Quant Trading – Machine Learning

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Quant Trading – Machine Learning

Last Updated: June 26, 2025

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Quant Trading – Machine Learning

Description of Machine Learning

Play the Markets Like a Pro by Integrating Machine Learning into Your Investment Strategies! This online training course takes a completely practical approach to applying Machine Learning techniques to Quant Trading. The focus is on practically applying Machine Learning techniques to develop sophisticated Quant Trading models. From setting up your own historical price database in MySQL, to writing hundreds of lines of Python code, the focus is on doing from the get-go.

Financial markets are fickle beasts that can be extremely difficult to navigate for the average investor. This Quant Trading Using Machine Learning course will introduce you to machine learning, a field of study that gives computers the ability to learn without being explicitly programmed, while teaching you how to apply these techniques to quantitative trading. Using Python libraries, you will discover how to build sophisticated financial models that will better inform your investing decisions. Supplemental Material included!

What will you learn in Machine Learning?

INTRODUCTION

You, This Course, and Us!

DEVELOPING TRADING STRATEGIES IN EXCEL

Are markets efficient or inefficient?
Momentum Investing
Mean Reversion
Evaluating Trading Strategies – Risk and Return
Evaluating Trading Strategies – The Sharpe Ratio
The 2 Step process – Modeling and Backtesting
Developing a Trading Strategy in Excel

SETTING UP YOUR DEVELOPMENT ENVIRONMENT

Installing Anaconda for Python
Installing Pycharm – a Python IDE
MySQL Introduced and Installed – Mac OS X
MySQL Server Configuration and MySQL Workbench – Mac OS X
MySQL Installation – Windows
For Linux-Mac OS Shell Newbies – Path and Other Environment Variables

SETTING UP A PRICE DATABASE

Programmatically Downloading Historical Price Data
Code Along – Downloading Price Data from Yahoo Finance
Code Along – Downloading a URL in Python
Code Along – Downloading Price Data from the NSE
Code Along – Unzip and Process the Downloaded Files
Manually download data for 10 years
Code Along – Download Historical Data for 10 years
Inserting the Downloaded Files into a Database
Code Along – Bulk Loading Downloaded Files into MySQL Tables
Data Preparation
Code Along – Data Preparation
Adjusting for Corporate Actions
Code Along – Adjusting for Corporate Actions 1
Code Along – Adjusting for Corporate Actions 2
Code Along – Inserting Index Prices into MySQL
Code Along – Constructing a Calendar Features Table in MySQL

DECISION TREES, ENSEMBLE LEARNING AND RANDOM FORESTS

Planting the seed – What are Decision Trees
Growing the Tree – Decision Tree Learning
Branching out – Information Gain
Decision Tree Algorithms
Overfitting – The Bane of Machine Learning
Overfitting Continued
Cross-Validation
Regularization
The Wisdom of Crowds – Ensemble Learning
Ensemble Learning continued – Bagging, Boosting and Stacking
Random Forests – Much More Than Trees

A TRADING STRATEGY AS MACHINE LEARNING CLASSIFICATION

Defining the Problem – Machine Learning Classification

FEATURE ENGINEERING

Know the basics – A Pandas tutorial
Code Along – Fetching Data from MySQL
Code Along – Constructing Some Simple Features
Code Along – Constructing a Momentum Feature
Code Along – Constructing a Jump Feature
Code Along – Assigning Labels
Code Along – Putting It All Together
Code Along – Include Support Features from Other Tickers

ENGINEERING A COMPLEX FEATURE – A CATEGORICAL VARIABLE WITH PAST TRENDS

Engineering a Categorical Variable
Code Along – Engineering a Categorical Variable

BUILDING A MACHINE LEARNING CLASSIFIER IN PYTHON

Introducing Scikit-Learn
Introducing RandomForestClassifier
Training and Testing a Machine Learning Classifier
Compare Results from Different Strategies
Using Class Probabilities for Predictions

