Algorithmic Trading is seeing a rapid expansion of the application of artificial intelligence (AI) and Machine Learning (ML). These technological developments have completely transformed Algo trading. Making informed decisions requires carefully analyzing both current and historical market data.

In order to analyze data and make effective forecasts for effective trading decisions, artificial intelligence and Machine Learning are useful tools. Despite the challenge, we have curated a list of some of the best study aids from the countless available. Below is a list of the top 10 machine learning blogs.

Machine Learning Basics

Did you know, that entrepreneurs all across the world are switching to machine learning for business operations?

As ML can forecast outcomes without being specifically trained to do so, it can be an extremely valuable tool for all traders. Get your fundamentals down before utilizing Machine learning for trading. This blog is a complete guide to machine learning for trading and a must-read for aspiring trades.

Top 10 Machine Learning Algorithms for Beginners

Utilize machine learning to improve your trading. Understanding the fundamental reasoning behind certain well-liked and very resourceful machine learning algorithms will help you build the greatest machine learning algorithms by serving as a solid foundation. These algorithms have been employed by the trading community.

Machine Learning Classification Strategy using Python

In algorithmic trading, tons of datasets are handled. That's where machine learning comes in. It categorizes data into OHLC or price data, fundamental data, or alternative data such as tweets and news data. It creates a machine learning model and uses the model to forecast the future.

Discover more about models, the whole structure, categorization, and how they are implemented in Python! All are comprehensively covered in this magnificent article.

Artificial Intelligence and Machine Learning for Trading

In today's time, it is a must to know how AI and ML have nudged profitable trading. this article consists of an elaborative understanding of Artificial Intelligence and Machine Learning from the trading perspective. Understanding the effects uses, and applications of artificial intelligence and machine learning in trading will undoubtedly be beneficial to you.

Trading Using Machine Learning in Python

Interested in learning how and why to apply Machine Learning algorithms to your trading?

This blog aims to guide you to the best places to learn about machine learning applications in trading. By the end of this blog, you will have the ability to create and use machine learning algorithms. trading using Python. You will also find the prerequisites for creating ML models and the free Python codes

Data Preprocessing using Python, and Machine Learning with Examples

Data preprocessing is among the most important steps when it comes to trading. It is a basic requirement of any good machine learning model. Preprocessing the data implies using data that is easily readable by the machine learning model

With this course, you will equip yourself with the essential knowledge required for the two most important steps for any machine learning model, which are: Data cleaning and Feature engineering

Gold Price Prediction using Machine Learning Python

Is it possible to predict where the Gold price is headed?

Yes! Using machine learning regression techniques you can actually predict the price of one of the most precious metals, Gold.

This is a step-by-step guide to predicting the Gold price using machine learning in Python. This blog details Python libraries, and various concepts, create a linear regression model, develops python codes, and examines the expected results. For any trader, this is a must-read!

Predicting SL/TP Signal Using Machine Learning

The most challenging part of trading is to decide when to exit a position. This EPAT Project could help you predict when to exit a BUY/ SELL position ie. in predicting SL/TP signal without human intervention, by using Machine Learning and can detect SL/TP with an accuracy of 65.86%. Downloadable data is available!

Prediction of the price trend of Metals with Machine Learning

Another project by an EPATian. This project will help you learn how you can predict the price trend of metals using Machine Learning in your trading practice. It will take you stepwise, using a computer vision to create a Convolutional Neural Network (CNN), which can predict the price movement.

The complete data files and Python code used in this project are also available in a downloadable format at the end.

Free Resources to Learn Machine Learning for Trading

Speaking about the financial technology domain, algorithmic trading practice is extremely efficient with machine learning algorithms. There are various resources available to learn machine learning for trading, but this article aims to make the free resources to learn Machine Learning for Trading accessible to you.

These free resources are divided into the following categories - Courses, Blogs, E-Books, Research papers, and Videos

Honourable Mentions

Machine Learning K-Nearest Neighbors (KNN) Algorithm in Python

Machine learning algorithms are used for regression and classification problems. This article will walk you through the Python K-Nearest Neighbors (KNN) concept in detail.

KNN algorithms employ data to categorize fresh data points based on similarity metrics. A step-by-step guide to implement KNN machine learning algorithms (Python codes available).

Machine Learning Logistic Regression Python

Through this blog, you will learn about linear and logistic regression concepts. It will walk you through everything from installing the pre-packed Python Machine Learning libraries to using the Logistic Regression classifier to forecast stock price movement to developing ML models in Python and utilizing them for trading.

Optimal Portfolio Construction Using Machine Learning

A portfolio management and risk management technique. In this article, you will learn about the Stereoscopic Portfolio Optimization (SPO) framework and how it can improve a quantitative trading strategy.

Cross Validation In Machine Learning Trading Models

Cross-validation in machine learning is a technique that provides an accurate measure of the performance of a machine learning model. After reading this, you can determine if your model is good at predicting which signal and performance of the model in different stress scenarios.

There are numerous other blogs and tutorials available to help you explore the fascinating field of Machine Learning. You can check them all out on our blog here.

We sincerely hope you enjoyed this quick look at the top 10 blogs about machine learning that our readers found to be the most popular in 2022. Please comment and let us know the subjects you'd want to see us cover in blogging in 2023.

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