Dr. Terry Benzschawel – Natural Language Processing in Trading

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Dr. Terry Benzschawel – Natural Language Processing in Trading download, Dr. Terry Benzschawel – Natural Language Processing in Trading review, Dr. Terry Benzschawel – Natural Language Processing in Trading free

Dr. Terry Benzschawel – 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.

• 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

Word Emeddings

• Baf of Words
• TF-IDF
• Word2Ves
• BERT

Predictive Modelling

• Supervisied Learning
• XGBoost Model
• Train and Test Datasets
• Corporate Bonds returns
• Stock Returns, Sharpe ratio

Python

• NumPy
• Pands
• XGBoost
• Matplotlib
• CountVectorizer

LEARNING TRACK

Sentiment Analysis in Trading

FOUNDATION

• Python For Reading

BEGINNER

• Trading using Options Sentiment Indications

INTERMEDIATE

• Trading Strategies with News and Tweets
• Trading with Machine Learning: Classification and SVM

ADVANCED

• Natural Language Processing in Trading

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.

SYLLABUS

1. Introduction to the Course
2. Applications of Natural Language Processing
3. Sources of News Headline Data
4. Sentiment Score and Strategy Logic
5. Sentiment Strategy on Stocks
6. Sentiment Strategy on Bonds
7. Introduction to Word Embeddings
8. Bag of Words
9. Predicting Sentiment Score Using XGBoost
10. Sentiment Class of News Headlines
11. TF-IDF
12. WordVec
13. BERT
14. BERT Model Adaptation
15. Result Analysis
16. Run Codes Locally on Your Machine
17. Live Trading on IBridgePy
18. Paper and Live Trading
19. Capstone Project
20. Course Summary

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