Analyzing Big Financial Data with Python Training Course

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