Financial Modelling and Simulation with Python: using Numpy, Scipy, Matplotlib and Pandas

Saurabh Jaiswal (~saurabh5)


21

Votes

Description:

Objective:

This Talk is about evolution of Python as a major technology used in Finance. These days various Financial Models are been simulated using python scientific libraries with amazing capabilities of Analysis, Calculation with higher order mathematical equations and statistical modelling complimented by Ploting libraries with 2D Graphs, Charts and Histograms.

Contents:

  1. Introduction of Python for Finance
  2. Black & Scholes for European Call Options
  3. Monte Carlo simulation for European Call Options
  4. Value at Risk calculation using Monte Carlo Simulation
  5. Geometric Brownian Motion
  6. Basics of Volatility and Normal function
  7. Basics of Correlation, Covariance and VaR

Intended Audience:

  • Newbies, Beginners, Intermediate and Advanced , Exploring Python as complete language
  • Scientific Community working on Modelling and Simulations using Python as alternative to Matlab, Mathematica and R.
  • Financial , Banking Domain sectors like Risk Analysis ,using Python for Development, Automation and Testing,

Prerequisites:

Knowledge of Basics of Python and Libraries like Numpy, Scipy, Matplotlib and Pandas .

Content URLs:

http://saurabhjaiswal.com/Financial%20Modelling%20and%20Simulation.pdf

Speaker Info:

  • More than 6 Years in Python Development in IT Industry
  • 3 Years into Research & Development in DRDO as Scientist and Researcher.
  • Open source Contributor in Python Software Foundation (PSF)
  • M.Tech. in Modelling and Simulations

Speaker Links:

You can find me at:

www.saurabhjaiswal.com

https://www.linkedin.com/in/saurabh37

https://twitter.com/saurabh3737

Section: Scientific Computing
Type: Talks
Target Audience: Intermediate
Last Updated:

I would request that you re-visit the intended audience. I agree with your premise that Python's adoption and popularity is increasing. However, the order in which you desire the audience may just be too niche/difficult for a PyCon (and I'd be happy to be proven wrong). Instead, if you focus on the newbies and demonstrate Python's ability to be extended into calculation of risk and such, you'd probably have an audience who are hooked (there have been previous instances where risk analysis etc have been great talks).

sankarshan mukhopadhyay (~sankarshan)

Thanks, sure that helps

Saurabh Jaiswal (~saurabh5)
The comment is marked as spam.

Cindy8

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