Ernest Chan – QuantInsti – Decision Trees in Trading

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Ernest Chan – QuantInsti – Decision Trees in Trading

Decision Trees in Trading
Offered by Dr. Ernest Chan, learn to predict markets and find trading opportunities using AI techniques. Train the algorithm to go through hundreds of technical indicators to decide which indicator performs best in predicting the correct market trend. Further, optimize these AI models and learn how to use them in live trading.

LIVE TRADING

• Create a machine learning trading strategy using Decision Trees and ensemble methods
• Identify best trading indicators and create trading rules
• Create automated trading strategies
• Enhance your existing prediction models using advanced techniques
• Evaluate performance of trading strategies
• Apply and analyze strategies in the live markets without any installations or downloads

COURSE FEATURES 

• Interactive Coding Practice
• Trade and Learn Together

PREREQUISITES
You should have basic knowledge of machine learning algorithms and train and test datasets. These concepts are covered in our free course ‘Introduction to Machine Learning’. Prior experience in programming is required to fully understand 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

Introduction To Decision Trees

This section introduces the topic and explains the basic structure of a decision tree. It also covers the concept of Decision Tree Inducers.

Introduction
4m 11s

Quantra Features and Guidance
3m 48s

Introduction To Decision Trees
3m 24s

Types Of Decision Trees
2m

Variables Of A Classification Tree
2m

Prediction With A Classification Tree
2m

Decision Tree Inducers
2m 49s

Split Parameter In A Decision Tree
2m

Leaf Parameter In A Decision Tree
2m

Greedy Approach In A Decision Tree
2m

Depth Of A Decision Tree
1m

Splitting, Stopping and Pruning Methods
This section describes some of the splitting measures like Gini Impurity and Mean Squared Error. It also includes stopping and pruning measures.

Splitting Measures
5m 8s

Entropy At An Internal Node
2m

Calculate Gini Index
2m

Information Gain At An Internal Node
2m

Loss Function
5m

Stopping Criteria And Pruning
2m 45s

Different Stopping Criteria
2m

K-Fold Cross Validation Pruning
2m

Classification Model
In this section, you will learn to develop and implement a classification model in Python code.

Classification Decision Tree Model – Part 1
3m 31s

Splitting The Data
2m

Using Balanced Class Weights
2m

Classification Decision Tree Model – Part 2
3m 19s

Identify The Pure Node
2m

Rule That Leads To A Pure Node
2m

Classification Report
2m

How to Use Jupyter Notebook?
1m 54s

Classification Decision Tree Model
10m

Import Data
5m

Define Predictor Variable
5m

Compute Future Returns
5m

Define Target Variable
5m

Create Train Slice
5m

Define Decision Tree Classifier
5m

Fit And Make Predictions
5m

Evaluate Model Performance
5m

Class Weights In Decision Trees
5m

Class Weights – 6
5m

Recap
1m 28s

Graphviz Installation Steps
10m

Live Trading on Blueshift
This section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.

Section Overview
2m 19s

Live Trading Overview
2m 41s

Vectorised vs Event Driven
2m

Process in Live Trading
2m

Real-Time Data Source
2m

Blueshift Code Structure
2m 57s

Important API Methods
10m

Schedule Strategy Logic
2m

Fetch Historical Data
2m

Place Orders
2m

Backtest and Live Trade on Blueshift
4m 5s

Additional Reading
10m

Blueshift Data FAQs
10m

Live Trading Template
Blueshift Live Trading Template

Classification Decision Tree Strategy Template
10m

FAQs for Live Trading on Blueshift
10m

Regression Trees
In this section, you will learn about regression trees and how to build a trading model using them. It demonstrates the Python code to implement this and also calculates the strategy returns.

