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 faz parte do Programa de cursos integrados Machine Learning for Trading
12.172 já se inscreveram
oferecido por
New York Institute of Finance
Google Cloud
Programa de cursos integrados Machine Learning for TradingNew York Institute of FinanceInformações sobre o curso
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Curso 3 de 3 no
NÃvel intermediário
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
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 você terá
- Reinforcement Learning Model Development
- Reinforcement Learning Trading Algorithm Optimization
- Reinforcement Learning Trading Strategy Development
- Reinforcement Learning Trading Algo Development
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
NÃvel intermediário
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
oferecido por
New York Institute of Finance
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 - O que você aprenderá com este curso
3 horas para concluir
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 concluir
10 vÃdeos (Total 64 mÃn.), 1 leitura, 1 teste
5 horas para concluir
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 concluir
9 vÃdeos (Total 39 mÃn.)
4 horas para concluir
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 concluir
10 vÃdeos (Total 54 mÃn.)
Avaliações
- 5 stars35,05%
- 4 stars26,80%
- 3 stars19,07%
- 2 stars7,21%
- 1 star11,85%
Principais avaliações do REINFORCEMENT LEARNING FOR TRADING STRATEGIES
por MS6 de abr de 2020
It has good practical stuff, BUT not any practical RL related to trading.
por GY1 de jul de 2022
I look forward to examples of integration of decision based on reinforcement learning and algo-trading logic
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 SF14 de mar de 2020
Good course introducing concepts in RL. Wish course provided more examples of using RL in stock prediction.
Sobre Programa de cursos integrados 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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