课程信息

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100% 在线
立即开始,按照自己的计划学习。
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

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纽约金融学院

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Google 云端平台

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 3 小时

Introduction to Course and Reinforcement Learning

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
2

2

完成时间为 5 小时

Neural Network Based Reinforcement Learning

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
3

3

完成时间为 4 小时

Portfolio Optimization

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

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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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