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 Duration 35 hours

Course Outline

Introduction

Establishing the Development Environment

  • Local vs. online programming: Anaconda and Jupyter

Core Python Programming Concepts

  • Control structures, data types, functions, data structures, and operators

Expanding Python's Functionality

  • Modules and Packages

Building Your First Python Application

  • Calculating initial and final dates and times

Retrieving External Data with Python

  • Importing/exporting data, and reading/writing CSV files
  • Querying SQL databases

Structuring Data with Arrays and Vectors in Python

  • NumPy and vectorized operations

Data Visualization with Python

  • 2D and 3D plotting with Matplotlib, pyplot, and SciPy

Data Analysis with Python

  • Analyzing data using scipy.stats and pandas
  • Importing and exporting financial data (Excel, web sources, etc.)

Simulating Asset Price Paths

  • Monte Carlo simulation

Asset Allocation and Portfolio Optimization

  • Executing capital allocation, asset distribution, and risk assessment

Risk Analysis and Investment Performance

  • Formulating and solving portfolio optimization challenges

Fixed-Income Analysis and Option Pricing

  • Conducting fixed-income analysis and pricing options

Financial Time Series Analysis

  • Examining time series data within financial markets

Deploying Your Python Application

  • Integrating applications with Excel and other web-based tools

Application Performance

  • Optimizing application speed and efficiency
  • Parallel Computing and Multiprocessing

Troubleshooting

Conclusion

Requirements

  • Foundational knowledge of finance (including securities, derivatives, etc.)
  • A general grasp of probability and statistics
  • Basic understanding of differential and integral calculus

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