A Trading Model Using: Regression Trees
4m 14s

Properties Of A Regression Trees
2m

What Is Not True About A Regression Tree?
2m

Stopping Conditions For The Regression Tree
2m

Trading Rule Model In Regression Tree
2m

Output Of Trading Model
2m

Regression Tree Model
10m 10s

Strategy Analytics
10m

Sklearn Method To Create Regression Tree
2m

Input To DecisionTreeRegressor
2m

Function Used To Predict The Target Variable
2m

Predict Signal
5m

Strategy Returns
5m

Sharpe Ratio
5m

Regression Tree Strategy Template
10m

Precap To Next Section
1m44s

Parallel Ensemble Methods
This section discusses ensemble methods like bagging, along with the Python code for the same. It also talks about the Random Forest algorithm which uses the ensemble method and also provides the Python code for the same.

Bagging
4m 37s

Parallel Ensemble Methods
2m

Properties Of Bagging
2m

Possible Outcome
2m

Final Output Of The Ensemble Model
2m

Bagging Model
5m

Define A Bagging Model
5m

Next Steps
2m

Bagging Performance
2m

Random Subspace And Random Forest
1m 52s

Bagging Vs Random Subspace Methods
2m

Random Forest Method
2m

Ensemble Vs Standalone Methods
2m

Random Subspace Method
2m

What Does Random Subspace Method Reduce?
2m

Random Subspace Model
5m

Randomly Sample Features
2m

Number Of Predictors To Use
2m

Random Subspace
5m

Random Forest Model
5m

Not A Valid Parameter
2m

Valid Parameters
2m

Random Forest
5m

Sequential Ensemble Methods
This section takes you through the concept of Boosting and how it works. It also explains about AdaBoosting and Gradient Boosting along with their Python codes.

Boosting
3m 8s

Overfitting Vs Underfitting
2m

Properties Of Boosting
2m

AdaBoosting Model
5m

Maths Behind AdaBoost

AdaBoosting
5m

Gradient Boosting Model
5m

Gradient Boosting
5m

Cross Validation and Hyperparameter Tuning
This section delves into the advanced techniques like cross-validation and hyper-parameter tuning which can be used to enhance your existing prediction models or any other machine learning models.

Cross Validation
2m 27s

Correct Use Of Cross Validation
2m

Larger K-value In Cross Validation
2m

K-Fold Cross Validation
5m

Cross Validation Method
2m

Number Of Folds
2m

Cross Validation Score
5m

Hyperparameters
2m

Structural Choices In Machine Learning
2m

Properties Of Hyperparameter
2m

Hyperparameters Tuning
5m 12s

Methods For Tuning Hyperparameters
2m

Dimensionality Of The Grid Increases
2m

Properties Of Grid Search
2m

Hyperparameter Tuning
10m

Randomized Search Vs Grid Search
2m

Parameter To RandomSearchCV
2m

Best Hyperparameters
2m

Call A RandomizedSearchCV Method
5m

Additional Reading
10m

Challenges in Live Trading
This section talks about the challenges faced during saving the model and data, and retraining the model. It demonstrates the simulation of trading using decision trees, and provides downloadable resources at the end of the course.
Challenges in Saving Model And Data
5m 46s

Pickle Parameter
2m

Dump Command
2m

Serialization
2m

Save The Model
2m

Save The Data
2m

Challenges In Retraining The Model
3m 26s

Retrain A Model
2m

How To Perform Simulation
3m 43s

Simulation Of Trading
2m

Data Leakage
2m

Trading Simulation Using Decision Trees
10m

Course Summary
2m 20s

Run Codes Locally on Your Machine
Learn to install the Python environment in your local machine.

Python Installation Overview
2m 18s

Flow Diagram
10m

Install Anaconda on Windows
10m

Install Anaconda on Mac
10m

Know your Current Environment
2m

Troubleshooting Anaconda Installation Problems
10m

Creating a Python Environment
10m

Changing Environments
2m

Quantra Environment
2m

Troubleshooting Tips For Setting Up Environment
10m

How to Run Files in Downloadable Section?
10m

Troubleshooting For Running Files in Downloadable Section
10m

Downloadable Code
Downloadable Code
2m

ABOUT AUTHOR

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.

WHY QUANTRA®?

• Gain more in less time
• Get taught by practitioners
• Learn at your own pace
• Get data & strategy models to practice on your own

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