NEAREST NEIGHBORS CLASSIFIER

A Nearest Neighbors Classifier
Code Along – A Nearest Neighbors Classifier

GRADIENT BOOSTED TREES

What are Gradient Boosted Trees
Introducing XGBoost – A Python Library for GBT
Code Along – Parameter Tuning for Gradient Boosted Classifiers

INTRODUCTION TO QUANT TRADING

Financial Markets – Who Are the Players
What is a Stock Market Index
The Mechanics of Trading – Long Vs Short Positions
Futures Contracts

More courses from the same author: Quant Trading

### Key Benefits of Taking the Course 'Quant Trading – Machine Learning'
1. **Practical Application of Machine Learning in Trading**: This course offers hands-on experience in applying machine learning techniques to quantitative trading, enabling you to develop sophisticated trading models using Python. You'll learn to integrate data-driven strategies into real-world investment decisions.
2. **Comprehensive Skill Development**: From setting up a historical price database in MySQL to coding complex algorithms in Python, the course covers essential technical skills. You'll master tools like Scikit-Learn, Pandas, and XGBoost, as well as concepts like decision trees, random forests, and gradient boosted trees.
3. **Enhanced Investment Decision-Making**: By learning to build and backtest trading strategies (e.g., momentum investing and mean reversion), you'll gain the ability to evaluate risk and return using metrics like the Sharpe Ratio, ultimately making more informed and data-driven investment choices.
4. **Understanding Financial Markets and Trading Mechanics**: The course provides a solid foundation in financial markets, including the roles of key players, stock market indices, and trading mechanics like long/short positions and futures contracts, ensuring you have the contextual knowledge to apply machine learning effectively.
5. **Feature Engineering and Model Optimization**: You'll learn to engineer complex features and construct machine learning classifiers, while also exploring techniques like cross-validation and regularization to prevent overfitting, ensuring your trading models are robust and effective.

Target Audience

N ### Target Audience for the Course 'Quant Trading – Machine Learning'
N Based on the detailed description provided for the course 'Quant Trading – Machine Learning,' the target audience can be identified as follows:
N 1. **Aspiring Quantitative Traders**: Individuals who are interested in pursuing a career in quantitative trading or enhancing their skills in financial markets with a focus on data-driven strategies. This includes those who want to learn how to apply machine learning to develop sophisticated trading models.
N 2. **Finance Professionals**: Professionals working in finance, such as portfolio managers, financial analysts, or hedge fund employees, who seek to integrate machine learning techniques into their investment strategies to improve decision-making and performance.
N 3. **Data Scientists and Machine Learning Enthusiasts**: Individuals with a background or interest in data science and machine learning who wish to apply their technical skills to the financial domain, specifically in quantitative trading. This group may already be familiar with Python and machine learning concepts but want to learn their application in trading.
N 4. **Programmers and Developers**: Those with programming experience, particularly in Python, who are interested in financial markets and want to build practical applications like historical price databases and trading algorithms using tools like MySQL, Pandas, and Scikit-Learn.
N 5. **Investors Seeking Advanced Strategies**: Average or experienced investors who want to move beyond traditional investment approaches and learn advanced techniques like momentum investing, mean reversion, and machine learning-based classification to navigate complex financial markets more effectively.
N 6. **Students and Academics**: University students or researchers in fields like finance, computer science, or data science who are looking to explore the intersection of machine learning and quantitative trading as part of their studies or research projects.
N 7. **Tech-Savvy Individuals with Basic Financial Knowledge**: People who may not have extensive experience in finance but possess technical skills (e.g., familiarity with coding or databases) and are eager to learn how to apply these skills to trading strategies.
N ### Key Characteristics of the Target Audience:
N - **Skill Level**: Intermediate to advanced learners who are comfortable with or willing to learn programming (Python), database management (MySQL), and statistical concepts.
N - **Interest**: Strong interest in both financial markets and machine learning, with a focus on practical, hands-on application rather than just theoretical knowledge.
N - **Motivation**: Desire to build and backtest trading strategies, improve investment decisions, or gain a competitive edge in the financial markets using cutting-edge technology.
N This course is not suited for complete beginners in programming or finance, as it assumes some foundational knowledge and focuses on practical implementation with tools like Python, MySQL, and machine learning libraries.

