Dr. Ernest P. Chan – Neural Networks in Trading

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Dr. Ernest P. Chan – Neural Networks in Trading

Recommended for programmers and quants to implement neural network and deep learning in financial markets. Offered by Dr. Ernest Chan, learn to use advanced techniques such as LSTM, RNN in live trading.

• Explain what a neural network is and how it works
• Code a neural network model using Sklearn
• Describe a Deep Neural Network
• List the various activation functions used
• Code a market trend predicting strategy
• Describe a Recurrent Neural Network
• Analyze an LSTM cell and its working
• Code a market close-price predicting strategy
• Perform a cross-validation to tune the hyper-parameters of a deep learning model
• Paper trade and live trade your strategies without any installations or downloads

SKILLS COVERED

Machine Learning

• Cross Validation
• Hyper-parameters
• Recurrent Neural Networks
• Long Short Term Memory

Math Concepts

• Mean Squared Error
• Loss Function
• Sigmoid Function
• Cross Entropy

Python

• Neural_network
• R2scorer
• Accuracy_score
• Keras
• Pickle

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 Tress in Trading
• Unsupervised Learning in Trading

ADVANCED

• Neural Networks in Trading

PREREQUISITES

You should have a basic knowledge of machine learning algorithms and training and testing datasets. These concepts are covered in our free course ‘Introduction to Machine Learning’. Prior experience in programming is required to fully understand the implementation of Artificial Intelligence techniques covered 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’ and ‘Sklearn’ library. Some of these skills are covered in the course ‘Python for Trading’.

SYLLABUS

1. Neural Networks
2. Live Trading on Blueshift
3. Live Trading Template
4. Deep Learning in Trading
5. Recurrent Neural Networks
6. Long Short Term Memory Unit (LSTMs)
7. Cross Validation in Keras
8. Challanges in Live Trading
9. Run Codes Locally on Your Machine
10. Paper and Live Trading
11. Downloadable Resources

ABOUT AUTHOR

Dr. Ernest P. Chan

Dr. Ernest P. 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 which was ranked 7th globally in the defense advanced research project competition. He aslo worked with Morgan Stanley’s Artificial intelligence and data mining group where he developed trading strategies.

Commonly 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. Neural Networks in Trading 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.