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.
Ce cours fait partie de la Spécialisation Machine Learning for Trading
Offert par
New York Institute of Finance
Google Cloud
Spécialisation Machine Learning for TradingNew York Institute of FinanceÀ propos de ce cours
23,064 consultations récentes
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Reinforcement Learning Model DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
10 vidéos (Total 64 min), 1 lecture, 1 quiz
10 vidéos
What is Reinforcement Learning?9 min
History Overview2 min
Value Iteration9 min
Policy Iteration6 min
TD Learning8 min
Q Learning6 min
Benefits of Reinforcement Learning in Your Trading Strategy6 min
DRL Advantages for Strategy Efficiency and Performance7 min
Introduction to Qwiklabs3 min
1 lecture
Idiosyncrasies and challenges of data driven learning in electronic trading10 min
5 heures pour terminer
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 heures pour terminer
9 vidéos (Total 39 min)
9 vidéos
TD-Gammon3 min
Deep Q Networks - Loss2 min
Deep Q Networks Memory2 min
Deep Q Networks - Code3 min
Policy Gradients4 min
Actor-Critic3 min
What is LSTM?7 min
More on LSTM4 min
Applying LSTM to Time Series Data7 min
4 heures pour terminer
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 heures pour terminer
10 vidéos (Total 54 min)
10 vidéos
Steps Required to Develop a DRL Strategy7 min
Final Checks Before Going Live with Your Strategy5 min
Investment and Trading Risk Management4 min
Trading Strategy Risk Management4 min
Portfolio Risk Reduction4 min
Why AutoML?13 min
AutoML Vision2 min
AutoML NLP3 min
AutoML Tables7 min
Avis
Meilleurs avis pour REINFORCEMENT LEARNING FOR TRADING STRATEGIES
par MS5 mars 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.
par GS6 mars 2020
Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn
par MS6 avr. 2020
It has good practical stuff, BUT not any practical RL related to trading.
À propos du Spécialisation 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).
Foire Aux Questions
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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.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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