Data Analysis and Programming for Finance

New York Institute of Finance

How long?

  • 5 days
  • in person, online

What are the topics?

New York Institute of Finance

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Who should attend

  • Developers
  • quants
  • analysts
  • financial engineers and anyone seeking to become a better financial modeler. While not essential a modest amount of prior programming experience will be beneficial. Some familiarity with financial instruments will be advantageous.

About the course

This course will teach you the essential elements of Python and R to build practically useful applications and conduct data analysis for finance.

This Professional Certificate comprises the following courses:

  • Python Programming for Finance (Days 1 - 3)
  • R Programming for Finance (Days 4 & 5)

Prerequisite knowledge:

  • Basic probability and statistics
  • Some familiarity with financial securities and derivatives
  • Elementary differential and integral calculus

CURRICULUM

Day 1

MODULE 1: INTRODUCTION TO PYTHON

  • The Anaconda Python distribution
  • Interactive programming: IPython and Jupyter notebooks
  • Programming: control structures, data types, functions, data structures
  • Modules and Packages

MODULE 2: ESSENTIAL PYTHON TOOLKIT

  • Date and time management : format, measuring time lapse, etc.
  • How to build and run a standalone application
  • Parsing command line arguments
  • Importing/Exporting files
  • Reading and writing in CSV format
  • Accessing SQL databases
  • Multiprocessing
  • Using a dictionary for explicit indexing

MODULE 3: ARRAYS, VECTORIZATION AND RANDOM NUMBERS

  • NumPy: array processing
  • Vectorized functions
  • Random number generation

Day 2

MODULE 1: SCIENTIFIC COMPUTING WITH PYTHON

  • Matplotlib: 2D and 3D plotting
  • Using pyplot
  • SciPy: scientific computing
  • Root finding, interpolation, integration and optimization

MODULE 2: DATA ANALYSIS WITH PYTHON

  • Data analysis with scipy.stats and pandas
  • Pandas data structures: series and data frames
  • Importing and exporting data from/to MS Excel
  • Importing data from websites

Day 3

MODULE 1: PYTHON APPLICATIONS

  • Monte Carlo simulation basics
  • Simulating asset price trajectories
  • Variance reduction techniques
  • Pricing options by Monte Carlo simulation
  • Pricing options by finite difference methods

Day 4

MODULE 1: R BASICS

  • The IDE: RStudio
  • R syntax
  • R objects: vectors, matrices, arrays, data frames and lists
  • Flow control: branching, looping and truth testing
  • Importing and manipulating data
  • Plotting with R

Day 5

MODULE 1: DATA ANALYSIS WITH R

  • Manipulating data frames
  • Descriptive statistics
  • Inference and time series analysis

MODULE 2: R APPLICATIONS

  • Regression analysis
  • Volatility modeling
  • Risk management: VaR and ES

WHAT YOU'LL LEARN

  • Learn the basic elements of programming in Python and R
  • Be familiar with the strengths and weaknesses of each development environment
  • Learn essential data analysis concepts and techniques for finance
  • Build realistic applications for finance using Monte Carlo and finite difference techniques, including an American option pricer

Data Analysis and Programming for Finance at New York Institute of Finance

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