Building a Workflow for AI

Learn how to prepare price data for AI models, backtest trading algorithms, and build a simple RSI algorithm.

Explore investment data, summarize key stats, use K-Means and PCA for clustering, adapt trading algorithms, and identify risk factors to enhance model insights on outperformance

Build regression models using past returns, explore regularization to avoid over/underfitting, and differentiate between training and test data while identifying signs of overfitting and underfitting.

Predict categorical variables using logistic regression and decision trees. Improve model performance with cross-validation for strong out-of-sample results.

Explore reinforcement learning (RL) and its components, Q-learning, the DQN algorithm, and how to build and backtest an RL model.