In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.
提供方
Google 云端平台
纽约金融学院
Machine Learning for Trading 专项课程Google 云端平台课程信息
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第 1 门课程（共 3 门）
中级
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
完成时间大约为9 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将学到的内容有
Understand the fundamentals of trading, including the concepts of trend, returns, stop-loss, and volatility.
Define quantitative trading and the main types of quantitative trading strategies.
Understand the basic steps in exchange arbitrage, statistical arbitrage, and index arbitrage.
Understand the application of machine learning to financial use cases.
您将获得的技能
- Finance
- Trading
- Investment
- Machine Learning applied to Finance
可灵活调整截止日期
根据您的日程表重置截止日期。
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 1 门课程（共 3 门）
中级
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
完成时间大约为9 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
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.
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.
授课大纲 - 您将从这门课程中学到什么
完成时间为 4 小时
Introduction to Trading with Machine Learning on Google Cloud
In this module you will be introduced to the fundamentals of trading. You will also be introduced to machine learning. Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of problems.
完成时间为 4 小时
26 个视频 （总计 131 分钟）, 3 个阅读材料, 5 个测验
完成时间为 3 小时
Supervised Learning with BigQuery ML
In this module you will be introduced to supervised machine learning and some relevant algorithms commonly applied to trading problems. You will get some hands-on experience building a regression model using BigQuery Machine Learning
完成时间为 3 小时
6 个视频 （总计 29 分钟）, 1 个阅读材料, 3 个测验
完成时间为 2 小时
Time Series and ARIMA Modeling
In this module you will learn about ARIMA modeling and how it is applied to time series data. You will get hands-on experience building an ARIMA model for a financial dataset.
完成时间为 2 小时
11 个视频 （总计 52 分钟）
完成时间为 1 小时
Introduction to Neural Networks and Deep Learning
In this module you'll learn about neural networks and how they relate to deep learning. You'll also learn how to gauge model generalization using regularization, and cross-validation. Also, you'll be introduced to Google Cloud Platform (GCP). Specifically, you'll be shown how to leverage GCP for implementing trading techniques.
完成时间为 1 小时
5 个视频 （总计 29 分钟）, 1 个阅读材料, 2 个测验
审阅
- 5 stars43.68%
- 4 stars30.24%
- 3 stars15.05%
- 2 stars4.43%
- 1 star6.58%
来自INTRODUCTION TO TRADING, MACHINE LEARNING & GCP的热门评论
由 MF 提供Jan 20, 2020
A good intro to machine learning in finance. I does not goes very deep, but hat some useful exercises and practice with google cloud.
由 MS 提供Jan 29, 2020
Excellent! But, I am missing some of the prerequisites since I just wanted to take a chance and try things out, but feel like proceeding further might lead to some stumbling blocks.
由 BP 提供Apr 3, 2020
Not as much coding as I would have wanted, or atleast exposure to code. Very solid historical context though.
由 AA 提供Jan 12, 2020
Good course that gives a lot of breadth as an introduction to machine learning in finance. Well put together
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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