QuantInsti – Trading with Machine Learning: Regression



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QuantInsti – Trading with Machine Learning: Regression

This course is perfect to create your first trading strategy using a machine learning algorithm. Learn in a step-by-step fashion: acquire data, pre-process it, train and test the machine learning regression model, and predict the stock prices. Hands-on coding assistance provided.


  • Code a trading strategy to predict the next day’s highs and lows
  • Explain the concept of regression
  • Explain how gradient descent helps in cost function optimization
  • Perform hyper-parameter tuning
  • Paper trade and live trade your strategies without any installations or downloads


Machine Learning

Math Concepts
Gradient Descent
Cost function optimization

Pandas, NumPy


  • Interactive Coding Practice
  • Trade and Learn Together

Prior experience in programming is required to fully understand the implementation of machine learning algorithm taught in the course. However, Python programming knowledge is optional. If you want to be able to code and implement the machine learning strategies in Python, then you should be able to work with ‘Dataframes’. These skills are covered in the course ‘Python for Trading’. Basic knowledge of machine learning algorithms and train and test datasets is a plus. These concepts are covered in our free course: Introduction to Machine Learning.


Problem Statement
This is an introduction to the course which discusses the topics which will be covered further.
Getting Started
Quantra Features and Guidance

Introduction to Data Generation
This section explains how to generate and import data, how to use scikit-learn, and how to create useful indicators for prediction in the algorithm.
Introduction to Scikit-Learn
Resizing the Data
Remove the NaN
Search the Hyperparameters
Importing Data
Import Data and Drop Missing Values
Importing Libraries
Dropping Missing Values
Input Parameters
Create Input and Output Parameters
Create a Moving Average
Move the Data
Creating Indicators

Data Preprocessing
This section specifies the importance of data pre-processing, demonstrates how to use Scikit-learn for data pre-processing, splitting the data into train and test, and fitting the regression function. It also includes important concepts like hyper-parameters, cross-validation, grid search, randomized search etc.
Creating X and Y Datasets
Why Do Data Pre-processing?
Why Scale the Data?
Why Center the Data?
Data Preprocessing
Input/Output Datasets
Imputer Function
Concept of Pipeline and Steps
Why Use a Pipeline?
Concept of Pipeline
Creating Steps
Instantiating Pipeline
Choose the Learning Model
What Are Hyperparameters?
Cross Validation, Test and Train
Tuning the Hyperparameters
How to Handle Grid Size?
Grid Search and Randomized Search
Properties of a Grid Search
Which Search to Choose?
Cross Validation, Test and Train
Gridsearchcv Function
Train and Test Split
Fit Regression Equation
Recap of Data Pre-Processing

This section delves into the types of machine learning regression, explains mathematical concepts behind regression function such as the gradient descent and cost function optimization, and demonstrates how to predict SPY movements.
Introduction to Linear Regression
Linear Regression
Interpreting Regression
Errors and Residuals
Types of Variation
Regression Application
Assumptions in Linear Regression
Assumptions of Linear Regression
Linear Regression
Solve the Regression
Calculate the Covariance
Cost Function and Gradient Descent
How Does Gradient Descent Work?
Cost Function and Gradient Descent
Understanding the Cost Function
Do You Know Gradient Descent?
Multivariate Linear Regression
Linear Regression and Predicting GLD Movement
Best Fit Variable
Linear Regression
Calling Imputer Function
Predicting SPY Movement
Recap of Regression
Additional Reading

Bias and Variance
This section provides information about the concepts of bias and variance, overfitting and underfitting, and regularization to optimize your models.
Bias and Variance
Bias or Variance?
Overfitting and Underfitting
Overfitting is Caused By?
Failure of ML Algorithms
Concept of Regularization
Lamda of Regularization
L2 Regularization
Recap Video

Applying the Prediction
This section explains how to apply prediction and assess the models.
Prediction and Model Assessment
Why Use Zero?
High of the Day Is?
Creating Column
Predict the Next Day’s High and Low
Predicted High

Creating the Algorithm
This section focuses on creating the algorithm and coding it in Python using the concepts learnt previously. It also explains log returns, signal generation, and Sharpe ratio to gauge the performance of the trading strategy.
Trading Strategy
How to Use Jupyter Notebook?
Frequently Asked Questions
Data Preparation
Data Preprocessing and Prediction
Strategy Analytics
Performance Analysis
Signal Generation
Sharpe Ratio

Live Trading on Blueshift
This section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.
Section Overview
Live Trading Overview
Vectorised vs Event Driven
Process in Live Trading
Real-Time Data Source
Blueshift Code Structure
Important API Methods
Schedule Strategy Logic
Fetch Historical Data
Place Orders
Backtest and Live Trade on Blueshift
Additional Reading
Blueshift Data FAQs

Live Trading Template
Blueshift Live Trading Template
Paper/Live Trading Regression Trading Strategy
FAQs for Live Trading on Blueshift

Run Codes Locally on Your Machine
Learn to install the Python environment in your local machine.
Python Installation Overview
Flow Diagram
Install Anaconda on Windows
Install Anaconda on Mac
Know your Current Environment
Troubleshooting Anaconda Installation Problems
Creating a Python Environment
Changing Environments
Quantra Environment
Troubleshooting Tips For Setting Up Environment
How to Run Files in Downloadable Section?
Troubleshooting For Running Files in Downloadable Section

Downloadable Resources
This section concludes the course and provides downloadable strategy codes and an e-book with the course contents.
Python Codes and Data


QuantInsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.


  • Gain more in less time
  • Get taught by practitioners
  • Learn at your own pace
  • Get data & strategy models to practice on your own
  • Frequently Asked Questions:

    1. Innovative Business Model:
      • Embrace the reality of a genuine business! Our approach involves forming a group buy, where we collectively share the costs among members. Using these funds, we purchase sought-after courses from sale pages and make them accessible to individuals facing financial constraints. Despite potential reservations from the authors, our customers appreciate the affordability and accessibility we provide.
    2. The Legal Landscape: Yes and No:
      • The legality of our operations falls into a gray area. While we lack explicit approval from the course authors for resale, there’s a technicality at play. When procuring the course, the author didn’t specify any restrictions on resale. This legal nuance presents both an opportunity for us and a boon for those seeking budget-friendly access.
    3. Quality Assurance: Unveiling the Real Deal:
      • Delving into the heart of the matter – quality. Acquiring the course directly from the sale page ensures that all documents and materials are identical to those obtained through conventional means. However, our differentiator lies in going beyond personal study; we take an extra step by reselling. It’s important to note that we are not the official course providers, meaning certain premium services aren’t included in our package:
        • No coaching calls or scheduled sessions with the author.
        • No access to the author’s private Facebook group or web portal.
        • No entry to the author’s exclusive membership forum.
        • No direct email support from the author or their team.

      We operate independently, aiming to bridge the affordability gap without the additional services offered by official course channels. Your understanding of our unique approach is greatly appreciated.

    Refund is acceptable:

    • Firstly, item is not as explained
    • Secondly, Item do not work the way it should.
    • Thirdly, and most importantly, support extension can not be used.

    Thank you for choosing us! We’re so happy that you feel comfortable enough with us to forward your business here.

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