This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.
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
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
Understand the the structure and techniques used in reinforcement learning (RL) strategies
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
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 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 GS6 de Mar de 2020
Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn
por MS6 de Abr de 2020
It has good practical stuff, BUT not any practical RL related to trading.
Sobre Programa de cursos integrados Machine Learning for Trading
This Specialization 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. Alternatively, this specialization can be for machine learning professionals who seek to apply their craft to quantitative trading strategies.
The courses will teach you how to create various trading strategies using Python. By the end of the Specialization, you will be able to create quantitative trading strategies that you can train and implement. You will also learn how to use reinforcement learning strategies to create algorithms that can update and train themselves.
To be successful in this Specialization, you should have a basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and a basic knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
Qual é a política de reembolso?
Se você se inscrever, terá 7 dias para testar sem custo e, durante este período, pode cancelar sem multa. Depois disso, não reembolsamos, mas você pode cancelar sua inscrição a qualquer momento. Veja nossa política para o reembolso total.
Existe algum auxílio financeiro disponível?
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 .
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