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所在平台: Udemy |
课程主页: https://www.udemy.com/course/your-home-trading-environment/
课程评论：没有评论
课程名称：懒人交易第一部分：在Windows 10上设置家庭计算机 课程概述： “机遇是留给有准备的人的。” - 塞内卡 懒人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！尤其关注建立决策支持系统，帮助自动化与交易相关的许多繁琐过程，并学习数据科学。在七门课程中，我们将通过执行基本数据周期（数据输入 - 数据处理 - 数据分析 - 输出）的方式构建多个算法。提供的示例将展示如何构建一个能够在没有大量人工输入的情况下自动演变的综合系统。 课程内容： 本课程将涵盖如何使用Windows 10操作系统设置个人家庭交易环境。在课程结束时，您将在个人计算机上安装并激活一个交易环境。该交易环境将成为整个系列课程中模块化系统的基础。我们将讨论以下主题： - 选择硬件（Windows PC） - 学习Windows 10中的基本管理工具 - 安装和准备必要的软件，包括Meta Trader 4平台 - 建立交易策略和工具的版本控制工具 - 概述旨在自动化交易决策的更大策略 - 设置开发、测试和生产交易终端 - 使用R统计软件和‘lazytrade’包建立决策支持系统 - 使交易环境更能抵御外部因素的影响 课程的独特之处： - 设置计算机，使其随时随地可用！ 该项目包含多个课程，旨在帮助您管理自动化交易系统： - 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 - 统计自动交易控制 - 阅读新闻与情感分析 - 使用人工智能检测市场状态 - 构建AI交易系统 重要提示：所有课程将包含“快速部署”部分以及包含理论解释的部分。 除了交易，您将学习到： - 使用决策支持系统进行学习和实践 - 运用版本控制和自动化统计分析实现系统化 - 学习使用R进行数据读取、处理和机器学习，包括深度学习 - 学习和实践数据可视化 - 学习情感分析和网络抓取 - 学习Shiny在数小时内部署任何数据项目 - 获取提高生产率的技巧 - 学会自动化任务及其调度 - 获取可扩展的MQL4和R代码示例 这些课程不包括： - 详细讲解具体编程概念 - 教授数据科学或交易的基础知识 - 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结果。重现所提议的方法和概念可能需要 significant 的时间投入。
"Luck is a preparation to Opportunity" - SenecaAbout 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: Set up your Trading EnvironmentThis course will cover setting up personal Home Trading Environment using Windows 10 Operating System. At the end of this course we will have a Trading Environment installed and active on the Home Computer. This trading environment will be a basis of a modular system which will be completed during the whole series of courses. We will cover the following topics:Choosing a hardware (Windows PC)Learning about basic administrative tools in Windows 10Install and prepare needed software including Meta Trader 4 PlatformEstablish Version Control Tools for Trading Strategies and toolsOutline the Bigger Strategy that aims to automate decisions of the TraderSet up Development, Test and Production Trading TerminalsEstablish Decision Support System using R statistical software and package 'lazytrade'Making Trading Environment more robust to external factors"What is that ONE thing very special about this course?"- Setting up the computer to be ready 24/7!This project is containing several courses focused to help you managing your 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
