Machine LearningFinanceTradingInvestmentAlgorithmic TradingPython ProgrammingReinforcement Learning Model DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
Machine Learning for Trading 專項課程
5,657 人已註冊！
您將學到的內容有
Understand the fundamentals of trading, including the concept of trend, returns, stop-loss and volatility
Use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks
Use Keras and Tensorflow to build machine learning models
Build trading strategies using Reinforcement Learning techniques
您將獲得的技能
關於此 專項課程
49,839 次近期查看
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).
應用的學習項目
The three courses will show you how to create various quantitative and algorithmic trading strategies using Python. By the end of the specialization, you will be able to create quantitative trading strategies that you can train, test and implement in live markets. You will also learn how to use reinforcement learning strategies to create algorithms that can update and train themselves
可分享的證書
完成後獲得證書
100% 在線課程
立即開始，按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
中級
完成時間大約為1 個月
建議 17 小時/週
英語（English）
字幕：英語（English）
專項課程的運作方式
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實踐項目
每個專項課程都包括實踐項目。您需要成功完成這個（些）項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程，則需要在開始之前完成其他所有課程。
獲得證書
在結束每門課程並完成實踐項目之後，您會獲得一個證書，您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。
此專項課程包含 3 門課程
Introduction to Trading, Machine Learning & GCP
285 個評分
This course is for finance professionals, investment management professionals, and traders. Alternatively, this course can be for machine learning professionals who seek to apply their craft to trading strategies.
Using Machine Learning in Trading and Finance
104 個評分
This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.
Reinforcement Learning for Trading Strategies
46 個評分
This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.
關於 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.
關於 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.
審閱
3.8
來自MACHINE LEARNING FOR TRADING的熱門評論
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 SA 提供Mar 10, 2020
The course covers broad and important topics on using AI for trading, but one will need to dig more quite extensively on alternative sources to deepen one's understanding
由 MS 提供Mar 5, 2020
It was easy to follow but not easy. I learned a lot and I now have the confidence to implement Reinforcement learning to my own FX trading strategies. Thank you so much.
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.
由 MA 提供Dec 25, 2019
Would be nice to have some extra step at the end of the lad to actually build a trading strategy instead of stopping at the fitting of the model
由 PL 提供Feb 28, 2020
The course is inspiring. It gave me another perspective of learning trading not just for Machine Learning also for day to day trading algorithm.
由 BA 提供Mar 15, 2020
Very good course us introduction to Trading, ML models for trading, ML, Neural networks concept and approaches, Google cloud platform.
由 MF 提供Jan 20, 2020
A good intro to machine learning in finance. I does not goes very deep, but hat some useful exercises and practice with google cloud.
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此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
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此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。
What background knowledge is necessary?
To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
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