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所在平台: Udemy |
课程主页: https://www.udemy.com/course/classification-based-machine-learning-for-trading-in-r/
课程评论：没有评论
课程名称：应用机器学习与R（交易应用案例） - 2020 课程概述：本课程旨在全面让学员融入量化交易/金融工作流程，从假设生成到数据准备、特征工程及多个机器学习算法的训练与测试（回测）。课程通过Bootcamp的形式，帮助学员从零基础提升到熟练运用R进行量化交易分析。课程内容包括对交易的理解，区分自我决策交易与量化交易，并且介绍不同的交易工具/产品或资产类别。 课程内容： - 学习交易及量化交易工作流程，深入了解量化交易分析所需的知识及其优缺点。 - 学习如何编写简单和复杂的R代码，包括R语言基础的复习。 - 学习如何使用quantmod包从雅虎财经等来源获取和加载免费市场数据。 - 学习如何从NinjaTrader下载期货数据，将数据导入R，并进行数据准备和可视化。 - 探索网络上的各种交易想法/假设，学习如何生成原创交易思路。 - 理解机器学习的概念，并掌握解决分类和回归问题的机器学习算法。 - 代码实践，学习特征工程，编写支持向量机、朴素贝叶斯和随机森林模型的训练与测试算法，以预测原油期货的价格走势。 - 比较模型性能，并通过选择非相关模型进行资产组合选择。 免责声明：本课程仅用于教育目的，并不构成交易或投资建议。所有内容、教学材料和代码均为分享和学习目的，不保证准确性或完整性。过去的表现不能代表未来表现，课程中展示的交易策略基于假设和历史回测。期货、外汇及期权交易具有损失风险。请仔细考虑交易是否适合您的财务状况，仅用可承受损失的资金进行投资，需谨慎对待固有风险。
The course is designed to fully immerse you into the complete quantitative trading/finance workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero using R. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.Course elements:Learn about trading and the quantitative trading workflow. Develop a solid understand of what is required to do quantitative trading analysis and the advantages and disadvantages.Learn how to write simple and complex codes in r with some r refresher lecture. Learn how to use the quantmod package to access/load free market data from yahoo finance and other sources.Learn how to download futures data from NinjaTrader. Load the data in R and do data preparation and visualization.Explore various trading ideas/hypothesis on the web, and learn how to generate original trading ideas. Learn and understand what machine learning is and get a good grip of the type of machine learning algorithms available to solve different type of problems ( namely classification and regression problems).Code along while learning about feature engineering, write algorithms for training and testing support vector machine, naïve bayes and random forest models and use these to predict the next price direction of crude oil futures. Realize that these strategies can be used for other trading instruments/products.Compare the model performance and do portfolio selection by only selecting the non correlated models.DisclaimerThis course is for educational purpose and does not constitute trading or investment advice. All content, teaching material and codes are presented with sharing and learning purpose and with no guarantee of exactness or completeness.No past performance is indicative of future performance and the trading strategies presented here are based on hypothetical and historical backtesting. Trading futures, forex and options involves the risk of loss. Please consider carefully if trading is appropriate to your financial situation. Only risk capital you can afford to lose, and the risk of loss being substantial, you should consider carefully the inherent risks.
