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Course Outline

Introduction

Configuring the Development Environment

  • Local programming vs. online environments: Anaconda and Jupyter

Core Python Programming Concepts

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

Expanding Python's Functionality

  • Working with modules and packages

Developing Your First Python Application

  • Calculating initial and final dates and times

Retrieving External Data with Python

  • Data import/export and reading/writing CSV files
  • Interacting with SQL databases

Data Management via Arrays and Vectors in Python

  • Utilizing NumPy and vectorized operations

Data Visualization Techniques in Python

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

Data Analysis in Python

  • Analytical tasks using scipy.stats and pandas
  • Importing and exporting financial data from sources like Excel and websites

Simulating Asset Price Movements

  • Monte Carlo simulation methods

Asset Allocation and Portfolio Optimization

  • Executing capital allocation, asset distribution, and risk evaluation

Risk Assessment and Investment Performance

  • Formulating and resolving portfolio optimization challenges

Fixed-Income Analysis and Option Valuation

  • Conducting fixed-income analysis and pricing options

Financial Time Series Examination

  • Evaluating time series data within financial markets

Deploying Python Applications in Production

  • Integrating your application with Excel and various web platforms

Application Optimization

  • Performance tuning of your application
  • Parallel computing and multiprocessing strategies

Debugging and Troubleshooting

Conclusion

Requirements

  • Familiarity with financial concepts, including securities and derivatives
  • A foundational understanding of probability and statistics
  • Basic knowledge of differential and integral calculus
 35 Hours

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