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Course Outline

Introduction

Configuring the Development Environment

  • Local vs. online programming environments: Using Anaconda and Jupyter

Essential Python Programming Concepts

  • Exploring control structures, data types, functions, data structures, and operators

Expanding Python's Functionality

  • Working with Modules and Packages

Building Your Initial Python Application

  • Calculating starting and ending dates and times

Retrieving External Data via Python

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

Structuring Data with Arrays and Vectors in Python

  • Leveraging NumPy and vectorized operations

Data Visualization with Python

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

Data Analysis with Python

  • Conducting statistical analysis using scipy.stats and pandas
  • Importing and exporting financial data from sources like Excel and web platforms

Simulating Asset Price Movements

  • Implementing Monte Carlo simulations

Asset Allocation and Portfolio Optimization Strategies

  • Executing capital allocation, asset distribution, and risk evaluation

Risk Assessment and Investment Performance Metrics

  • Formulating and resolving portfolio optimization challenges

Fixed-Income Analysis and Option Valuation

  • Conducting fixed-income analysis and pricing options

Financial Time Series Analysis

  • Evaluating time-series data within financial markets

Transitioning Your Python Application to Production

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

Application Performance Enhancement

  • Optimizing application speed and efficiency
  • Utilizing Parallel Computing and Multiprocessing

Debugging and Troubleshooting

Conclusion and Final Thoughts

Requirements

  • Familiarity with financial instruments, including securities and derivatives
  • A solid grasp of fundamental probability and statistics concepts
  • Basic knowledge of differential and integral calculus
 35 Hours

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