Steve Bell DPhil

Steve Bell D.Phil




I'm a blogger and writer with expertise in finance, statistical modelling, machine learning and Python. I started my career as a physicist working on instrumentation projects for major companies such as ABB. Probably I learnt most from building a model used to predict the properties of crude oil from the North Sea Forties pipeline. This involved handling large amounts of laboratory analysis data and making inferences from near infra-red spectral data. The model was used to value the crude oil by the participating oil companies. Later I moved into finance and used my statistical modelling experience to develop energy trading strategies for a quantitative hedge fund in London UK. I used novel data sources such as shipping movements as well as more conventional analysis of the forward curve for the commodities. Since then I've turned my attention to writing and teaching. I'm an enthusiast for the Python programming language and my plan is to post my code onto GitHub so as to provide examples and templates of how to use Python and modules such as Pandas for statistical analysis. I live in London UK with my wife and two children. When I'm not busy writing you might be able to spot me cycling through Epping Forest in a brightly coloured jacket...

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"Quantitative Finance for Dummies" John Wiley 2016
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Published by John Wiley August 2016

In August 2016 John Wiley published my book "Quantitative Finance for Dummies". It's one of the best sellers in its field and a great introduction to the subject that includes aspects of data science as well as key topics such as option pricing and portfolio theory. You can buy your copy at online bookshops such as Blackwell's or Amazon.

I've also taught finance at the London campus of Coventry University including an innovative course on algorithmic trading. Last year I taught a Python based module 'Managing and Visualising Data' on the MSc Data Science course at the London School of Economics.

Table of Contents


  1. Quantitative Finance Unveiled
  2. Understanding Probability and Statistics
  3. Taking a look ar Random Behaviours
  4. Sizing up Interest Rates, Shares and Bonds
  5. Exploring Options
  6. Trading Risk with Futures
  7. Reading the Market's Mood: Volatility
  8. Analysing all the Data
  9. Analysing Data Matrices: Principal Components
  10. Examining the Binomial and Black-Scholes Pricing Models
  11. Using the Greeks in the Black-Scholes Model
  12. Gauging Interest-Rate Derivatives
  13. Managing Market Risk
  14. Comprehending Portfolio Theory
  15. Measuring Potential Losses: Value at Risk (VaR)
  16. Forecasting Markets
  17. Fitting Models to Data
  18. Markets in Practice
  19. Ten Key Ideas of Quantitative Finance
  20. Ten ways to Act your Career in Quantitative Finance




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