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.
Этот курс входит в специализацию ''Специализация Machine Learning for Trading'
от партнера
Google Cloud
Нью-Йоркский институт финансов
Специализация Machine Learning for TradingGoogle CloudОб этом курсе
Недавно просмотрено: 60,052
Карьерные результаты учащихся
14%
получил значимые преимущества в карьере благодаря этому курсу
20%
стал больше зарабатывать или получил повышение
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 1 из 3 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Средний уровень
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
Прибл. 9 часов на выполнение
Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
Чему вы научитесь
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.
Приобретаемые навыки
FinanceTradingInvestmentMachine Learning applied to Finance
Карьерные результаты учащихся
14%
получил значимые преимущества в карьере благодаря этому курсу
20%
стал больше зарабатывать или получил повышение
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 1 из 3 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Средний уровень
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
Прибл. 9 часов на выполнение
Английский
Субтитры: Французский, Португальский (Европа), Русский, Английский, Испанский
от партнера
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.
Нью-Йоркский институт финансов
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.
Программа курса: что вы изучите
4 ч. на завершение
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.
4 ч. на завершение
26 видео ((всего 131 мин.)), 3 материалов для самостоятельного изучения, 5 тестов
26 видео
Course Overview Introduction to Trading with Machine Learning on Google Cloud5мин
What is AI and ML ? What is the difference between AI and ML?58
Applications of ML in the Real World1мин
What is ML?3мин
Game: The importance of good data4мин
Brief History of ML in Quantitative Finance11мин
Why Google?1мин
Why Google Cloud Platform?2мин
What are AI Platform Notebooks1мин
Using Notebooks1мин
Benefits of AI Platform Notebooks2мин
What do we want to model? Let's start simple5мин
Demo: Building a model with BigQuery ML25мин
How to use Qwiklabs for your Labs3мин
Lab Intro: Building a Regression Model37
Lab Walkthrough: Building a Regression Model9мин
Trading vs Investing6мин
The Quant Universe2мин
Quant Strategies7мин
Quant Trading Advantages and Disadvantages4мин
Exchange and Statistical Arbitrage8мин
Index Arbitrage2мин
Statistical Arbitrage Opportunities and Challenges5мин
Introduction to Backtesting5мин
Backtesting Design6мин
3 материала для самостоятельного изучения
Supervised Learning and Regression10мин
Welcome to Introduction to Trading, Machine Learning and GCP10мин
Case Study: Capital Markets in the Cloud10мин
4 практических упражнения
Python Skills Assessment Quiz
AI and Machine Learning5мин
Google Cloud
Trading Concepts Review15мин
3 ч. на завершение
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
3 ч. на завершение
6 видео ((всего 29 мин.)), 1 материал для самостоятельного изучения, 3 тестов
6 видео
What is forecasting? - part 24мин
Choosing the right model and BQML - part 13мин
Choosing the right model and BQML - part 22мин
Lab Intro: Forecasting Stock Prices using Regression in BQML36
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML12мин
1 материал для самостоятельного изучения
Staying current with BigQuery ML model types10мин
1 практическое упражнение
Forecasting
2 ч. на завершение
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.
2 ч. на завершение
11 видео ((всего 52 мин.))
11 видео
AR - Auto Regressive7мин
MA - Moving Average2мин
The Complete ARIMA Model4мин
ARIMA compared to linear regression7мин
How can you get a variety of models from just a single series?1мин
How to choose ARIMA parameters for your trading model4мин
Time Series Terminology: Auto Correlation4мин
Sensitivity of Trading Strategy4мин
Lab Intro: Forecasting Stock Prices Using ARIMA32
Lab Walkthrough: Forecasting Stock Prices using ARIMA7мин
1 практическое упражнение
Time Series
1 ч. на завершение
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.
1 ч. на завершение
5 видео ((всего 29 мин.)), 1 материал для самостоятельного изучения, 2 тестов
5 видео
Short history of ML: Modern Neural Networks8мин
Overfitting and Underfitting6мин
Validation and Training Data Splits4мин
Course Recap + Preview of next course 1мин
1 материал для самостоятельного изучения
Example BigQuery ML DNN code10мин
2 практических упражнения
Model generalization
Recap Quiz8мин
Рецензии
Лучшие отзывы о курсе INTRODUCTION TO TRADING, MACHINE LEARNING & GCP
от партнера MS29 янв. 2020 г.
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.
от партнера LA2 июня 2020 г.
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.
от партнера AJ20 нояб. 2020 г.
I thought this was excellent. Some familiarity with standard SQL is needed to get the most benefit from the materials, and the course is clearly aimed at GCP users.
от партнера DG4 мая 2020 г.
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.
Специализация Machine Learning for Trading: общие сведения
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.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Is financial aid available?
Получу ли я зачеты в университете за прохождение курса?
Остались вопросы? Посетите Центр поддержки учащихся.
