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所在平台: Udemy |
课程主页: https://www.udemy.com/course/self-learning-trading-robot/
课程评论：没有评论
课程名称：懒惰交易第七部分：开发自适应交易系统 课程概述： “没人能保证这一定能成功，至少它能自我运行！”懒惰交易系列课程旨在结合算法交易的迷人体验，同时学习计算机和数据科学！特别关注构建决策支持系统，以帮助自动化与交易相关的许多繁琐流程，并学习数据科学。通过整个七个课程提供的示例，将展示如何构建一个具有自动进化能力的全面系统，且无需过多手动输入。 课程内容： 在本课程中，学员将学习如何利用统计建模开发自学习交易机器人。课程将涵盖使用深度学习回归模型预测金融资产未来价格的内容。整合之前讲解的所有知识点，内容包括： - 使用MQL4 DataWriter机器人收集金融资产数据 - 使用R统计软件汇总数据，以便进行建模 - 使用H2O机器学习平台训练深度学习回归模型 - 使用随机神经网络结构 - 提供R包中的函数测试和示例 - 使用模型预测和历史数据进行交易策略的回测，并在必要时更新模型 - 使用模型和新数据生成预测 - 将模型输出应用于MQL4交易机器人 - 添加和使用市场类型信息（来自第六部分课程） - 通过添加强化学习实验，选择最佳市场类型 - 尝试易于部署的复杂交易系统 课程特别之处： - 观察人工智能预测未来！ 该项目包含多个课程，专注于管理自动化交易系统，其中包括： - 设置家庭交易环境 - 设置交易策略机器人 - 设置自动化交易日志 - 统计自动化交易控制 - 阅读新闻和情绪分析 - 使用人工智能检测市场状态 - 构建AI交易系统 重要提示： 所有课程都将包含“快速部署”章节以及包含理论解释的章节。 学习收获： 除了交易外，完成这些课程后，学员将学习到更多内容： - 学习和实践使用决策支持系统 - 通过版本控制和自动统计分析保持组织性和系统性 - 学习使用R进行数据读取、操作和机器学习（包括深度学习） - 学习和实践数据可视化 - 学习情感分析和网页抓取 - 学习Shiny在数小时内部署任何数据项目 - 获取生产率技巧 - 学习自动化任务并安排日程 - 获取可扩展的MQL4和R代码示例 这系列课程的不足之处： 这些课程不会详细教授具体的编程概念，也不打算教授数据科学或交易的基础知识，且不保证无错误的编程。 免责声明： 交易存在风险。本课程不应被视为财务建议或服务。过去的结果不能保证未来的收益。重现所提出的方法和概念可能需要显著的时间投入。
"No one can promise that this will work, at least it will work by itself!"About the Lazy Trading Courses:This series of courses is designed to to combine fascinating experience of Algorithmic Trading and at the same time to learn Computer and Data Science! Particular focus is made on building Decision Support System that can help to automate a lot of boring processes related to Trading and also learn Data Science. Several algorithms will be built by performing basic data cycle 'data input-data manipulation - analysis -output'. Provided examples throughout all 7 courses will show how to build very comprehensive system capable to automatically evolve without much manual input.About this Course: Developing Self Learning Trading Robot with Statistical ModelingThis course will cover usage of Deep Learning Regression Model to predict future prices of financial asset. This course will blend everything that was previously explained to use:Use MQL4 DataWriter robot to gather financial asset dataUse R Statistical Software to aggregate data to be ready for modelingUse H2O Machine Learning Platform to train Deep Learning Regression ModelsUse random neural network structuresFunctions with test and examples in R packageBack-test trading strategy using Model prediction and historical data.update model if neededUse Model and New Data to generate predictionsUse Model output in MQL4 Trading RobotAdding and using Market Type info [from course 6]Experiment by adding Reinforcement Learning to select best possible Market TypeTry easy to deploy ready to use complex Trading System"What is that ONE thing very special about this course?"- Watch AI predicting the future!This project is containing several courses focused to help managing Automated Trading Systems:Set up your Home Trading EnvironmentSet up your Trading Strategy RobotSet up your automated Trading JournalStatistical Automated Trading ControlReading News and Sentiment AnalysisUsing Artificial Intelligence to detect market statusBuilding an AI trading systemIMPORTANT: all courses will have a 'quick to deploy' sections as well as sections containing theoretical explanations.What will you learn apart of trading:While completing these courses you will learn much more rather than just trading by using provided examples:Learn and practice to use Decision Support SystemBe organized and systematic using Version Control and Automated Statistical AnalysisLearn using R to read, manipulate data and perform Machine Learning including Deep LearningLearn and practice Data VisualizationLearn sentiment analysis and web scrappingLearn Shiny to deploy any data project in hoursGet productivity hacksLearn to automate your tasks and scheduling themGet expandable examples of MQL4 and R codeWhat these courses are not:These courses will not teach and explain specific programming concepts in detailsThese courses are not meant to teach basics of Data Science or TradingThere is no guarantee on bug free programmingDisclaimer:Trading is a risk. This course must not be intended as a financial advice or service. Past results are not guaranteed for the future. Significant time investment may be required to reproduce proposed methods and concepts
