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第 1 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9 小时
英语(English)
字幕:英语(English)

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:英语(English)

提供方

Google 云端平台 徽标

Google 云端平台

纽约金融学院 徽标

纽约金融学院

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up88%(1,634 个评分)Info
1

1

完成时间为 4 小时

Introduction to Trading with Machine Learning on Google Cloud

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
2

2

完成时间为 3 小时

Supervised Learning with BigQuery ML

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
3

3

完成时间为 2 小时

Time Series and ARIMA Modeling

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
4

4

完成时间为 1 小时

Introduction to Neural Networks and Deep Learning

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

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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....
Machine Learning for Trading

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