Python for Trading & Investing
4.1 (24 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
626 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Python for Trading & Investing to your Wishlist.

Add to Wishlist

Python for Trading & Investing

Learn to use Python for analyzing data and trade in Stock Markets
4.1 (24 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
626 students enrolled
Last updated 9/2016
English
Current price: $10 Original price: $95 Discount: 89% off
1 day left at this price!
30-Day Money-Back Guarantee
Includes:
  • 3 hours on-demand video
  • 2 Articles
  • 13 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
Requirements
  • Python installed in your computer
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.

Who is the target audience?
  • Programmers with interest in Finance
  • Finance practitioners with interest in programming
  • Anyone interested in learning either programming or basic finances
Students Who Viewed This Course Also Viewed
Curriculum For This Course
36 Lectures
04:52:10
+
INTRODUCTION
1 Lecture 05:25
+
WORKING ENVIRONMENT
3 Lectures 17:50

In this lecture, the tool Ipython will be introduced.

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

Ipython Introduction
03:08

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

Ipython Demo
14:42

Ipython Demo - Notebook
2 pages
+
DATA STRUCTURES
5 Lectures 37:12

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

Data Structures Introduction
04:19

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

Numpy Data Structures
08:52

Numpy Data Structures - Notebook
7 pages

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

Pandas Data Structure
24:01

Pandas Data Structures - Notebook
11 pages
+
GETTING & SAVING DATA
6 Lectures 33:47

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
17:37

Text and csv Files - Notebook
7 pages

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

Excel Files
06:14

Excel Files - Notebook
4 pages

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

Json Files
09:56

Json Files - Notebook
4 pages
+
DATA MUNGING
4 Lectures 23:30

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

Indexing & Selecting Data
15:17

Indexing & Selecting Data - Notebook
16 pages

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

Joining, Merging & Concatenating
08:13

Joining, Merging & Concatenating - Notebook
8 pages
+
CHARTS
2 Lectures 09:53

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

Charts
09:53

Charts - Notebook
4 pages
+
QUANTITATIVE BASICS
4 Lectures 20:46

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.
Preview 14:30

Time Value of Money (1) - Notebook
6 pages

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)
06:16

Time Value of Money (2) - Notebook
3 pages
+
FINANCIAL OPTIONS
7 Lectures 34:19

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
+
Quantopian - Algorithms Backtesting
1 Lecture 08:13

Introduction to Platform Quantopian for backtesting algorithms written in Python

Quantopian - Introduction - Hello World!
08:13
+
DOCUMENTATION
3 Lectures 00:16
Documentation to download
22 pages

Ipython Notebooks
00:05

Financial Data Sources
00:11
About the Instructor
Ricardo Naya Arboleya
4.0 Average rating
355 Reviews
4,497 Students
7 Courses
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.