Python for Finance

  Intermediate CPD: 14 hours   2 days   Live

Description

Python for Finance

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:
    • 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

  • 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

Dates and Locations

Date
Date(s): 29 Nov 2021 - 30 Nov 2021

Location
London

Category
Data Science

Other Dates and Locations
Check our course schedule for alternative dates and locations where this course is offered.


   Note that the course fee of £2,100.00 already includes 20% VAT.

£2,100.00


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