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Investment Banking: Introduction to Structured Products培训
 
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上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】:北京中山学院/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【广州分部】:广粮大厦 【西安分部】:协同大厦 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦
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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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