Terry Benzschawel – Natural Language Processing in Trading
Description of Natural Language Processing in Trading
If you are looking to trade based on the sentiments and opinions expressed in the news headline through cutting edge natural language processing techniques, this is the right course for you. Learn to quantify the news headline and add an edge to your trading using powerful models such as Word2Vec, BERT and XGBoost.
LIVE TRADING
Train a machine learning model to calculate a sentiment from a news headline
Implement and compare the word embeddings methods such as Bag of Words (BoW), TF-IDF, Word2Vec and BERT
Predict the stock returns and bond returns from the news headlines
Describe the applications of natural language processing
Automate and paper trade the strategies covered in the course
Fetch the recent news headline data
Implement strategies in the live markets and analyze the performance
SKILLS COVERED
Predictive Modelling
Supervised Learning
XGBoost Model
Train and Test Datasets
Corporate Bonds returns
Stock Returns, Sharpe ratio
Word Embeddings
Bag of Words
TF-IDF
Word2Vec
BERT
Python
Numpy
Pandas
XGBoost
Matplotlib
CountVectorizer
PREREQUISITES
Basic familiarity with machine learning concepts such as training, testing, features and target variables is required. Exposure to programming concepts is required to interpret the codes covered in the course. However, experience with Python coding knowledge is optional. If you want to be able to code and implement the strategies in Python, you should be able to work with ‘Pandas Data frames’. All the required skill sets are covered in the foundation courses available in the learning track.
What will you learn in Natural Language Processing in Trading?
Natural Language Processing in Trading by Dr. Terry Benzschawel,what is it included:
Section 1: Introduction to the Course
Section 2: Applications of Natural Language Processing
Section 3: Sources of News Headline Data
Section 4: Sentiment Score and Strategy Logic
Section 5: Sentiment Strategy on Stocks
Section 6: Sentiment Strategy on Bonds
Section 7: Introduction to Word Embeddings
Section 8: Bag of Words
Section 9: Predicting Sentiment Score Using XGBoost
Section 10: Sentiment Class of News Headlines
Section 11: TF-IDF
Section 12: WordVec
Section 13: BERT
Section 14: BERT Model Adaptation
Section 15: Result Analysis
Section 16 (Optional): Python Installation
Section 17: (Optional): Live Trading on IBridgePy
Section 18: Paper and Live Trading
Section 19: Capstone Project
Section 20: Course Summary
About Terry Benzschawel
Dr. Terry Benzschawel is the founder and Principal at Benzschawel Scientific, LLC. Before that, Terry had worked with Citigroup’s Institutional Clients Business, as a Managing Director, heading the Quantitative Credit Trading group. In Citi’s Fixed Income Strategy department, Terry has worked as a credit strategist with a focus on client-oriented solutions across all credit markets. Before that, he had worked in Chase Manhattan and Citi to build algorithms to predict corporate bankruptcy and to detect credit fraud on card transactions. He has authored two books on Credit Modeling.
