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.
提供方
纽约金融学院
Google 云端平台
课程信息
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第 3 门课程（共 3 门）
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中级
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
完成时间大约为12 小时
英语（English）
字幕：英语（English）
您将学到的内容有
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.
您将获得的技能
Reinforcement Learning Model DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 3 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
完成时间大约为12 小时
英语（English）
字幕：英语（English）
提供方
纽约金融学院
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 云端平台
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
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.
完成时间为 3 小时
10 个视频 （总计 64 分钟）, 1 个阅读材料, 1 个测验
10 个视频
What is Reinforcement Learning?9分钟
History Overview2分钟
Value Iteration9分钟
Policy Iteration6分钟
TD Learning8分钟
Q Learning6分钟
Benefits of Reinforcement Learning in Your Trading Strategy6分钟
DRL Advantages for Strategy Efficiency and Performance7分钟
Introduction to Qwiklabs3分钟
1 个阅读材料
Idiosyncrasies and challenges of data driven learning in electronic trading10分钟
完成时间为 5 小时
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.
完成时间为 5 小时
9 个视频 （总计 39 分钟）
9 个视频
TD-Gammon3分钟
Deep Q Networks - Loss2分钟
Deep Q Networks Memory2分钟
Deep Q Networks - Code3分钟
Policy Gradients4分钟
Actor-Critic3分钟
What is LSTM?7分钟
More on LSTM4分钟
Applying LSTM to Time Series Data7分钟
完成时间为 4 小时
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.
完成时间为 4 小时
10 个视频 （总计 54 分钟）
10 个视频
Steps Required to Develop a DRL Strategy7分钟
Final Checks Before Going Live with Your Strategy5分钟
Investment and Trading Risk Management4分钟
Trading Strategy Risk Management4分钟
Portfolio Risk Reduction4分钟
Why AutoML?13分钟
AutoML Vision2分钟
AutoML NLP3分钟
AutoML Tables7分钟
审阅
来自REINFORCEMENT LEARNING FOR TRADING STRATEGIES的热门评论
由 JM 提供Jul 18, 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
由 SA 提供Mar 10, 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
由 MS 提供Mar 5, 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.
由 SF 提供Mar 14, 2020
Good course introducing concepts in RL. Wish course provided more examples of using RL in stock prediction.
关于 Machine Learning for Trading 专项课程
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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