Multi Commodity Exchange & Ernest Chan – QuantInsti – Learning Track Machine Learning & Deep Learning in Financial Markets

Description

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Multi Commodity Exchange & Ernest Chan – QuantInsti – Learning Track Machine Learning & Deep Learning in Financial Markets

LEARNING TRACK

Machine Learning & Deep Learning in Financial Markets

FOUNDATION

Python For Trading!

Introduction to Machine Learning for Trading

BEGINNER

Trading with Machine Learning: Regression

INTERMEDIATE

Trading with Machine Learning: Classification and SVM

Decision Trees in Trading

Unsupervised Learning in Trading

ADVANCED

Neural Networks in Trading

COURSE FEATURES

Faculty Support on Community

Interactive Coding Practice

Capstone Project using Real Market Data

Trade and Learn Together

Get Certified

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, you should be able to work with `Dataframes`. These skills are covered in the course `Python for Trading` which is a part of this learning track.

SYLLABUS

Course 1

Python For Trading!

Introduction to Course

Introduction to Python

Functions, Variables and Objects

Data Structures: Lists and Dict

Data Structures: Series and Dataframe

Financial Market Data

Dealing With Financial Data

Data Visualisation

Relative Strength Index

Other Technical Indicators

Backtesting

Performance Metrics

Live Trading on Blueshift

Live Trading Template

Run Codes Locally on Your Machine

Python Codes and Data Files

Course 2

Introduction to Machine Learning for Trading

Introduction

Machine Learning

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Predict Trend Using Classification

Live Trading on Blueshift

Live Trading Template

Natural Language Processing

Data & Feature Engineering

Run Codes Locally on Your Machine

Summary

Course 3

Trading with Machine Learning: Regression

Problem Statement

Introduction to Data Generation

Data Preprocessing

Regression

Bias and Variance

Applying the Prediction

Creating the Algorithm

Live Trading on Blueshift

Live Trading Template

Run Codes Locally on Your Machine

Downloadable Resources

Course 4

Trading with Machine Learning: Classification and SVM

Introduction

Binary Classification

Multiclass Classification

Support Vector Machine

Prediction and Strategy

Live Trading on Blueshift

Live Trading Template

Run Codes Locally on Your Machine

Downloadable Resources

Course 5

Decision Trees in Trading

Introduction To Decision Trees

Splitting, Stopping and Pruning Methods

Classification Model

Live Trading on Blueshift

Live Trading Template

Regression Trees

Parallel Ensemble Methods

Sequential Ensemble Methods

Cross Validation and Hyperparameter Tuning

Challenges in Live Trading

Run Codes Locally on Your Machine

Downloadable Code

Course 6

Unsupervised Learning in Trading

Introduction to the Course

Introduction to Unsupervised Learning

Clustering

K-Means Clustering

K-Means for Financial Data

Scaling the Data

Feature Selection

Selecting Clusters for K-Means

Analysing Clusters: Hit Ratio

Analysing Clusters: Skewness

Putting It All Together

Curse of Dimensionality

Introduction to Principal Component Analysis

Maths Behind Principal Component Analysis

Principal Component Analysis

Application of Unsupervised Learning for Pairs Trading

DBSCAN

Pairs Trading using Clustering Algorithms

Run Codes Locally on Your Machine

Capstone Project

Automate Trading Strategy Using IBridgePy

Course Summary

Course 7

Neural Networks in Trading

Neural Networks

Live Trading on Blueshift

Live Trading Template

Deep Learning in Trading

Recurrent Neural Networks

Long Short Term Memory Unit (LSTMs)

Cross Validation in Keras

Challenges in Live Trading

Run Codes Locally on Your Machine

Paper and Live Trading

Downloadable Resources

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.

Dr. Ernest P. Chan

Dr. Ernest Chan is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor. QTS manages a hedge fund as well as individual accounts. He has worked in IBM human language technologies group where he developed natural language processing system which was ranked 7th globally in the defense advanced research project competition. He also worked with Morgan Stanley’s Artificial intelligence and data mining group where he developed trading strategies.

Multi Commodity Exchange

The Multi Commodity Exchange of India Limited (MCX), India’s first listed exchange, is a state-of-the-art, commodity derivatives exchange that facilitates online trading of commodity derivatives transactions, thereby providing a platform for price discovery and risk management. The Exchange, which started operations in November 2003, operates under the regulatory framework of Securities and Exchange Board of India (SEBI).

Why quantra

• Gain more in less time
• Get taught by practitioners
• Learn at your own pace
• Get data & strategy models to practice on your ownCommonly Asked Questions:
  1. Business Model Innovation: Acknowledge the reality of a legitimate enterprise! Our approach involves the coordination of a collective purchase, in which the costs are shared among the participants. We utilize this cash to acquire renowned courses from sale pages and make them accessible to individuals with restricted financial resources. Our clients appreciate the affordability and accessibility we provide, despite the authors’ concerns.
  2. QuantInsti – Learning Track Machine Learning & Deep Learning in Financial Markets Course
  • There are no scheduled coaching calls or sessions with the author.
  • Access to the author’s private Facebook group or web portal is not permitted.
  • No access to the author’s private membership forum.
  • There is no direct email support available from the author or their team.