This module guides learners through leveraging no-code generative AI tools, such as ChatGPT, to develop, backtest, and optimize trading strategies. Participants will explore how to prompt AI for financial data analysis, translate academic trading models into code, and investigate advanced strategies like portfolio optimization and options arbitrage. By the end, learners will be able to use AI to accelerate the creation and evaluation of quantitative trading ideas.

This module introduces key machine learning techniques used in asset management, including supervised learning, feature selection, and portfolio optimization. Learners will explore practical applications such as regression models, neural networks, and performance metrics, while also addressing challenges like survivorship bias and data frequency alignment. By the end, participants will understand how to leverage ML tools to enhance investment strategies.

This module introduces the foundational concepts of generative artificial intelligence, exploring how generative models create complex data such as images, text, and code. Learners will also discover the role of representation learning in enabling AI systems like ChatGPT to encode and generate new data for various applications.

This module explores advanced deep learning techniques for modeling sequential data, including autoregressive models, masked autoencoders, recurrent neural networks, and transformers. Learners will gain practical insights into how these models are applied to time-series and density estimation tasks, as well as how to fit them using maximum likelihood estimation.

This module introduces learners to the fundamentals of latent variable models, including probabilistic PCA and Gaussian Mixture Models, and explores how deep learning techniques extend these concepts. Learners will gain practical insights into model optimization and the application of variational autoencoders (VAEs) to sequential and time-series data.

This module introduces the principles and techniques behind flow-based generative models, including linear, coupling, and autoregressive flows. Learners will explore how these models transform probability distributions and how they can be adapted for complex data such as time series. By the end, you'll understand both the mathematical foundations and practical considerations for applying flow models.

This module delves into the fundamentals and challenges of Generative Adversarial Networks (GANs), including their training dynamics, theoretical underpinnings, and practical difficulties. Learners will explore advanced techniques to improve GAN performance and understand alternative approaches such as Wasserstein GANs. By the end, participants will gain a solid foundation in both the conceptual and practical aspects of GANs.

This module guides learners through building a practical sentiment analysis pipeline for financial trading using large language models. You will learn how to collect audio data from online sources, transcribe it, and apply specialized models to extract sentiment relevant to trading decisions. The module culminates in evaluating the effectiveness of this end-to-end system using real-world financial speech data.

This module explores strategies for making machine learning inference more efficient, focusing on the challenges posed by large model sizes and the benefits of quantization techniques. Learners will examine real-world experiments that demonstrate how reducing model precision can impact memory usage, computational speed, and accuracy. By the end, you'll understand practical methods for optimizing deep learning models for deployment.

This module reflects on how large language models (LLMs) can streamline the development of trading applications through no-code solutions. Learners will review key takeaways from earlier lessons and consider the practical implications of integrating LLMs into trading workflows.