Dr. Ermest P.Chan & Dr. Roger Hunter – Data & Feature Engineering for Trading

How many times have you created a strategy that performed well during backtesting, however failed to make money in the real markets? An essential course to create robust machine learning strategies which can be executed on trading platforms. This course teaches the data cleaning aspects on financial datasets and with real-world examples.

SKILLS COVERED

Data Engineering

• Financial data cleaning
• Exploratory data analysis
• Data types nuances
• Survivorship & Look ahead Bias

Feature Engineering

• Triple barrier method
• Dollar and volume bars
• Stationarity
• Fractional differentiation

Python

• Itertools
• Numpy
• Pandas
• Matplotlib
• Pickle

PREREQUISITES
You should be familiar with basic machine learning principles such as train and test datasets. There are no prerequisites as such and anyone who is familiar with financial markets data can enroll in the course.

AFTER THIS COURSE YOU’LL BE ABLE TO
Preprocess price data to resolve outliers, duplicate values, multiple stock classes, survivorship bias, and look-ahead bias issues.
Work with sentiment data to identify structural break and aggregate categorical features.
Examine fundamental data and resolve multiple data merging issues.
Create features and target variables for machine learning models.
Explain various challenges associated with the financial data

SYLLABUS

In this introductory section, you will learn the importance of data engineering and feature engineering which can be used either in your personal trading or in an institutional setting. Preprocessing of the financial dataset is essential to make it suitable for analysis. Extracting features from the datasets to feed into the machine learning algorithms, and setting the target variable for a particular ML problem increases the predictive power of your algorithm.

• Introduction by Dr. Ernest Chan
• Course Overview
• Quantra Features and Guidance

Data & Feature Engineering for Trading by Dr. Ermest P.Chan & Dr. Roger Hunter, what is it included (Content proof: Watch here!)

Section 1: Introduction to the Course

Section 2: Challenges in Financial Data Engineering

Section 3: Exploratory Data Analysis in Finance

Section 4: Survivorship Bias for Stock Data

Section 5: Redundant Stocks Data

Section 6: Multiple Stock Classes: One or All?

Section 7: Outliers-How to Identify and Deal With Them?

Section 8: News Data-Numerical Features

Section 9: News Data-Categorical Features

Section 10: Structural Breaks in Financial Data

Section 11: Fundamental Data-Merge Them Correctly

Section 12: Look-ahead Bias-Deceptive Returns

Section 13: Types of Bars-Features Extraction

Section 14: Information Bars-Market Order Imbalances

Section 15: Data Labelling for Better Outcomes

Section 16: Why Stationary Features?

Section 17 (Optional): Python Installation

Section 18: Summary

ABOUT AUTHOR

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.

Dr. Roger Hunter is the Chief Technology Officer of QTS. He is responsible for designing high performance automated execution system that achieved negative slippage. Roger is a serial entrepreneur, having founded profitable hedge funds and software firms. Roger was formerly professor of mathematics at New Mexico State University, and he obtained his Ph.D. in Mathematics from Australian National University.

WHY QUANTRA?

LEARNING EXPERIENCE

USER TESTIMONIALS

Molefe S

Manager, Standard bank, South Africa

I really liked the course especially the Interactive Exercises. This has helped me get used to the syntax for different functions in Python. I enjoyed experimenting with the scripts in the Jupyter notebook that is integrated with the course. I also used the downloadable codes to get a hands-on experience of coding but I found the integrated notebook easier to use and experiment with the codes. The course is very well curated, nothing feels out of place, in fact, I have started to practically apply the learnings from this course in my day to day task as I deal with Data daily in my job

Alan

Hong Kong

I really liked the content of the course provided on Quantra, especially in the Machine Learning (ML) related courses. The video units make it very easy to understand complex concepts of ML. They also provide you with downloadable codes at the end of the courses which can be used by you to experiment and learn on your own. This is not very common in the online teaching industry.

German Montenegro

Developer, Argentina

Quantra always has good content that is easy to understand! The best part about the course is that it is a mix of theory and practice, which is a great way to learn and grasp concepts, as opposed to reading books or articles.

Sale Page: https://quantra.quantinsti.com/course/data-and-feature-engineering-for-trading
Archive: https://archive.ph/wip/6kJJX

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