Python for Finance is geared toward financial analysts and quants who would like to learn how to use Python in their day-to-day work. Exercises include filtering and plotting an array of Dow Jones closing data, calculating options pricing using Black-Scholes models, estimating volatility using GARCH, and using a Monte Carlo simulation to calculate an option price.
The first day is devoted to understanding how to think in Python. We start by demonstrating the IPython interactive environment and how it can be used for rapid application development. The pace of this day is determined by previous exposure to Python. Even experienced Python programmers report learning new ideas from the experts that teach this course.
The NumPy extension module to Python is exposed as a tool for rapidly manipulating and processing large data-sets.
Pandas is rapidly emerging as the tool of choice for manipulating time-series data in Python. In this module you will cover:
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Programming experience in some language (C, VB, Fortran, Matlab) is expected. Experience with C, C++, and/or Fortran is useful for some topics. Object oriented programming skills are not necessary but will be helpful. Knowledge of calculus, statistics, signal and image processing, optimization, are all valuable but not absolutely required.
Participants will receive 30 days of Enthought Training on Demand Python Foundations Series access following the course