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所在平台: Udemy |
课程主页: https://www.udemy.com/course/forex-news-and-sentiment-analysis/
课程评论：没有评论
课程名称：懒惰交易第五部分：阅读外汇新闻与情绪分析 课程概述： 本课程旨在提升您对外汇市场的理解，通过自动读取外汇日历中的重要事件信息，例如美国非农就业数据或特朗普演讲时间，为您的交易策略提供支持。您将学习如何在交易机器人的操作中简单地禁用相关策略。 此外，课程还包括对资产情感数据与未来价格之间的相关性进行深入研究，具体围绕两个交易思路进行：1) 美国、加拿大和英国新闻标题情感差异与其货币对的关联性；2) 与特斯拉股票价格相关的推特数据情感分析。 课程的主要内容包括： - 网络爬虫新闻并分析其情感以支持交易 - 在项目中设置版本控制 - 自动化R代码的设置 - 基于情感分析极性得分和NRC情感词典（8种情感）进行文本情感分析 - 对新闻标题情感极性得分进行描述性分析 - 将推特数据导入R - 使用深度回归学习将情感得分与目标变量关联（在h2o深度学习环境中进行） 请注意，本课程提出的方法并不一定保证有效。 懒惰交易课程系列旨在结合算法交易的趣味经验与计算机和数据科学的学习。它特别关注决策支持系统的建立，以帮助自动化交易相关的繁琐流程。该系列包括多门短课程，聚焦于帮助您管理自动化交易系统，例如： - 设置家庭交易环境 - 配置交易策略机器人 - 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：课程将集中在特定主题上，提供非常简短的理论解释，旨在帮助您通过自动化实现交易策略的开发。 学习收获： 完成这些课程后，您将学到的不仅仅是交易知识，还包括： - 使用决策支持系统的技巧 - 有条理、系统地运用版本控制和自动化统计分析 - 使用R语言读取、处理数据及进行机器学习（包括深度学习） - 数据可视化技巧 - 情感分析与网络爬虫的基本知识 - 快速部署数据项目的Shiny应用 - 提高工作效率的小技巧 - 自动化任务与调度的能力 - 可扩展的MQL4和R代码示例 课程不包括的内容： - 不会详细讲解特定编程概念 - 不会教授数据科学或交易的基础知识 - 不能保证无错误的编程 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，过往表现不代表未来结果的保证。
About this Course: Read news and Sentiment AnalysisThe fifth part of this series will give you the ability to automatically read Forex Calendar for any specific event like US Non-Farm Payroll or when President Trump is going to have a speech. This will provide an ability to consider these events in your trading strategies in a simplest form of disabling the trading robots.Additional research of this course will be about correlation of Asset's Text data Sentiment to the Asset's price in the future. This research will be conducted on two trading ideas*:Sentiment difference of News Headers in the US, Canada, GB and it's their currency Pairs. Sentiment of Twitter data relevant to Tesla Stock pricesAs usual provided methods and ideas will help us to practice computer and data science skills:Webscrap news and analyse their sentiment for tradingSetting up Version Control in our ProjectsKnow how to automate our R codeText Sentiment analysis using basic Sentiment Analysis Polarity Scoring and NRC Sentiment Dictionary (8 emotions)Performing descriptive analysis of the Sentiment Polarity Scoring of the News HeadersGetting Twitter data into RDeep regression learning to correlate Sentiment scores to the objective variable [performed in h2o deep learning environment]*There is absolutely no guarantee that proposed methods will work!!!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 foundation of Decision Support System that can help to automate a lot of boring processes related to Trading.This project is containing several short 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 systemUpdate: dedicated R package 'lazytrade' was created to facilitate code sharing among different coursesIMPORTANT: All courses will be short focusing to one specific topic with very short theoretical explanations. These courses will help to focus on developing strategies by automating boring but important processes for a trader.Best possible way to take the courses as a series is to reproduce all methods by re-creating automated trading system on PC WindowsWhat 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.
