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 long-term trading strategies, short-term trading strategies, and hedging strategies. 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 foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
关于此 专项课程
100% 在线课程
灵活的计划
中级
完成时间大约为1 个月
英语（English）
专项课程的运作方式
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实践项目
每个专项课程都包括实践项目。您需要成功完成这个（些）项目才能完成专项课程并获得证书。如果专项课程中包括单独的实践项目课程，则需要在开始之前完成其他所有课程。
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在结束每门课程并完成实践项目之后，您会获得一个证书，您可以向您的潜在雇主展示该证书并在您的职业社交网络中分享。
此专项课程包含 3 门课程
Introduction to Trading, Machine Learning & GCP
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. At the end of the course you will be able to do the following: - Understand the fundamentals of trading, including the concept of trend, returns, stop-loss and volatility - Understand the differences between supervised/unsupervised and regression/classification machine learning models - Identify the profit source and structure of basic quantitative trading strategies - Gauge how well the model generalizes its learning - Explain the differences between regression and forecasting - Identify the steps needed to create development and implementation backtesters - Use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks To be successful in this course, 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 foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
Using Machine Learning in Trading and Finance
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. At the end of the course you will be able to do the following: - Design basic quantitative trading strategies - Use Keras and Tensorflow to build machine learning models - Build a pair trading strategy prediction model and back test it - Build a momentum-based trading model and back test it 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).
Reinforcement Learning for Trading Strategies
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. At the end of the course you will be able to do the following: - Understand what reinforcement learning is and how trading is an RL problem - Build Trading Strategies Using Reinforcement Learning (RL) - Understand the benefits of using RL vs. other learning methods - Differentiate between actor-based policies and value-based policies - Incorporate RL into a momentum trading strategy 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).
讲师
Jack Farmer
Curriculum Director
Ram Seshadri
Machine Learning Consultant关于 Google 云端平台
关于 New York Institute of Finance
常见问题
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此课程是 100% 在线学习吗？是否需要现场参加课程？
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完成专项课程后我会获得大学学分吗？
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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