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Udacity

Building a Workflow for AI

via Udacity

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Overview

Refine your skills in AI-based trading by mastering key machine learning techniques such as reinforcement learning, supervised and unsupervised learning. Develop and backtest trading models using real financial data.

Syllabus

  • Introduction to AI Workflows in Trading
    • Learn how to prepare price data for AI models, backtest trading algorithms, and build a simple RSI algorithm.
  • Unsupervised Learning
    • 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
  • Supervised Learning: Regression
    • 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.
  • Supervised Learning: Classification
    • Predict categorical variables using logistic regression and decision trees. Improve model performance with cross-validation for strong out-of-sample results.
  • Reinforcement Learning
    • Explore reinforcement learning (RL) and its components, Q-learning, the DQN algorithm, and how to build and backtest an RL model.

Taught by

Metin Akyol, PhD

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