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.
Este curso forma parte de Programa especializado: Machine Learning for Trading
9713 ya inscrito
ofrecido por
Instituto de Finanzas de Nueva York
Google Cloud
Programa especializado: Machine Learning for TradingInstituto de Finanzas de Nueva YorkAcerca de este Curso
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Curso 3 de 3 en
Nivel intermedio
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
Aprox. 12 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Qué aprenderás
Understand the structure and techniques used in reinforcement learning (RL) strategies.
Understand the benefits of using RL vs. other learning methods.
Describe the steps required to develop and test an RL trading strategy.
Describe the methods used to optimize an RL trading strategy.
Habilidades que obtendrás
Reinforcement Learning Model DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 3 en
Nivel intermedio
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
Aprox. 12 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Instituto de Finanzas de Nueva York
The New York Institute of Finance (NYIF), is a global leader in training for financial services and related industries. Started by the New York Stock Exchange in 1922, it now trains 250,000+ professionals in over 120 countries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The institute has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes. Its US customers include the SEC, the Treasury, Morgan Stanley, Bank of America and most leading worldwide banks.
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Programa - Qué aprenderás en este curso
3 horas para completar
Introduction to Course and Reinforcement Learning
In this module, reinforcement learning is introduced at a high level. The history and evolution of reinforcement learning is presented, including key concepts like value and policy iteration. Also, the benefits and examples of using reinforcement learning in trading strategies is described. We also introduce LSTM and AutoML as additional tools in your toolkit to use in implementing trading strategies.
3 horas para completar
10 videos (Total 64 minutos), 1 lectura, 1 cuestionario
5 horas para completar
Neural Network Based Reinforcement Learning
In the previous module, reinforcement learning was discussed before neural networks were introduced. In this module, we look at how reinforcement learning has been integrated with neural networks. We also look at LSTMs and how they can be applied to time series data.
5 horas para completar
9 videos (Total 39 minutos)
4 horas para completar
Portfolio Optimization
In this module we discuss the practical steps required to create a reinforcement learning trading system. Also, we introduce AutoML, a powerful service on Google Cloud Platform for training machine learning models with minimal coding.
4 horas para completar
10 videos (Total 54 minutos)
Reseñas
Principales reseñas sobre REINFORCEMENT LEARNING FOR TRADING STRATEGIES
por JM18 de jul. de 2020
perhaps an applied trading notebook would have been nice...I understand that liability issues might have arisen, but there might have been a reasonable avenue with repeat disclaimers, etc
por ML13 de jul. de 2021
Provide the idea and method of RL for trading, but seems like less practice knowledge for the trading. hope can add more detail for for the trading build up. overall the course are good.
por SA10 de mar. de 2020
The course covers broad and important topics on using AI for trading, but one will need to dig more quite extensively on alternative sources to deepen one's understanding
por MS5 de mar. de 2020
It was easy to follow but not easy. I learned a lot and I now have the confidence to implement Reinforcement learning to my own FX trading strategies. Thank you so much.
Acerca de Programa especializado: Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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