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 Duration 35 hours

Course Outline

Introduction

Configuring the Development Environment

  • Local vs. online programming: Anaconda and Jupyter

Core Python Programming Concepts

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

Extending Python Functionality

  • Utilizing modules and packages

Building Your First Python Application

  • Calculating start and end dates and times

Retrieving External Data with Python

  • Importing and exporting data, including reading and writing CSV files
  • Interacting with SQL databases

Structuring Data with Arrays and Vectors

  • NumPy and vectorized operations

Data Visualization in Python

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

Data Analysis with Python

  • Statistical analysis using scipy.stats and pandas
  • Handling financial data from sources like Excel and websites

Simulating Asset Price Movements

  • Monte Carlo simulation techniques

Asset Allocation and Portfolio Optimization

  • Capital allocation, asset distribution, and risk evaluation

Risk Assessment and Performance Measurement

  • Formulating and solving portfolio optimization challenges

Fixed-Income Analysis and Derivatives Pricing

  • Executing fixed-income calculations and option pricing

Financial Time Series Analysis

  • Analyzing temporal data in financial markets

Productionizing Your Python Application

  • Integration with Excel and other web-based platforms

Performance Optimization

  • Enhancing application efficiency
  • Parallel computing and multiprocessing strategies

Debugging and Troubleshooting

Conclusion

Requirements

  • Working knowledge of financial markets, including securities and derivatives
  • A solid grasp of probability and statistical concepts
  • Basic proficiency in differential and integral calculus

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