Analyzing Big Financial Data with Python培训

课程编码

pythonbigdata

课程时长

35 小时 通常来说是5天,包括中间休息。

要求

  • Basic programming experience
  • A solid grasp of mathematics for finance

课程概览

Python是一种高级编程语言,以其清晰的语法和代码可读性而闻名。

在这个以讲师为主导的现场培训中,参与者将学习如何使用Python进行定量融资。

在培训结束时,参与者将能够:

  • 理解Python编程的基础知识
  • Python用于财务应用程序,包括实现数学技术,随机指标和统计数据
  • 使用性能Python实现财务算法

听众

  • 开发商
  • 定量分析师

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

Understanding the Fundamentals of Python

Overview of Using Technology and Python in Finance

Overview of Tools and Infrastructure

  • Python Deployment Using Anaconda
  • Using the Python Quant Platform
  • Using IPython
  • Using Spyder

Getting Started with Simple Financial Examples with Python

  • Calculating Implied Volatilities
  • Implementing the Monte Carlo Simulation
    • Using Pure Python
    • Using Vectorization with Numpy
    • Using Full Vectoriization with Log Euler Scheme
    • Using Graphical Analysis
  • Using Technical Analysis

Understanding Data Types and Structures in Python

  • Learning the Basic Data Types
  • Learning the Basic Data Structures
  • Using NumPy Data Structures
  • Implementing Code Vectorization

Implementing Data Visualization in Python

  • Implementing Two-Dimensional Plots
  • Using Other Plot Styles
  • Implementing Finance Plots
  • Generating a 3D Plot

Using Financial Time Series Data in Python

  • Exploring the Basics of pandas
  • Implementing First and Second Steps with DataFrame Class
  • Getting Financial Data from the Web
  • Using Financial Data from CSV Files
  • Implementing Regression Analysis
  • Coping with High-Frequency Data

Implementing Input/Output Operations

  • Understanding the Basics of I/O with Python
  • Using I/O with pandas
  • Implementing Fast I/O with PyTables

Implementing Performance-Critical Applications with Python

  • Overview of Performance Libraries in Python
  • Understanding Python Paradigms
  • Understanding Memory Layout
  • Implementing Parallel Computing
  • Using the multiprocessing Module
  • Using Numba for Dynamic Compiling
  • Using Cython for Static Compiling
  • Using GPUs for Random Number Generation

Using Mathematical Tools and Techniques for Finance with Python

  • Learning Approximation Techniques
    • Regression
    • Interpolation
  • Implementing Convex Optimization
  • Implementing Integration Techniques
  • Applying Symbolic Computation

Stochastics with Python

  • Generation of Random Numbers
  • Simulation of Random Variables and of Stochastic Processes
  • Implementing Valuation Calculations
  • Calculation of Risk Measures

Statistics with Python

  • Implementing Normality Tests
  • Implementing Portfolio Optimization
  • Carrying Out Principal Component Analysis (PCA)
  • Implementing Bayesian Regression using PyMC3

Integrating Python with Excel

  • Implementing Basic Spreadsheet Interaction
  • Using DataNitro for Full Integration of Python and Excel

Object-Oriented Programming with Python

Building Graphical User Interfaces with Python

Integrating Python with Web Technologies and Protocols for Finance

  • Web Protocols
  • Web Applications
  • Web Services

Understanding and Implementing the Valuation Framework with Python

Simulating Financial Models with Python

  • Random Number Generation
  • Generic Simulation Class
  • Geometric Brownian Motion
    • The Simulation Class
    • Implementing a Use Case for GBM
  • Jump Diffusion
  • Square-Root Diffusion

Implementing Derivatives Valuation with Python

Implementing Portfolio Valuation with Python

Using Volatility Options in Python

  • Implementing Data Collection
  • Implementing Model Calibration
  • Implementing Portfolio Valuation

Best Practices in Python Programming for Finance

Troubleshooting

Summary and Conclusion

Closing Remarks

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