Machine Learning – Quant Trading

Teacher

Chairman

About Course

Play the markets like a pro by integrating machine learning into your investment strategies! This online training course takes a completely hands-on approach to applying machine learning techniques to quantum trading. The focus is on the practical application of machine learning techniques to develop complex quantum trading models. From creating your own database of historical prices in MySQL to writing hundreds of lines of Python code, the focus is on getting it right from the start.

Financial markets are fickle animals that can be extremely difficult for the average investor to navigate. This Quant Trading Using Machine Learning course will introduce you to machine learning, an area of study that empowers computers to learn without being explicitly programmed, while at the same time teaching you how to apply these techniques to quantitative trading. Using Python libraries, you will learn how to build complex financial models that will better inform your investment decisions. Additional material included!

content

INTRODUCTION

You, this course, and we! 00:02:01
DEVELOPING TRADING STRATEGIES IN EXCEL
Are markets efficient or ineffective? 00:10:27
Momentum Investing 00: 11: 31
Medium Reverse 00: 06: 30
Evaluation of trading strategies – risk and profitability 00: 16: 22
Evaluation of Trading Strategies – Sharpe Ratio 00: 10: 16
Step 2 Process – Modeling and Backtesting 00: 03: 49
Developing a trading strategy in Excel 00: 11: 42
CONFIGURING THE DEVELOPMENT ENVIRONMENT
Installing Anaconda Python 00:09:00
Installing Pycharm-A Python IDE 00: 03:55
MySQL introduced and installed – Mac OS X 00: 07:04
MySQL Server and MySQL Workbench Configuration – Mac OS X 00: 17: 32
Installing MySQL – Windows 00: 06:32
For Linux-Mac OS newbies Shell-Path and other environment variables 00: 08:26

CUSTOMIZING THE PRICE DATABASE

Historical Price Data Software Download 00: 06:24
Code Along – Loading Price Data From Yahoo Finance 00: 14: 40
Code Along-Loading URL in Python 00: 07:39
i Code Along-download of price data from NSE 00: 13: 55
Code Along – Unzip and Process Uploaded Files 00: 05:22
Download data manually for 10 years 00: 01:00
Code Along – download historical data for 10 years 00: 06:26
Insert uploaded files into database 00: 10: 11
Code Along – Bulk Upload of Uploaded Files to MySQL Tables 00:15:13
Data Preparation 00: 04: 16
Code Along-Data Preparation 00: 12: 43
Adjustment for corporate actions 00: 08: 41
Code together-adjustment for corporate actions 1 00: 15: 29
Code together-adjustment for corporate actions 2 00: 08:47
i Code Along – Inserting Index Prices in MySQL 00: 05:41
Code Along – Building a Table of Calendar Functions in MySQL 00: 06:54

DECISION TREES, ENSEMBLE LEARNING AND RANDOM FORESTS

Planting a seed – what are decision trees 00: 17: 02
Growing a Tree – Learning Decision Tree 00: 18: 04
Forking-getting information 00: 18: 51
Decision Tree Algorithms 00: 07:51
Retrofitting – The Curse Of Machine Learning 00: 19: 04
Refurbishment Lasted 00: 11: 20
Cross-check 00: 18: 55
Regularization 00: 07: 18
Crowd Wisdom – Ensemble Learning 00: 16: 39
The ensemble’s training continues – packing, forcing and laying 00: 18: 03
Random Woods – Much More Than Trees 00: 12: 28

TRADING STRATEGY AS A CLASSIFICATION OF MACHINE LEARNING
Problem definition – machine learning classification 00: 15: 51

ENGINEERING CHARACTERISTICS
Know The Basics – Tutorial for Pandas 00: 11: 42
Code Along-fetching data from MySQL 00: 18: 35
Code Along – Building Some Simple Functions 00: 07:28
i Code Along-Plotting Pulse Function 00: 08:42
Code along-plotting jump function 00: 05:52
Code Along-Assigning Tags 00: 03:13
i Code Together – Putting It All Together 00: 18: 08
Code Together – Enable Support Features From Other Tickers 00: 06:34

Also Get Machine Learning – Quant Trading on Traderknow.com

DESIGNING A COMPLEX CATEGORIAL VARIABLE WITH PAST TRENDS
Designing a categorical variable 00: 03:49
Along-Engineering code categorical variable 00: 06:47
BUILDING A MACHINE LEARNING CLASSIFIER IN THE PYTHON LANGUAGE
Introducing Scikit-Learn 00:03:33
Introducing RandomForestClassifier 00: 09:26
Train and test a machine learning classifier 00: 15: 01
Comparison of the results of different strategies 00: 05:45
Using Class Probabilities for Predictions 00: 03: 11

CLASSIFIER OF NEAREST NEIGHBORS

Nearest Neighbor Classifier 00: 06:50
Code Along-Classifier of Nearest Neighbors 00: 04:16
TREE GROWTH GRADIENT
What are gradient augmented trees 00: 12: 38
Introducing the XGBoost Python Library for GBT 00: 11: 51
Code along-Setting parameters for gradient enhanced classifiers 00: 09:21
INTRODUCTION TO QUANTUM TRADING
Financial markets – who are the players 00: 16: 38
What is a stock market index 00: 03:14
Trading Mechanics – Long Vs Short Positions 00: 11:56
Futures Contracts 00: 14: 26