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Course Outline

Introduction

Establishing the Development Environment

  • Local versus online programming: Anaconda and Jupyter

Foundations of Python Programming

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

Expanding Python's Functionalities

  • Modules and Packages

Developing Your Initial Python Application

  • Calculating starting and ending dates and times

Retrieving External Data Using Python

  • Importing, exporting, reading, and writing CSV data
  • Accessing data stored in SQL databases

Structuring Data with Arrays and Vectors in Python

  • NumPy and vectorized functions

Data Visualization with Python

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

Data Analysis with Python

  • Conducting data analysis using scipy.stats and pandas
  • Importing and exporting financial data, including Excel and website data

Simulating Asset Price Movements

  • Monte Carlo simulation

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 Pricing

  • Carrying out fixed-income analysis and pricing options

Financial Time Series Analysis

  • Examining time series data within financial markets

Deploying Your Python Application to Production

  • Integrating applications with Excel and other web-based solutions

Application Performance

  • Optimizing application performance
  • Parallel Computing and Multiprocessing

Debugging and Troubleshooting

Conclusion

Requirements

  • Familiarity with financial concepts, such as securities and derivatives
  • A general grasp of probability and statistics
  • Basic knowledge of differential and integral calculus
 35 Hours

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