Reinforcement Learning for Trading Strategies

所在平台: CourseraArchive

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大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/trading-strategies-reinforcement-learning

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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).

交易策略强化学习:本课程适用于金融专业人士,投资管理专业人士和交易员。或者,该专业化课程可用于寻求将自己的手艺应用于交易策略的机器学习专业人员。 在课程结束时,您将可以执行以下操作: -了解什么是强化学习以及交易如何是RL问题 -使用强化学习(RL)制定交易策略 -了解使用RL与其他学习方法相比的优势 -区分基于参与者的策略和基于价值的策略 -将RL纳入动量交易策略 为使本课程取得成功,您应具备Python编程的基本能力,并熟悉Scikit Learn,Statsmodels和Pandas库。您应具有统计学背景(期望值和标准差,高斯分布,较高矩,概率,线性回归)和金融市场基础知识(股票,债券,衍生工具,市场结构,对冲)。

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