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Курс от New York Institute of Finance, Google Cloud
courseraАнализ данных
Reinforcement Learning for Trading Strategies
Курс от New York Institute of Finance, Google CloudО курсе
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
Навыки, которые вы освоите
Reinforcement LearningDeep LearningFinancial TradingModel TrainingApplied Machine LearningRecurrent Neural Networks (RNNs)Model OptimizationMachine Learning SoftwareSecurities TradingArtificial Intelligence and Machine Learning (AI/ML)Portfolio ManagementModel DeploymentMachine Learning MethodsRisk ManagementArtificial Neural NetworksMarkov Model
Программа курса
3 модулей · 34 учебных материалов
01Introduction to Course and Reinforcement Learning12 материалов
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Jack Farmer
Curriculum Director