Machine Learning

Learn basics to advanced concepts in machine learning and its implementation in financial markets. Includes deep learning, tensor flows, installation guides, downloadable strategy codes along with real-market data.

Top Picks

Reinforcement Learning in Finance: Resources and Expert Advice from Paul Bilokon

Discover expert resources on Reinforcement Learning in Finance, featuring insights and strategies from Dr. Paul Bilokon. Learn how RL is transforming trading with AI-driven solutions and practical tools....
14 min read

Neural Network In Python: Types, Structure And Trading Strategies

What is a neural network and how does it work? How can you create a neural network with the famous Python programming language? In this tutorial, learn the concept of neural networks, their work, and their applications along with Python in trading....
19 min read

Machine Learning Basics: Components, Application, Resources and More

Machine Learning basics, algorithms, concepts and techniques are the talk of the day! Machine Learning has dramatically altered every field. Dive into the basics of machine learning, and learn all about it....
14 min read

An Introduction to Unsupervised Learning for Trading

Learn about the basics of unsupervised learning algorithms and their use cases in Finance/Investment/Trading with examples in Python....
15 min read

Gold Price Prediction: Step By Step Guide Using Python Machine Learning

A guide about Gold price using prediction using machine learning in Python. Learn about defining the variables to create a linear regression model, and eventually predicting the Gold ETF prices....
11 min read

Machine Learning Strategy using Blueshift Visual Programming

We use Blueshift Visual Programming to create a machine learning strategy without writing any code! We also backtest and then live trade the strategy....
10 min read

Introduction to Support Vector Machines

In this article, we will understand how support vector machines work and its application in trading. We will also go through the maths behind the SVM and the process of using it in a non-linear model....
15 min read

Decision Tree For Trading Using Python

We will focus on trading and how to use decision trees to find trading rules that allow us to gain an edge in the market....
14 min read

Machine Learning

Simple and Multiple Linear Regression in Algorithmic Trading: A Practical Guide for Quants

Explore how linear regression powers trading strategies in quantitative finance. Understand OLS, model assumptions, Python code for stock prediction, and real-world use cases for building and evaluating trading models....
Machine Learning
23 min read

Beginner's Guide to Machine Learning Classification in Python

Build classification trading strategy in Python for predicting the S&P500 price from scratch. Learn how to handle binary and multiclass problems using key ML algorithms like SVM, with a full coding workflow—from data prep and training to evaluation and visualization....
Machine Learning
6 min read

Building a Trading Strategy using Bias-Variance Decomposition

Explore the bias-variance tradeoff in machine learning for trading strategies. Learn how to build and backtest a trading model using PCA, VIF, and bias-variance decomposition to optimize performance and mitigate overfitting....
Machine Learning
17 min read

Reinforcement Learning in Trading

Learn how reinforcement learning is applied in stock trading with Q-learning, experience replay, and advanced techniques. Explore its edge over traditional ML in building trading strategies....
Machine Learning
9 min read

Building Blocks of Bias-Variance Tradeoff for Trading the Financial Markets

Explore bias-variance tradeoff in machine learning for trading. Learn how underfitting, overfitting, and error decomposition impact model performance and strategy development in finance....
Machine Learning
12 min read

A novel drift detection algorithm for machine learning in trading

Explore the autoregressive based drift detection method (ADDM) for identifying concept drift and market regime changes in trading. Learn how ADDM enhances ML strategies and supports backtesting in finance....
Machine Learning
14 min read

Walk-Forward Optimization in Python for ML Models

Learn how to apply Walk-Forward Optimization (WFO) in Python using XGBoost for stock price prediction. Understand how WFO helps manage concept drift and maintain model accuracy in dynamic financial markets....
Machine Learning
9 min read

Walk-Forward Optimization (WFO): A Framework for More Reliable Backtesting

Learn how Walk-Forward Optimization (WFO) works, its limitations, and how to implement it for backtesting trading strategies. Enhance your strategy testing with a structured framework for more reliable results....
Machine Learning
5 min read

DBSCAN Vs K-Means

K-Means has its limitations DBSCAN solves them. This guide explains how DBSCAN works, its advantages over K-Means, and how to implement it in Python....
Machine Learning
9 min read

The TGAN Algorithm for Trading

Learn about the TGAN algorithm, how it creates synthetic data, and its use in backtesting trading strategies. Explore the benefits, challenges, and applications of TGAN in time-series analysis....
Machine Learning
11 min read