codefinance.training

Coding for Finance

Fixed Income Analytics: Pricing and Risk Management

The fixed income markets are central to the modern economy, and are arguably the most central and influential markets in the entire financial system. Indeed, interest rates, the most important prices in the entire economy, are set in the bond and money markets. A famous and colorful lament from then President-Elect Bill Clinton in 1993 lead his aide, James Carville, to declare that in his next life he wanted to come back as something really influential: the bond market.

This course, which assumes no knowledge of finance, and with minimal math requirements (business school calculus is more than enough) will be useful for financial professionals who wish to go to the next level with their understanding of the fixed income markets, and for quantitative professionals from other fields who are interested in learning something about finance. If you’re looking for one segment of the capital markets to start an exploration of finance, you can’t go wrong with the fixed income markets.

What you’ll learn

  • The general structure of global bond and money markets
  • Pricing, yield, accrued interest and day count conventions
  • Arbitrage and the time value of money as the core principles underlying security valuation, and how to use them to price fixed income securities
  • The term structure of interest rates, its applications, and the accepted theories of the forces that shape it
  • The classic risk measures of fixed income securities: duration, DV01, and convexity, and their applications to risk management
  • Trading applications: riding the yield curve and rate level trading
  • Immunization and applications in asset/liability management

Financial Derivatives: A Quantitative Finance View

Interested in a lucrative and rewarding position in quantitative finance?  Are you a quantitative professional working in finance or a technical field and want to bridge the gap and become a full on quant?  Then read on.

The role of a quantitative analyst in an investment bank, hedge fund, or financial company is an attractive career option for many quantitatively skilled professionals working in finance or other fields like data science, technology or engineering.  If this describes you, what you need to move to the next level is a gateway to the quantitative finance knowledge required for this role that builds on the technical foundations you have already mastered.

This course is designed to be exactly such a gateway into the quant world.  If you succeed in this course you will become a master of quantitative finance and the financial engineering of the most influential class of financial products that exist on markets today: derivatives.

What you’ll learn
  • Learn the fundamentals of derivatives at a quantitative level
  • Master arbitrage, the core principle underlying derivatives, quantitative risk management and quantitative trading
  • Use derivatives to control and manage financial risk
  • Price forwards, futures, swaps and options
  • Understand the Black-Scholes theory and formula intuitively, avoiding stochastic calculus
  • Learn the limitations of the Black-Scholes theory, and how it is used in practice
  • Python based tools are provided for computations with bonds, yield curves, and options

Tools for Data Science > IBM

What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you’ll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

What is Data Science? > IBM

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

Machine Learning with Python > IBM

This course explores techniques and applications of machine learning using Python and has two main components:

  1. The purpose of Machine Learning and it’s applications in the real world.
  2. A general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

The course covers real-life examples of Machine learning and shows you how it affects society in ways you may not have guessed!

With only a few hours study a week you get:

  • New skills for CV including regression, classification, clustering, sci-kit learn and SciPy
  • New application to demonstrate, including image classification detection, trend analysis and prediction, customer analysis, recommendation engines, and more.
  • A certificate in machine learning to prove your competency plus an IBM digital badge upon successful completion.