INTRODUCTION IN PYTHON FOR FINANCE; volume 1.

English

Contents of vol. 1

Part I. Python and Finance;

1. Why Python for Finance?;

.What Is Python?;

.Brief History of Python;

.The Python Ecosystem;

.Python User Spectrum;

.The Scientific Stack;

.Technology in Finance;

.Technology Spending;

.Technology as Enabler;

.Technology and Talent as Barriers to Entry;

.Ever-Increasing Speeds, Frequencies, Data Volumes;

.The Rise of Real-Time Analytics;

.Python for Finance;

.Finance and Python Syntax;

.Efficiency and Productivity Through Python;

.From Prototyping to Production;

.Conclusions;

2. Infrastructure and Tools;

.Python Deployment;

.Anaconda;

.Python Quant Platform;

.Tools;

.Python;

.IPython;

.Spyder;

.Conclusions;

3. Introductory Examples;

.Implied Volatilities;

.Monte Carlo Simulation;

.Pure Python;

.Vectorization with NumPy;

.Full Vectorization with Log Euler Scheme;

.Graphical Analysis;

.Technical Analysis;

.Conclusions;

Part II. Financial Analytics and Development;

4. Data Types and Structures;

.Basic Data Types;

.Integers;

.Floats;

.Strings;

.Basic Data Structures;

.Tuples;

.Lists;

.Excursion: Control Structures;

.Excursion: Functional Programming;

.Dicts;

.Sets;

.NumPy Data Structures;

.Arrays with Python Lists;

.Regular NumPy Arrays;

.Structured Arrays;

.Vectorization of Code;

.Basic Vectorization;

.Memory Layout;

.Conclusions;

5. Data Visualization;

.Two-Dimensional Plotting;

.One-Dimensional Data Set;

.Two-Dimensional Data Set;

.Other Plot Styles;

.Financial Plots;

.3D Plotting;

.Conclusions;

6. Financial Time Series;

.pandas Basics;

.First Steps with DataFrame Class;

.Second Steps with DataFrame Class;

.Basic Analytics;

.Series Class;

.GroupBy Operations;

.Financial Data;

.Regression Analysis;

.High-Frequency Data;

.Conclusions;

7. Input/Output Operations;

.Basic I/O with Python;

.Writing Objects to Disk;

.Reading and Writing Text Files;

.SQL Databases;

.Writing and Reading NumPy Arrays;

.I/O with pandas;

.SQL Database;

.From SQL to pandas;

.Data as CSV File;

Data as Excel File;

Fast I/O with PyTables;

and more...

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