Lecture 10: XGB II – Model Fitting

Lecture 11: XGB II – Model Evaluation – Measuring Loss and ROC

Lecture 12: XGB II – Model Evaluation – Performance Comparison

Lecture 13: XGB II – Model Evaluation – Summary Report

Lecture 14: XGB II – Model Evaluation – Confusion Matrix

Lecture 15: XGB II – Model Evaluation – View Tree

Chapter 10: Supervised Deep Learning – Basic Introduction

Lecture 1: Theory – Deep Learning Neural Network Anatomy

Lecture 2: Deep Learning – Feature Engineering Part I

Lecture 3: Deep Learning – Feature Engineering Part II

Lecture 4: Deep Learning – Neural Net and Data Build

Lecture 5: Deep Learning – Model Training

Lecture 6: Deep Learning – (Optional Code Walkthrough) – LSTM Sequential Model

Chapter 11: Reinforcement Learning

Lecture 1: Theory – Reinforcement Learning Complete Basics

Lecture 2: Theory – Proximal Policy Optimisation (PPO) Overview

Lecture 3: RL – First Steps

Lecture 4: RL – Sine Wave Construction

Lecture 5: RL – Environment Variables

Lecture 6: RL – Environment Reward Structure

Lecture 7: RL – Environment Observation Structure

Instructors

Shaun McDonogh
Lead Analyst and Full Stack (Python and React) Developer

Rating Distribution

1 stars: 11 votes

2 stars: 10 votes

3 stars: 33 votes

4 stars: 123 votes

5 stars: 358 votes

Frequently Asked Questions

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