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Raul Glavan | How to build a stock market money printing machine with AI
Here is the meta description: Build a stock market "money printing" machine with AI, using sentiment data, Twitter analysis, and fundamental factors like copper production and price-to-book value to make informed investment decisions.
- Analysis with sentiment data and Twitter data is necessary to build a stock market money printing machine with AI
- Copper production is very important in the fundamental space
- Price to book value is a key fundamental data point
- Medium term data is more important for AI trading
- Technical analysis is not effective in AI trading
- Normalization of data is crucial for AI models
- Feature engineering is important for AI models
- Sample size is small, and overfitting is a problem
- Data quality is essential for AI models
- Public data is easily accessible and free
- Private data is competitive and difficult to access
- Insider trading data is not reliable or consistent
- Weather patterns and construction data can be used for AI trading
- Decision trees and LSTMs can be used for AI trading
- Sentiment analysis can be used for AI trading
- Alternative data sources can be used for AI trading *_DOMAIN_KNOWLEDGEimportant in AI trading
- Training AI models requires a lot of work and effort
- Predictive value of AI models can decrease over time
- AI models require frequent updates and optimization
- Data acquisition and feature engineering are crucial for AI trading