01_intpy_day_2_overview.md

Intermediate Python for Finance Training

This is the Gist for Day 2 of the Intermediate Python for Finance Training in London, 29. November 2017

Agenda

Module 3 — pandas

This module is about data analysis and visualization with pandas for finance.

pandas has become the central data analysis tool in the Python ecosystem. It is powerful, among others, in handling financial time series data, in visualizing such data and implementing algorithms on such data sets.

The goal of this module is to be aware of the capabilities of pandas and to be able to apply pandas to typical financial analytics tasks.

Module 4 — OOP

The final module addresses object-oriented programming (OOP) in Python based on finance examples.

OOP is a powerful programming paradigm with many benefits over simple procedural implementations. Among others, it allows a simplified modeling of real-world and financial object, to avoid redundancies, to simplify code maintenance and re-usability.

The goal of this module is to be aware of basic OOP features in Python and to implement financial algorithms in a re-usable fashion.

Notebooks & Code Files

Below you find the all the notebooks and code files as worked on during this day.

The Python Quants

http://twitter.com/dyjh

team@tpq.io | http://tpq.io

http://pyalgo.tpq.io | http://certificate.tpq.io

02_numpy_finance.ipynb

03_pandas_basics.ipynb

04_pandas_finance.ipynb

99_day_2_exercises.ipynb

btc_usd.csv

mvp_class.py

Titanic.csv