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Artificial intelligence

in trading

By Dr. Ernest P. Chan

Learn end to end implementation of Artificial Intelligence in your trading strategy using techniques like Decision Trees and Neural Networks. With added expert insights from Dr. Ernest P. Chan’s experiences in Artificial intelligence and Quantitative trading over two decades.

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Dr. Ernest Chan
Course 1
Decision trees trading

This intermediate course covers various basics of decisions trees and how they can be implemented in your trading strategy. Also includes various methods to optimize the results.

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Course 2
Neutral network trading

This advanced course teaches how to identify trading opportunities from raw financial markets using neural networks and deep learning. And learn to create automated trading strategies.

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What Will You Learn?

course 1

Decision Trees Trading

Section 1 & 2: How Decision Trees decide?

Greedy Algorithm, Divide & Conquer approach; Splitting criteria, Entropy, Gini Index & Information Gain; Stopping criteria; Pruning methods; Parameters that affect the accuracy of the tree.


Section 3: Generate trading signals using Classification Trees

Code in Python to: create input data for the model; train the model; test the model & check for its prediction accuracy


Section 4: Create a Trading Strategy using Regression Trees

Predict next day returns using regression trees; compute Sharpe ratio and CAGR for the trading strategy


Section 5: Avoid overfitting with Parallel ensemble methods

Methods: Bagging, Random Subspace, Random Forest; Code using Scikit Learn and improve model performance


Section 6: Avoid underfitting with Sequential ensemble methods

Get a better fitted model for more accurate predictions using methods: Adaboosting, Gradient Boosting; Code using Scikit Learn


Section 7: Cross Validation & Hyperparameter tuning

From core concepts to applications of decision trees, learn to cross validate your model and tune to find the best hyperparameters

course 2

Neural Networks Trading

Section 1: Neural Networks in Trading

How a neuron works; Forward propagation, Backward propagation; Prediction of next day returns using the Sklearn library in Python


Section 2: Deep Learning models in Keras

Using Keras to create deep learning models; Features of Keras: dense, activation, dropout, model checkpoint; Cross Entropy loss function


Section 3: Recurrent Neural networks for financial markets data

Understanding the vanishing & exploding gradients problem; Recurrent neural networks as a solution to that problem; continue coding in Keras


Section 4: Long Short term memory model

Simplifying the working of the complex LSTM model; modelling it in Keras; and using it in financial markets to predict the entries and exits of trade


Section 5: Hyperparameter tuning in Keras

Automating the hyperparameter tuning in Keras using Grid search and cross validation techniques; understanding the different parameters which result in overfitting and poor results in live markets

Course Features

140 Quizzes and Interactive Coding Exercises

140 Quizzes and Interactive Coding Exercises

15 Downloadable Strategy Code

15 Downloadable Strategy Code

Certification From QuantInsti®

Certification From QuantInsti®

Lifetime Course Access

Lifetime Course Access

Save 30% on the bundle. Click below to see the price!

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The price of 2-course bundle is almost equal to price of individual course!

A Whole New Learning Experience With Quantra

Customized Video Experience
Customized Video Experience
Jupyter Notebook Documents
Jupyter Notebook Documents
Machine driven interactive Exercises
Machine driven interactive Exercises

Save 30% on the bundle. Click below to see the price!

enroll now
The price of 2-course bundle is almost equal to price of individual course!