Natural Language Processing in Trading by Dr. Terry Benzschawel

Natural Language Processing in Trading by Dr. Terry Benzschawel

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.

ABOUT AUTHOR

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.

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