Description
Learning Track Machine Learning & Deep Learning in Financial Markets – QuantInsti, Multi Commodity Exchange QuantInsti & Dr. Ernest P. Chan – Learning Track Machine Learning & Deep Learning in Financial Markets – QuantInsti, Learning Track Machine Learning & Deep Learning in Financial Markets – QuantInsti download, Multi Commodity Exchange QuantInsti & Dr. Ernest P. Chan – Learning Track Machine Learning & Deep Learning in Financial Markets – QuantInsti review, Learning Track Machine Learning & Deep Learning in Financial Markets – QuantInsti free torent
Multi Commodity Exchange, QuantInsti & Dr. Ernest P. Chan – Learning Track Machine Learning & Deep Learning in Financial Markets – QuantInsti
A highly recommended track for those interested in Machine Learning and its applications in trading. From simple logistic regression models to complex LSTM models, these courses are perfect for beginners and experts. Learn to tune hyperparameters, gradient boosting, ensemble methods, advanced techniques to make robust predictive models, use unsupervised learning in trading to enhance your algorithms.
LIVE TRADING
Master artificial intelligence techniques and packages essential for financial markets prediction.
Hands-on training on creating predictive models using Regression algorithm, Support Vector Classifier, Decision Trees, Random Forests, Neural Networks, Activation Layers and more.
Understand why and how predictive models fail and what can be done to improve them; cross-validation techniques, gradient boosting, ensemble methods, hyper-parameter tuning and more.
Use cutting-edge research in artificial intelligence to create improved models: Recurrent Neural Networks, Long Short Term Memory Unit.
Use predictive models in live trading. Save and update your model regularly for live trading. Know how to use the models for live trading.
Describe, implement and list the differences between the workings of k-means clustering algorithm and DBSCAN clustering algorithm.
Code and fine-tune various machine learning algorithms from simple to advance in complexity. If you consider machine learning as an important part of the future in financial markets, you can’t afford to miss this specialization.
SKILLS COVERED
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).
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