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
46,418 次近期查看
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第 1 门课程（共 3 门）
可灵活调整截止日期
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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9 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 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
FinanceTradingInvestmentMachine Learning applied to Finance
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）
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9 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 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.
教学大纲 - 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
26 个视频
Course Overview Introduction to Trading with Machine Learning on Google Cloud5分钟
What is AI and ML ? What is the difference between AI and ML?58
Applications of ML in the Real World1分钟
What is ML?3分钟
Game: The importance of good data4分钟
Brief History of ML in Quantitative Finance11分钟
Why Google?1分钟
Why Google Cloud Platform?2分钟
What are AI Platform Notebooks1分钟
Using Notebooks1分钟
Benefits of AI Platform Notebooks2分钟
What do we want to model? Let's start simple5分钟
Demo: Building a model with BigQuery ML25分钟
How to use Qwiklabs for your Labs3分钟
Lab Intro: Building a Regression Model37
Lab Walkthrough: Building a Regression Model9分钟
Trading vs Investing6分钟
The Quant Universe2分钟
Quant Strategies7分钟
Quant Trading Advantages and Disadvantages4分钟
Exchange and Statistical Arbitrage8分钟
Index Arbitrage2分钟
Statistical Arbitrage Opportunities and Challenges5分钟
Introduction to Backtesting5分钟
Backtesting Design6分钟
3 个阅读材料
Supervised Learning and Regression10分钟
Welcome to Introduction to Trading, Machine Learning and GCP10分钟
Case Study: Capital Markets in the Cloud10分钟
4 个练习
Python Skills Assessment Quiz
AI and Machine Learning5分钟
Google Cloud
Trading Concepts Review15分钟
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
6 个视频
What is forecasting? - part 24分钟
Choosing the right model and BQML - part 13分钟
Choosing the right model and BQML - part 22分钟
Lab Intro: Forecasting Stock Prices using Regression in BQML36
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML12分钟
1 个阅读材料
Staying current with BigQuery ML model types10分钟
1 个练习
Forecasting
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）
11 个视频
AR - Auto Regressive7分钟
MA - Moving Average2分钟
The Complete ARIMA Model4分钟
ARIMA compared to linear regression7分钟
How can you get a variety of models from just a single series?1分钟
How to choose ARIMA parameters for your trading model4分钟
Time Series Terminology: Auto Correlation4分钟
Sensitivity of Trading Strategy4分钟
Lab Intro: Forecasting Stock Prices Using ARIMA32
Lab Walkthrough: Forecasting Stock Prices using ARIMA7分钟
1 个练习
Time Series
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
5 个视频
Short history of ML: Modern Neural Networks8分钟
Overfitting and Underfitting6分钟
Validation and Training Data Splits4分钟
Course Recap + Preview of next course 1分钟
1 个阅读材料
Example BigQuery ML DNN code10分钟
2 个练习
Model generalization
Recap Quiz8分钟
审阅
来自INTRODUCTION TO TRADING, MACHINE LEARNING & GCP的热门评论
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 LA 提供Jun 2, 2020
Good introduction to quant theory and ML, labs could be a lot better though, they lack proper explanations and don't cover some of the basics necessary to complete them.
由 DG 提供May 4, 2020
The lectures appear to jump around a bit. Looks like it was stitched together from different places. So the course lacks a continuity I have seen in other courses.
由 GG 提供Feb 1, 2020
Giving 4 stars as there were some technical problems with AI Platform in week 3 and could not access the lab work, which is pretty disappointing.
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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