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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/trading-strategies-reinforcement-learning
课程评论：没有评论
课程名称：基于强化学习的交易策略 课程概述：在“机器学习与交易”专业的最后一门课程中，您将被介绍到强化学习（RL），并学习强化学习在交易策略中的应用优势。课程将探讨如何将强化学习与神经网络结合，并回顾长短期记忆网络（LSTM）如何应用于时间序列数据。课程结束时，您将能够利用强化学习构建交易策略，区分基于行动者的策略和基于价值的策略，并将RL融入动量交易策略中。 成功完成本课程的前提条件包括：熟练掌握Python编程，并熟悉相关的机器学习库，如Scikit-Learn、StatsModels和Pandas。建议具备SQL经验。此外，您还应具备统计学背景（预期值与标准差、高斯分布、高阶矩、概率、线性回归）以及基础的金融市场知识（股票、债券、衍生品、市场结构、对冲）。 课程大纲： 1. **课程介绍和强化学习概述**：本模块高层次地介绍强化学习的历史和发展，包括价值迭代和策略迭代等关键概念，以及强化学习在交易策略中的应用优势和示例。同时，介绍LSTM和AutoML作为实施交易策略的额外工具。 2. **基于神经网络的强化学习**：在前一模块中讨论了强化学习后，本模块将探讨强化学习与神经网络的结合，以及LSTM如何应用于时间序列数据。 3. **投资组合优化**：本模块讨论创建强化学习交易系统所需的实际步骤，同时介绍Google Cloud Platform上的AutoML，它是一个可在最小编码的情况下训练机器学习模型的强大工具。 通过这门课程，参与者将深入了解如何应用强化学习来优化交易策略，并掌握相关技术和工具。
Name:Introduction to Course and Reinforcement Learning
Description:In this module, reinforcement learning is introduced at a high level. The history and evolution of reinforcement learning is presented, including key concepts like value and policy iteration. Also, the benefits and examples of using reinforcement learning in trading strategies is described. We also introduce LSTM and AutoML as additional tools in your toolkit to use in implementing trading strategies.
Name:Neural Network Based Reinforcement Learning
Description:In the previous module, reinforcement learning was discussed before neural networks were introduced. In this module, we look at how reinforcement learning has been integrated with neural networks. We also look at LSTMs and how they can be applied to time series data.
Name:Portfolio Optimization
Description:In this module we discuss the practical steps required to create a reinforcement learning trading system. Also, we introduce AutoML, a powerful service on Google Cloud Platform for training machine learning models with minimal coding.
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. 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).
