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Course Outline

Introduction

Establishing the Development Environment

  • Local programming versus online environments: Anaconda and Jupyter

Foundations of Python Programming

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

Expanding Python's Capabilities

  • Working with modules and packages

Creating Your First Python Application

  • Calculating start and end dates and times

Connecting Python to External Data

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

Structuring Data with Arrays and Vectors

  • Utilizing NumPy and vectorized functions

Data Visualization with Python

  • Using Matplotlib for 2D and 3D plotting, along with pyplot and SciPy

Data Analysis with Python

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

Simulating Asset Price Trajectories

  • Implementing Monte Carlo simulation

Asset Allocation and Portfolio Optimization

  • Executing capital allocation, asset allocation, and risk assessment

Risk Analysis and Investment Performance

  • Formulating and solving portfolio optimization problems

Fixed-Income Analysis and Option Pricing

  • Conducting fixed-income analysis and pricing options

Financial Time Series Analysis

  • Analyzing time series data within financial markets

Deploying Your Python Application to Production

  • Integrating applications with Excel and other web-based tools

Application Performance

  • Optimizing application speed and efficiency
  • Implementing parallel computing and multiprocessing

Troubleshooting

Conclusion

Requirements

  • A solid grasp of finance concepts (including securities and derivatives)
  • A general knowledge of probability and statistics
  • Basic proficiency in differential and integral calculus
 35 Hours

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