Python for Trading & Investing

Learn to use Python for analyzing data and trade in Stock Markets
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  • Lectures 36
  • Length 5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 6/2015 English

Course Description

There are so many different trading or investing approaches as people in the market.

Many existing tools support the most common ones, but if you really want to success with an innovative practice, you have to build it on your own.

Due to its characteristics, Python is being adopted by the financial industry as its reference programming language.

But Python is not expensive as other financials tools are, in fact it is completely free. And it is not difficult to learn. So, why don't give it a try?

Learn how to apply Python to Trading and Investing with this Hands-on Course.

  • Ipython Working Environment
  • Main Data Analysis Python Libraries
  • How to import/export Financial Data
  • Data Munging
  • Customized Charts
  • Different Projects applying this knowledge

Improve your Programming and Investing Skills at a time.

Either if you can already program and are interested in Finance. Or if you are already a Finance practitioner and are interested in applying programming to your career. This is a course for you.

In addition to using this new knowledge for your own investments, new opportunities will widely open up for you if you are able to combine these two disciplines.

The volume of data is increasing at not seen before rates. And new algorithms and tools are needed to get the most of it. It is difficult to imagine a more promising skill in your career path than learning to manage and analyze data through programming.

Content and Overview

This course will start with a review of main Python libraries to use for Data Analysis.

Although due to the readability of Python it is not necessary to have previous knowledge of it. It is recommended at least to have a previous contact with it.

The main goal is to focus in the application of it to Finance concepts. So not much time will be addressed to common functions or data structures. You will be able, anyway, to send any doubt to the Instructor and if necessary new lectures will be upload replying most frequently asked questions.

The best way to learn is doing. So in the second part of the course, actual applications with the complete code will be developed. You will be able to test and modify them with your desired parameters or strategies and even propose new ones. Building, in this way, a community around the course that will help us to grow up individually.

New projects will be added periodically in the future, but the course price will go up accordingly. So enroll now, It will be your best investment.

What are the requirements?

  • Python installed in your computer

What am I going to get from this course?

  • Get free Financial Data from the web with Python
  • Improve programming skills and get to know main data analysis Python libraries
  • Use Python for analyzing financial data

What is the target audience?

  • Programmers with interest in Finance
  • Finance practitioners with interest in programming
  • Anyone interested in learning either programming or basic finances

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: INTRODUCTION
Introduction
Preview
05:25
Section 2: WORKING ENVIRONMENT
03:08

In this lecture, the tool Ipython will be introduced.

Ipython is an interactive solution for running Python scripts very recommended for data analysis.

14:42

In this lecture, a demo run over Ipython will be shown, just to see how it works.

Ipython Demo - Notebook
2 pages
Section 3: DATA STRUCTURES
04:19

An overview of the different data structures for Data Analysis that will be explained in next lectures.

08:52

In this lecture, we will see the main Numpy Data Structures and their main functionality.

Numpy Data Structures - Notebook
7 pages
24:01

In this lecture, we will see the main Pandas Data Structures and their main functionality.

Pandas Data Structures - Notebook
11 pages
Section 4: GETTING & SAVING DATA
17:37

In this lecture, we will see how to save data to a plain file or csv and how to retrieve them from it.

Text and csv Files - Notebook
7 pages
06:14

In this lecture, we will see how to save data to an excel file and how to retrieve them from it.

Excel Files - Notebook
4 pages
09:56

In this lecture, we will see how to save data to a json file and how to retrieve them from it.

Json Files - Notebook
4 pages
Section 5: DATA MUNGING
15:17

In this lecture, we will see how to index and select data from different Data Structures.

Indexing & Selecting Data - Notebook
16 pages
08:13

In this lecture, we will see how to Join, Merge and Concatenate different Dataframes.

Joining, Merging & Concatenating - Notebook
8 pages
Section 6: CHARTS
09:53

In this lecture, we will see how to create basic charts from Dataframes.

Charts - Notebook
4 pages
Section 7: QUANTITATIVE BASICS
14:30

Goals of this session are two:

  1. Recall some basic Quantitative Formulas and Concepts about changes in value of money in time.
  2. Use Python for calculations getting familiar to how it works and even starting to see some plots.
Time Value of Money (1) - Notebook
6 pages
06:16

In this second lecture about Time Value of Money we will see how to calculate:


  • Future value of Series of Cash Flows
  • Present Value of Future Cash Flows
Time Value of Money (2) - Notebook
3 pages
Section 8: FINANCIAL OPTIONS
Financial Options - Introduction (1)
Preview
04:57
Financial Options - Introduction (2)
05:42
Financial Options - Introduction (3)
04:37
Financial Options - Introduction (4)
07:00
Financial Options - Introduction (5)
04:57
Financial Options - Introduction (6)
07:06
Financial Options - Introduction - Notebook
7 pages
Section 9: Quantopian - Algorithms Backtesting
08:13

Introduction to Platform Quantopian for backtesting algorithms written in Python

Section 10: DOCUMENTATION
Documentation to download
22 pages
Ipython Notebooks
Article
Financial Data Sources
Article

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Instructor Biography

Ricardo Naya Arboleya, Freelance Consultant SAP

Ricardo Naya, Industrial Engineer, CPIM (Certified in Production and Inventory Management), SAP PP certified and MBA among other titles, has worked during more than 12 years in SAP implementation or improvement projects in main leader companies worldwide in different industries (Nestle, Alcatel, Roche, Burberry, ...).

SAP consulting experience has allowed him to know the processes and best practices in different industries, as long as adquire a deep knowledge of this ERP.

SAP consulting does not only require technical knowledge of the system, like execute the different transactions. But also a functional knowledge of why SAP offers the different options and which standard ones are allowed.

By means of his training courses, Ricardo hopes to deliver both approaches and in this way to ease the learning and understanding of SAP.

--------------------------------------------------------------------------------------------------------------------

Ricardo Naya, Ingeniero Industrial Superior en Organización Industrial, CPIM (Certified in Production and Inventory Management), certificado en SAP PP y MBA entre otros estudios, ha trabajado durante más de 12 años en proyectos de implantación o de mejora de SAP en las principales empresas líderes mundiales de diferentes sectores (Nestlé, Alcatel, Roche, Burberry,...).

La experiencia en consultoría SAP le ha permitido conocer los procesos y las mejores prácticas en diferentes industrias, así como adquirir un amplio conocimiento de este ERP.

Trabajar con SAP no solo requiere unos conocimientos técnicos del sistema, cómo realizar las distintas transacciones por ejemplo. Sino también un conocimiento funcional de el porqué SAP las ofrece de una manera y qué opciones de configuración estándar permite.

Por medio de sus cursos de formación, Ricardo espera poder transmitir ambos enfoques y de este modo facilitar el aprendizaje y entendimiento de este ERP.

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