课程信息

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可分享的证书

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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

完成时间大约为13 小时

建议:19 hours/week...

英语(English)

字幕:英语(English)

您将学到的内容有

  • Understand the the structure and techniques used in reinforcement learning (RL) strategies

  • Describe the steps required to develop and test an RL trading strategy

  • Describe the methods used to optimize an RL trading strategy

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

完成时间大约为13 小时

建议:19 hours/week...

英语(English)

字幕:英语(English)

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 Specialization 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. Alternatively, this specialization can be for machine learning professionals who seek to apply their craft to quantitative trading strategies. The courses will teach you how to create various trading strategies using Python. By the end of the Specialization, you will be able to create quantitative trading strategies that you can train and implement. You will also learn how to use reinforcement learning strategies to create algorithms that can update and train themselves. To be successful in this Specialization, you should have a basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and a basic knowledge of financial markets (equities, bonds, derivatives, market structure, hedging)....
Machine Learning for Trading

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