sentiment-analysis

This Skill quantifies market sentiment by converting subjective fear and greed into measurable indicators using Python. It applies machine learning models to market indicators and social media APIs for investment analysis.

Market sentiment analysis utilizes the Fear & Greed Index, Put-Call Ratio, Margin Trading Signals, Northbound Funds Signals, and Social Media Sentiment Analysis to provide a holistic view of real-time market conditions.

Yes, applying machine learning models is required to perform this sentiment analysis. The Skill relies on scikit-learn to process real-time market data and social media APIs to generate measurable sentiment indicators.

Yes, you can use pandas and numpy to process market data and calculate sentiment signals for trading strategies. This Skill relies on these dependencies to process market indicators and output quantifiable sentiment metrics.

You analyze current market sentiment by executing the 'analyze' command. This processes real-time market data and social media sentiment to help adjust your investment strategy accordingly.

Social media sentiment analysis for risk management requires continuous access to social media APIs and real-time market data sources. A limitation is its dependency on external API availability to maintain accurate sentiment signals.