QuantInsti – Trading with Machine Learning: Classification and SVM

Description

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QuantInsti – Trading with Machine Learning: Classification and SVM

Learn to use SVM on financial markets data and create your own prediction algorithm. The course covers classification algorithms, performance measures in machine learning, hyper-parameters and building of supervised classifiers.

LIVE TRADING

• Explain the concept of Support Vectors
• Explain what a hyperplane is
• Use cross-validation to tune the hyper-parameters of a support vector machine
• Code a trading strategy to predict the next day’s trend using a Support Vector Classifier
• Paper trade and analyze the strategies and apply in live markets without any installations or downloads

SKILLS COVERED

Machine Learning
SVM
Trend
Validation
Hyper-parameters

Math Concepts
Classification
Hyperplane
Differentiation
Functions

Python
Pipeline
Standard Scaler
TA-Lib
Randomized Search CV
Matplotlib

COURSE FEATURES 

• Interactive Coding Practice
• Trade and Learn Together

PREREQUISITES

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.

SYLLABUS

Introduction
This section presents the topic of machine learning classification, along with its types and applications. It covers technical indicators such as RSI, Parabolic SAR, and ADX.
Getting Started
Quantra Features and Guidance
What is Classification?
Steps to Classify
Teaser on Classification
Types and Applications
Technical References
Importing Libraries
Technical Indicators – Part A
Know Your Technical Indicators
Technical Indicators – Part B
Applying Technical Indicators
Dropping Values
Creating An Indicator
Recap

Binary Classification
This section helps develop an understanding of binary classification, its uses and the math behind it. It also includes types of classifiers like sigmoid, tanh and gradient descent.
What is Binary Classification?
Types of Classification
How to Classify?
Uses of Binary Classification
The Math in Classification
Decision Boundary
Equation of Classification
Choosing the Learning Rate
Applying Classification
Binary Classification
Why Use Technical Indicators?
Recap

Multiclass Classification
This section delves into the topic of multiclass classification and explains the concepts of one versus all algorithm, one-hot encoding, and Softmax Function. It also covers performance measures in machine learning.
What is Multiclass Classification?
Identify the Classification

Multiclass Classification
What is the One Vs All Algorithm?
Which Class Does It Belong To?
One Vs All
Probability Concepts Part 1
Probability Basics
More Probability
Probability Concepts Part 2
Advanced Probability
Applying Probability
Performance Measure in ML
Define Accuracy
What is F1 Score?
One Hot Encoding and Softmax
Encoding
Recap

Support Vector Machine
This section explains the concept of Support Vector Machine (SVM), the mathematics behind it, what support vectors are, and the parameters involved in SVM.
What is Support Vector Machine?
Mathematics Behind SVM
What Are Support Vectors?
Separating Data
The Parameters in SVM
Degree As a Parameter
Effect of C
Recap

Prediction and Strategy
This section involves the building of a predictive model using SVM, and an intraday trading strategy based on this predictive model. It includes the Python codes for the same and also helps in understanding the different hyper-parameters used for optimising algorithms and analysing the strategy performance.
Code Overview
How to Use Jupyter Notebook?
Trading Strategy Classification
Frequently Asked Questions
How to Prepare the Data?
Data Resolution
What Are the Inputs?
Trimming the Data
Classifying the Returns
Modifying a Dataframe
Pipelines and Steps
Hyperparameter
Training and Fetching
Support Vector Classification
Testing and Predicting
Analysing the Performance
Recap
FAQs on Live Trading
Test on Classification and SVM

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 Classification Strategy
FAQs for Live Trading on Blueshift

Live Trading on IBridgePy
Section Overview
Live Trading Overview
Vectorised vs Event Driven
Process in Live Trading
Real-Time Data Source
Code Structure
API Methods
Schedule Strategy Logic
Fetch Historical Data
Place Orders
IBridgePy Course Link
Additional Reading
Frequently Asked Questions

Paper and Live Trading
In this section, a live trading strategy template will be provided to you. You can tweak the strategy template to deploy your strategies in the live market!
Template Documentation
Template Code Files

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

ABOUT AUTHOR

QuantInsti®
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.

WHY QUANTRA®?

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

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