Importing Financial Data with Python from Free Web Sources

Importing Financial Data with Python from Free Web Sources

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What you will learn

  • Importing free / low-priced Financial Data from the Web with Python
  • Installing the required Libraries and Packages
  • Working with powerful APIs and Python wrapper packages
  • Downloading Historical Prices and Fundamentals for thousands of Stocks, Indexes, Mutual Funds and ETF´s
  • Downloading Historical Prices for Currencies (FOREX), Cryptocurrencies, Bonds & more
  • Saving / Storing the Data locally
  • Pandas Coding Crash Course

Curriculum

Section 1: Getting Started

Section 2: Importing Financial Data from Web Source 1

Section 3: Importing Financial Data from Web Source 2

Section 4: Importing Financial Data from Web Source 3

Section 5: Web Source 3b (for US and Canadian Residents)

Section 6: Importing Financial Data from Web Source 4

Section 7: Installing Python and Download/Working with Templates

Section 8: Appendix 1: Pandas Crash Course

Course Description

Get Historical Prices, Fundamentals, Metrics/Ratios etc. for thousands of Stocks, Bonds, Indexes, (Crypto-) Currencies

Requirements

  • Some Python Basics
  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming videos and downloading data
  • Ideally first experience with Pandas Library (not necessary, a Pandas crash course is included in the course)

Description

What can be the most critical and most expensive part when working with financial data?

Pandas coding? Creating some advanced Algorithms to analyse and optimize portfolios? Building solutions for Algorithmic Trading and Robo Advising? Maybe! But very often it is … getting the Data!

Financial Data is scarce and Premium Data Providers typically charge $20,000 p.a. and more! 


However, in 95% of all cases where Finance Professionals or Researchers require Financial Data, it can actually be obtained from Free or low-priced web sources. Some of them provide powerful APIs and Python wrapper packages, which makes it easy and comfortable to import the data with and into Python. 


+++ This course shows you how to get massive amounts of Financial Data from the web and provides downloadable Python coding templates (Jupyter Notebooks) for your convenience! +++  


This course covers four different data sources and explains in detail how to install required Libraries and how to download and import the data with few lines of Python Code. You will have access to

  • 60+ Exchanges all around the world
  • 120,000+ Symbols/Instruments
  • Historical Price and Volume Data for thousands of Stocks, Indexes, Mutual Funds and ETFs
  • Foreign Exchange (FOREX): 150+ Physical Currencies / Currency Pairs
  • 500+ Digital- / Cryptocurrencies
  • Fundamentals, Ratings, Historical Prices and Yields for Corporate Bonds
  • Commodities (Crude Oil, Gold, Silver, etc.)
  • Stock Options for 4,500 US Stocks
  • Fundamentals, Metrics and Ratios for thousands of Stocks, Indexes, Mutual Funds and ETFs
  • Balance Sheets
  • Profit and Loss Statements (P&L)
  • Cashflow Statements
  • 50+ Technical Indicators (e.g. SMA, Bollinger Bands)
  • Real-time and Historical Data (back to 1960s)
  • Streaming high-frequency real-time Data
  • Stock Splits and Dividends and how these are reflected in Stock Prices
  • Learn how Stock Prices are adjusted for Stock Splits and Dividends...
  • … and use appropriately adjusted data for your tasks! (avoid the Pitfalls!)  
  • Build your own Financial Databases...

… And save thousands of USDs!


What are you waiting for? As always, I provide a 30-Days-Money-Back Guarantee. So, there is no risk for you!

Looking forward to seeing you in the course!

Who this course is for:

  • Investment & Finance Professionals (and their Companies) spending thousands of USD p.a. on Financial Data.
  • (Finance) Students and Researchers who need to work with large financial datasets with only small budgets.
  • Everybody working occasionally with Financial Data.