Python for Finance

Description

Python is increasingly being seen as an essential skill for everyone working in finance. This highly practical course is intensively focused on how Python can be used in a range of real-world finance applications.

Learning Outcomes

By attending this course, you will:

• Explore a wide range of Python libraries essential for building financial applications
• Explore how to source, download, scrub, analyse, and visualize financial data
• Learn how to build practical Python applications in investment management, derivatives pricing, and algorithmic trading

Who Should Attend

Anyone who wants to use Python in practical financial applications.

Prerequisites

You should be familiar with the content of our Introduction to Python course.

Seminar Content

Review of Essential Skills

• Defining and using functions
• computer Creating and using a PV function
• Working with classes
• computer Creating and using a bond class
• Working with arrays
• Generating random numbers
• computer Implementing random-walk price sequence

Python Libraries for Finance

• The essentials:
  • Pandas
  • SciPy
  • NumPy
  • Statistics
  • Matplotlib
• Finance libraries:
  • yfinance
  • Openpyxl
  • Pyfin
  • QuantPy
  • QuantLib
  • Quant DSL
  • Ffn
  • Quandl
  • Pynance
  • PyAlgoTrade
  • Zipline

Working with Data

• Sources of financial data
• Time-series data
  • Downloading
  • Cleaning / scrubbing
  • Transformations
  • Analyzing
  • Displaying
• Data visualization
• Interfacing Python with Excel
• computer Analyzing historical volatility

Real-World Finance Applications

Investment Management

• Retrieving and analyzing company data
• computer Company valuation
• Working with securities portfolios
• Generating the efficient frontier
• Calculating alpha, beta, and the Sharpe ratio
• computer Building an optimal investment portfolio

Option Pricing

• computer Implementing the Black Scholes model for option pricing
• computer Implementing a Monte-Carlo pricing model for a vanilla option
• computer Implementing a Monte-Carlo pricing model for exotic options

Trading Strategies

• Machine learning techniques
• Analyzing financial markets data
• computer Implementing an algorithmic trading strategy

When and Where
   16 Apr 2025 - 17 Apr 2025
   09:00-17:00
   London
   Data Science

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Other Dates and Locations
Search for Python for Finance in our course schedule for alternative dates and locations where this course is offered.