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Generative AI for Trading and Asset Management · LearnSpace
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Generative AI for Trading and Asset Management

Курс от John Wiley & Sons
Средний≈ 10.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

A hands-on guide to applying generative AI in trading and asset management. Learn techniques for portfolio optimization, AI-based trading strategies, and more. This resource explores how artificial intelligence transforms trading and asset management, offering practical insights into AI-driven decision-making, risk management, and market analysis. It provides a clear understanding of AI techniques and their real-world applications in finance, helping learners stay ahead in a rapidly evolving industry. This resource is ideal for asset managers, traders, investors, and entrepreneurs interested in AI's role in finance. It is suitable for professionals with a basic understanding of finance and technology, as well as developers and data scientists looking to apply AI in financial contexts. This course provides a comprehensive, hands-on guide to using generative AI in trading and asset management. It walks through practical applications of unsupervised learning, supervised learning, and reinforcement learning models, showing how AI can optimize portfolios, create new trading strategies, and improve trading efficiency. Copyright © 2025 by John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Based on the book, Generative AI for Trading and Asset Management, by Hamlet Jesse Medina Ruiz and Ernest Chan.

Навыки, которые вы освоите

Generative Model ArchitecturesGenerative AILarge Language ModelingFinancial MarketRisk ManagementDimensionality ReductionPortfolio ManagementArtificial IntelligenceModel OptimizationMachine LearningStatistical Machine LearningAsset ManagementApplied Machine LearningFinancial AnalysisDeep LearningGenerative Adversarial Networks (GANs)Financial TradingLLM ApplicationAutoencodersMachine Learning Methods

Программа курса

11 модулей · 79 учебных материалов

01No-code Generative AI for Basic Quantitative Finance6 материалов

Harnessing Generative AI for Automated Quant Finance Tasks

OverviewВидеоIntroductionЧтениеComputing Sharpe RatioЧтениеTranslating Matlab Codes to Python CodesЧтение

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Преподаватель курса

Generative AI for Trading and Asset Management
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Обучение на Coursera

≈ 10.4 ч

11 модулей

Язык: Английский

Часть программы вашего университета
ConclusionЧтение
Introduction to Generative AI in Quantitative FinanceЗадание
02No-code Generative AI for Trading Strategies Development8 материалов

Harnessing Generative AI for Automated Trading Strategy Development

OverviewВидеоIntroductionЧтениеUser PromptЧтениеSummarizing a Trading Strategy Paper and Creating Backtest Codes from ItЧтениеSearching for a Portfolio Optimization Algorithm Based on Machine LearningЧтениеExplore Options Term Structure Arbitrage StrategiesЧтениеConclusionЧтениеNo-code Generative AI and Financial Strategy FundamentalsЗадание
03Whirlwind Tour of ML in Asset Management15 материалов

Harnessing Machine Learning for Smarter Asset Management

OverviewВидеоIntroductionЧтениеHierarchical Risk Parity (HRP)ЧтениеCluster-based Feature Selection (cMDA)ЧтениеSupervised LearningЧтениеLinear and Logistic RegressionsЧтениеL1 and L2 RegularizationsЧтениеPerformance MetricsЧтениеCopilot ResponseЧтениеNeural NetworksЧтениеRecurrent Neural NetworkЧтениеDeep Reinforcement LearningЧтениеSurvivorship BiasЧтениеMerging Time Series with Different FrequenciesЧтениеMachine Learning Fundamentals in Financial ApplicationsЗадание
04Understanding Generative AI5 материалов

Unleashing Creativity: How Generative AI Models Learn and Create

OverviewВидеоIntroductionЧтениеGenerating New DataЧтениеA Few Words on Representation Learning with ChatGPTЧтениеExploring Generative AI FundamentalsЗадание
05Deep Autoregressive Models for Sequence Modeling7 материалов

Mastering Sequential Data: From RNNs to Transformers in Time-Series Modeling

OverviewВидеоIntroductionЧтениеMasked Autoencoder for Density Estimation (MADE)ЧтениеRecurrent Neural Networks (RNN)ЧтениеFrom NLP Transformer to the Time-series TransformersЧтениеModel FittingЧтениеExploring Sequence Modeling with TransformersЗадание
06Deep Latent Variable Models9 материалов

Unveiling Hidden Structures: From Probabilistic Models to Deep Generative Techniques

OverviewВидеоIntroductionЧтениеLatent Variable ModelsЧтениеProbabilistic Principal Component AnalysisЧтениеGaussians Mixture ModelsЧтениеDeep Latent Variable ModelsЧтениеOptimizationЧтениеVAEs for Sequential Data and Time SeriesЧтениеExploring Latent Variable Modeling ConceptsЗадание
07Flow Models7 материалов

Transforming Distributions: From Linear Flows to Time Series Applications

OverviewВидеоIntroductionЧтениеLinear FlowsЧтениеCoupling FlowsЧтениеAutoregressive FlowsЧтениеAdapting Flows for Time SeriesЧтениеFlow Models and Normalizing TransformationsЗадание
08Generative Adversarial Networks7 материалов

Mastering the Art and Science of GANs: From Theory to Practice

OverviewВидеоIntroductionЧтениеTrainingЧтениеSome Theoretical Insight in GANsЧтениеWhy Is GAN Training Hard? Improving GAN Training TechniquesЧтениеWasserstein GAN (WGAN)ЧтениеExploring GANs and Their Core ConceptsЗадание
09Leveraging LLMs for Sentiment Analysis in Trading6 материалов

From Audio to Action: Building a Financial Sentiment Pipeline

OverviewВидеоIntroductionЧтениеCollecting Audio DataЧтениеSentiment AnalysisЧтениеExperiment ResultsЧтениеSentiment Analysis and Language Models in TradingЗадание
10Efficient Inference6 материалов

Optimizing Model Performance: From Size to Speed

OverviewВидеоEfficient Inference IntroductionЧтениеImpact of Model SizeЧтениеModel QuantizationЧтениеExperiment Results with Linear Quantization on Distilled FinBERTЧтениеEfficient Inference in Machine LearningЗадание
11Afterword3 материалов

Harnessing LLMs for No-Code Trading Innovation

OverviewВидеоAfterword - The ReadingЧтениеExploring Generative Models in Financial ApplicationsЗадание