Course Content

Quant Trading – Machine Learning

01 – You, This Course, and Us!

00:00
02 – Are markets efficient or inefficient

00:00
03 – Momentum Investing

00:00
04 – Mean Reversion

00:00
05 – Evaluating Trading Strategies – Risk and Return

00:00
06 – Evaluating Trading Strategies – The Sharpe Ratio

00:00
07 – The 2 Step process – Modeling and Backtesting

00:00
08 – Developing a Trading Strategy in Excel

00:00
09 – Installing Anaconda for Python

00:00
10 – Installing Pycharm – a Python IDE

00:00
11 – MySQL Introduced and Installed – Mac OS X

00:00
12 – MySQL Server Configuration and MySQL Workbench – Mac OS X

00:00
13 – MySQL Installation – Windows

00:00
14 – For Linux-Mac OS Shell Newbies – Path and Other Environment Variables

00:00
15 – Programmatically Downloading Historical Price Data

00:00
16 – Code Along – Downloading Price Data from Yahoo Finance

00:00
17 – Code Along – Downloading a URL in Python

00:00
18 – Code Along – Downloading Price Data from the NSE

00:00
19 – Code Along – Unzip and Process the Downloaded Files

00:00
20 – Manually download data for 10 years

00:00
21 – Code Along – Download Historical Data for 10 years

00:00
22 – Inserting the Downloaded Files into a Database

00:00
23 – Code Along – Bulk Loading Downloaded Files into MySQL Tables

00:00
24 – Data Preparation

00:00
25 – Code Along – Data Preparation

00:00
26 – Adjusting for Corporate Actions

00:00
27 – Code Along – Adjusting for Corporate Actions 1

00:00
28 – Code Along – Adjusting for Corporate Actions 2

00:00
29 – Code Along – Inserting Index Prices into MySQL

00:00
30 – Code Along – Constructing a Calendar Features Table in MySQL

00:00
31 – Planting the seed – What are Decision Trees

00:00
32 – Growing the Tree – Decision Tree Learning

00:00
33 – Branching out – Information Gain

00:00
34 – Decision Tree Algorithms

00:00
35 – Overfitting – The Bane of Machine Learning

00:00
36 – Overfitting Continued

00:00
37 – Cross-Validation

00:00
38 – Regularization

00:00
39 – The Wisdom of Crowds – Ensemble Learning

00:00
40 – Ensemble Learning continued – Bagging, Boosting and Stacking

00:00
41 – Random Forests – Much More Than Trees

00:00
42 – Defining the Problem – Machine Learning Classification

00:00
43 – Know the basics – A Pandas tutorial

00:00
44 – Code Along – Fetching Data from MySQL

00:00
45 – Code Along – Constructing Some Simple Features

00:00
46 – Code Along – Constructing a Momentum Feature

00:00
47 – Code Along – Constructing a Jump Feature

00:00
48 – Code Along – Assigning Labels

00:00
49 – Code Along – Putting It All Together

00:00
50 – Code Along – Include Support Features from Other Tickers

00:00
51 – Engineering a Categorical Variable

00:00
52 – Code Along – Engineering a Categorical Variable

00:00
53 – Introducing Scikit-Learn

00:00
54 – Introducing RandomForestClassifier

00:00
55 – Training and Testing a Machine Learning Classifier

00:00
56 – Compare Results from Different Strategies

00:00
57 – Using Class Probabilities for Predictions

00:00
58 – A Nearest Neighbors Classifier

00:00
59 – Code Along – A Nearest Neighbors Classifier

00:00
60 – What are Gradient Boosted Trees

00:00
61 – Introducing XGBoost – A Python Library for GBT

00:00
62 – Code Along – Parameter Tuning for Gradient Boosted Classifiers

00:00
63 – Financial Markets – Who Are the Players

00:00
64 – What is a Stock Market Index

00:00
65 – The Mechanics of Trading – Long Vs Short Positions

00:00
66 – Futures Contracts

00:00

Student Ratings & Reviews

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Quant Trading – Machine Learning

Quant Trading – Machine Learning

Description of Machine Learning

What will you learn in Machine Learning?

