Neural Networks in Trading by Dr. Ernest P. Chan

Description

Neural Networks in Trading by Dr. Ernest P. Chan – Instant download!

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.

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

Math Concepts  Mean Squared Error Loss Function Sigmoid Function Cross Entropy

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

Python  Neural_network R2scorer Accuracy_score Keras Pickle

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

Neural Networks in Trading by Dr. Ernest P. Chan, what is it included (Content proof: Watch here!)

1. Introduction – 3m 54s
2. Quantra Features and Guidance – 4m 9s
3. Neural Networks Intuition – 1m 55s
4. Linear Regression Revisited – 3m
5. Hidden Layers – 2m
6. Structure of a Neural Network – 2m
7. Understanding Forward Propagation – 10m
8. Forward Propagation Mechanism – 2m
9. Backpropagation – 2m 56s
10. Calculate the MSE – 2m
11. Identify Loss Functions – 2m
12. Loss Optimisation – 2m
13. Identify Optimisation Method – 2m
14. Function Derivative Chain Rule – 10m
15. Identify Derivative Equation – 2m
16. Math behind Back-Propagation – 10m
17. Implement a MLPClassifier – 2m 26s
18. Identify the Sigmoid Graph – 2m
19. Output of a Sigmoid Function – 2m
20. How to Use Jupyter Notebook? – 1m 54s
21. MLPClassifier Hands-on – 10m
22. Import Boeing Co Data – 5m
23. Define Predictor Variable – 5m
24. Calculate Future Returns – 5m
25. Define Target Variable – 5m
26. Train-Test Split – 5m
27. Feature Scaling – 5m
28. Loss Optimisation Algorithm – 2m
29. What is Sigmoid? – 2m
30. Hidden Layer Sizes – 2m
31. MLPClassifier Definition – 5m
32. Predict Market Movement – 5m
33. Generate Evaluation Metrics – 2m
34. Live Trading on Blueshift
35. Live Trading Template
36. Deep Learning in Trading
37. Recurrent Neural Networks
38. Long Short Term Memory Unit (LSTMs)
39. Cross Validation in Keras
40. Challenges in Live Trading
41. Python Installation
42. Paper and Live Trading
43. Downloadable Resources

WHY QUANTRA?

USER TESTIMONIALS

Abinash Tripathy  Accountant at HCL Peripherals, India Let me explain why I gave this course 5 stars. When I first bought this course, it lacked the implementation section. I complained about it and they fixed it, thereby actually making the course worth for me. Thank you very much Team Quantra. If you have no clue what Neural Networks in Trading is and want to learn about it then this is the course for you. Highly Recommended, especially due to the insane support they provide in case you have any issue related to the course.

Sergei Cherkasov  Trader, Russia I had two Dr. Chans courses on artificial intelligence and I want to rate them as really good. For me it was a good start in machine learning. Learned a lot here as these courses are made well. My very big desire for these courses is to have paper/real trading examples for every strategy and model that was in the course, as it will help learners to learn faster and prosper at trading!

Sergei Belov  United States A very good explanation of RNNs and LSTMs as well as hyper-parameter tuning.