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
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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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível 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 DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível 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 vídeos (Total 64 mín.), 1 leitura, 1 teste
10 videos
What is Reinforcement Learning?9min
History Overview2min
Value Iteration9min
Policy Iteration6min
TD Learning8min
Q Learning6min
Benefits of Reinforcement Learning in Your Trading Strategy6min
DRL Advantages for Strategy Efficiency and Performance7min
Introduction to Qwiklabs3min
1 leituras
Idiosyncrasies and challenges of data driven learning in electronic trading10min
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 vídeos (Total 39 mín.)
9 videos
TD-Gammon3min
Deep Q Networks - Loss2min
Deep Q Networks Memory2min
Deep Q Networks - Code3min
Policy Gradients4min
Actor-Critic3min
What is LSTM?7min
More on LSTM4min
Applying LSTM to Time Series Data7min
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 vídeos (Total 54 mín.)
10 videos
Steps Required to Develop a DRL Strategy7min
Final Checks Before Going Live with Your Strategy5min
Investment and Trading Risk Management4min
Trading Strategy Risk Management4min
Portfolio Risk Reduction4min
Why AutoML?13min
AutoML Vision2min
AutoML NLP3min
AutoML Tables7min
Avaliações
Principais avaliações do 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 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.
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
O acesso a palestras e tarefas depende do tipo de inscrição. Se você participar de um curso como ouvinte, você poderá ver quase todo o conteúdo do curso gratuitamente. Para acessar tarefas valendo nota e obter um Certificado, você precisará adquirir a experiência do Certificado, durante ou após a participação como ouvinte. Se você não vir a opção de participar como ouvinte:
- o curso pode não oferecer essa opção. Você pode experimentar um teste gratuito ou solicitar o auxílio financeiro.
- Em vez disso, o curso pode oferecer 'Curso completo, sem Certificado'. Com esta opção, é possível ver todo o conteúdo do curso, enviar as avaliações necessárias e obter uma nota final. Isso também significa que você não poderá comprar uma experiência de Certificado.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Is financial aid available?
Sim, a Coursera oferece auxílio financeiro ao aluno que não possa pagar a taxa. Faça a solicitação clicando no link Auxílio Financeiro, abaixo do botão "Inscreva-se" à esquerda. Preencha uma solicitação e será notificado caso seja aprovado. Você terá que completar esta etapa para cada curso na Especialização, incluindo o Trabalho de Conclusão de Curso. Saiba mais .
Ganharei créditos universitários por concluir o curso?
Este curso não oferece créditos universitários, mas algumas universidades podem aceitar certificados de cursos que podem ser convertidos em créditos. Entre em contato com sua instituição para saber mais. Com os cursos on-line e os certificados Mastertrack™ do Coursera, é possível ganhar créditos universitários.
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