What Will You Learn?

Target Audience

Course Content

Quant Trading – Machine Learning

01 – You, This Course, and Us!

02 – Are markets efficient or inefficient

03 – Momentum Investing

04 – Mean Reversion

05 – Evaluating Trading Strategies – Risk and Return

06 – Evaluating Trading Strategies – The Sharpe Ratio

07 – The 2 Step process – Modeling and Backtesting

08 – Developing a Trading Strategy in Excel

09 – Installing Anaconda for Python

10 – Installing Pycharm – a Python IDE

11 – MySQL Introduced and Installed – Mac OS X

12 – MySQL Server Configuration and MySQL Workbench – Mac OS X

13 – MySQL Installation – Windows

14 – For Linux-Mac OS Shell Newbies – Path and Other Environment Variables

15 – Programmatically Downloading Historical Price Data

16 – Code Along – Downloading Price Data from Yahoo Finance

17 – Code Along – Downloading a URL in Python

18 – Code Along – Downloading Price Data from the NSE

19 – Code Along – Unzip and Process the Downloaded Files

20 – Manually download data for 10 years

21 – Code Along – Download Historical Data for 10 years

22 – Inserting the Downloaded Files into a Database

23 – Code Along – Bulk Loading Downloaded Files into MySQL Tables

24 – Data Preparation

25 – Code Along – Data Preparation

26 – Adjusting for Corporate Actions

27 – Code Along – Adjusting for Corporate Actions 1

28 – Code Along – Adjusting for Corporate Actions 2

29 – Code Along – Inserting Index Prices into MySQL

30 – Code Along – Constructing a Calendar Features Table in MySQL

31 – Planting the seed – What are Decision Trees

32 – Growing the Tree – Decision Tree Learning

33 – Branching out – Information Gain

34 – Decision Tree Algorithms

35 – Overfitting – The Bane of Machine Learning

36 – Overfitting Continued

37 – Cross-Validation

38 – Regularization

39 – The Wisdom of Crowds – Ensemble Learning

40 – Ensemble Learning continued – Bagging, Boosting and Stacking

41 – Random Forests – Much More Than Trees

42 – Defining the Problem – Machine Learning Classification

43 – Know the basics – A Pandas tutorial

44 – Code Along – Fetching Data from MySQL

45 – Code Along – Constructing Some Simple Features

46 – Code Along – Constructing a Momentum Feature

47 – Code Along – Constructing a Jump Feature

48 – Code Along – Assigning Labels

49 – Code Along – Putting It All Together

50 – Code Along – Include Support Features from Other Tickers

51 – Engineering a Categorical Variable

52 – Code Along – Engineering a Categorical Variable

53 – Introducing Scikit-Learn

54 – Introducing RandomForestClassifier

55 – Training and Testing a Machine Learning Classifier

56 – Compare Results from Different Strategies

57 – Using Class Probabilities for Predictions

58 – A Nearest Neighbors Classifier

59 – Code Along – A Nearest Neighbors Classifier

60 – What are Gradient Boosted Trees

61 – Introducing XGBoost – A Python Library for GBT

62 – Code Along – Parameter Tuning for Gradient Boosted Classifiers

63 – Financial Markets – Who Are the Players

64 – What is a Stock Market Index

65 – The Mechanics of Trading – Long Vs Short Positions

66 – Futures Contracts

Student Ratings & Reviews

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