Learning Track: Sentiment Analysis in Trading
35 hours
Learn to quantify human sentiments expressed in news and tweets using machine learning techniques. Use sentiment indicators and sentiment scores to create trading strategy. Learn to implement the same in live trading. Recommended for traders who want to harness alternate sources of data
Level
FOUNDATION to ADVANCED
Price
₹88745/- ₹22187/-
- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
Fetch tweets and news data and backtest an intraday strategy using the sentiment score.
Train a machine learning model to calculate a sentiment from a news headline. Predict the stock returns and bond returns.
Implement and compare the word embeddings methods such as Bag of Words (BoW), TF-IDF, Word2Vec and BERT.
Work with time-series data and be able to manipulate it and incorporate transaction costs and slippage in backtesting.
Analyze the trading strategies using various performance metrics.
Create a trading strategy using sentiment indicators such as Put-Call ratio, TRIN and VIX indicators and analyze different types of risks involved in trading.
Learn to live and paper trade the strategies covered in the course.
skills covered
Need help? Write to us at quantra@quantinsti.com or call us at +91-8291945960.
Prerequisites
Working of the financial markets and familiarity with basic programming skills are recommended to fully appreciate the implementation of various strategies covered in this learning track. Even if you haven`t traded in financial markets nor coded in python, this learning track can be easily completed.
