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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-methods-reinforcement-learning-finance
课程评论：没有评论
课程名称：金融领域高级强化学习方法概述 课程概述：在我们的专业课程的最后一门课程——金融领域高级强化学习方法概述中，我们将深入探讨在第三门课程《金融中的强化学习》中讨论的主题。特别地，我们将讨论强化学习与期权定价及物理学之间的联系，逆向强化学习对市场影响和价格动态建模的影响，以及强化学习中的感知-行动循环。最后，我们将概述强化学习在高频交易、加密货币、点对点借贷等领域的趋势和潜在应用。 完成本课程后，学生将能够： - 解释金融的基本概念，例如市场均衡、无套利和可预测性； - 讨论市场建模； - 将强化学习的方法应用于高频交易、信用风险、点对点借贷及加密货币交易。 课程大纲： - 名称：布莱克-斯科尔斯-默顿模型，物理学与强化学习 - 描述：探讨布莱克-斯科尔斯-默顿模型及其与物理学和强化学习的关系。 - 名称：优化交易和市场建模的强化学习 - 描述：研究如何利用强化学习优化交易策略和市场建模。 - 名称：感知 - 超越强化学习 - 描述：分析强化学习中的感知-行动过程及其超越传统强化学习的应用。 - 名称：强化学习的其他应用：点对点借贷、加密货币等 - 描述：探讨强化学习在点对点借贷和加密货币等领域的应用。
Name:Black-Scholes-Merton model, Physics and Reinforcement Learning
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Name:Reinforcement Learning for Optimal Trading and Market Modeling
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Name:Perception - Beyond Reinforcement Learning
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Name:Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
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In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high-frequency trading, cryptocurrencies, peer-to-peer lending, and more. After taking this course, students will be able to - explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability, - discuss market modeling, - Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.
