Machine Learning for Finance (with R)培训

课程编码

mlfinancer

课程时长

28 小时 通常来说是4天,包括中间休息。

要求

  • Programming experience with any language
  • Basic familiarity with statistics and linear algebra

课程概览

机器学习是人工智能的一个分支,其中计算机具有学习能力而无需明确编程。 R是金融行业中流行的编程语言。它用于从核心交易程序到风险管理系统的金融应用程序。

在这个以讲师为主导的现场培训中,参与者将学习如何应用机器学习技术和工具来解决金融行业中的现实问题。 R将用作编程语言。

参与者首先学习关键原则,然后通过构建自己的机器学习模型并将其用于完成一些团队项目,将他们的知识付诸实践。

在培训结束时,参与者将能够:

  • 理解机器学习的基本概念
  • 了解金融机器学习的应用和用途
  • 使用R的机器学习开发自己的算法交易策略

听众

  • 开发商
  • 数据科学家

课程形式

  • 部分讲座,部分讨论,练习和繁重的实践练习

Machine Translated

课程大纲

Introduction

  • Difference between statistical learning (statistical analysis) and machine learning
  • Adoption of machine learning technology and talent by finance companies

Understanding Different Types of Machine Learning

  • Supervised learning vs unsupervised learning
  • Iteration and evaluation
  • Bias-variance trade-off
  • Combining supervised and unsupervised learning (semi-supervised learning)

Understanding Machine Learning Languages and Toolsets

  • Open source vs proprietary systems and software
  • Python vs R vs Matlab
  • Libraries and frameworks

Understanding Neural Networks

Understanding Basic Concepts in Finance

  • Understanding Stocks Trading
  • Understanding Time Series Data
  • Understanding Financial Analyses

Machine Learning Case Studies in Finance

  • Signal Generation and Testing
  • Feature Engineering
  • Artificial Intelligence Algorithmic Trading
  • Quantitative Trade Predictions
  • Robo-Advisors for Portfolio Management
  • Risk Management and Fraud Detection
  • Insurance Underwriting

Introduction to R

  • Installing the RStudio IDE
  • Loading R Packages
  • Data Structures
  • Vectors
  • Factors
  • Lists
  • Data Frames
  • Matrices and Arrays

Importing Financial Data into R

  • Databases, Data Warehouses, and Streaming Data
  • Distributed Storage and Processing with Hadoop and Spark
  • Importing Data from a Database
  • Importing Data from Excel and CSV

Implementing Regression Analysis with R

  • Linear Regression
  • Generalizations and Nonlinearity

Evaluating the Performance of Machine Learning Algorithms

  • Cross-Validation and Resampling
  • Bootstrap Aggregation (Bagging)
  • Exercise

Developing an Algorithmic Trading Strategy with R

  • Setting Up Your Working Environment
  • Collecting and Examining Stock Data
  • Implementing a Trend Following Strategy

Backtesting Your Machine Learning Trading Strategy

  • Learning Backtesting Pitfalls
  • Components of Your Backtester
  • Implementing Your Simple Backtester

Improving Your Machine Learning Trading Strategy

  • KMeans
  • k-Nearest Neighbors (KNN)
  • Classification or Regression Trees
  • Genetic Algorithm
  • Working with Multi-Symbol Portfolios
  • Using a Risk Management Framework
  • Using Event-Driven Backtesting

Evaluating Your Machine Learning Trading Strategy's Performance

  • Using the Sharpe Ratio
  • Calculating a Maximum Drawdown
  • Using Compound Annual Growth Rate (CAGR)
  • Measuring Distribution of Returns
  • Using Trade-Level Metrics

Extending your Company's Capabilities

  • Developing Models in the Cloud
  • Using GPUs to Accelerate Deep Learning
  • Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion

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