Raul Glavan | How to build a stock market money printing machine with AI


Master AI for profitable stock market trading: data sources, feature engineering, machine learning algorithms, and avoiding pitfalls for long-term success.

Key takeaways
  • Data acquisition and pre-processing are crucial steps in building a successful AI trading model.
  • Public data is easily accessible but often lacks predictive value.
  • Alternative data sources, such as social media, weather patterns, and corporate insider data, can provide valuable insights.
  • Feature engineering is essential for extracting meaningful information from raw data.
  • Machine learning algorithms, such as LSTMs and decision trees, are commonly used for financial forecasting.
  • Overfitting and look-ahead bias are common pitfalls to avoid when training AI models.
  • Regular model evaluation and optimization are necessary to maintain performance.
  • Markets are becoming increasingly efficient, making it harder to gain an edge through AI trading.
  • Transparency and ethical considerations are important factors to keep in mind when using AI in finance.
  • Collaboration and sharing of knowledge can accelerate progress in the field of AI trading.
  • Domain-specific expertise is valuable in developing effective AI trading models.