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Course Outline

Introduction

Establishing the Development Environment

  • Local programming versus online environments: Anaconda and Jupyter

Core Python Programming Concepts

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

Expanding Python's Functionality

  • Modules and Packages

Developing Your First Python Application

  • Calculating start and end dates and times

Accessing External Data via Python

  • Importing and exporting, as well as reading and writing CSV data
  • Retrieving data from SQL databases

Structuring Data with Arrays and Vectors in Python

  • Utilizing NumPy and vectorized functions

Data Visualization with Python

  • Creating 2D and 3D plots with Matplotlib, pyplot, and SciPy

Data Analysis with Python

  • Conducting data analysis using scipy.stats and pandas
  • Importing and exporting financial data (including Excel and web-based data)

Simulating Asset Price Movements

  • Monte Carlo simulation

Asset Allocation and Portfolio Optimization

  • Executing capital allocation, asset allocation, and risk assessment

Risk Analysis and Investment Performance

  • Formulating and resolving portfolio optimization challenges

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 for Production

  • Integrating your application with Excel and other web-based tools

Application Performance

  • Enhancing application efficiency
  • Parallel Computing and Multiprocessing

Debugging and Troubleshooting

Concluding Remarks

Requirements

  • Familiarity with finance 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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