Importing Finance Data with Python from Free Web Sources
What you'll 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
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)
![Booking](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 110 18"%3E%3C/svg%3E){kind=small}
![Lyft](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 42 30"%3E%3C/svg%3E){kind=small}
![Mercedes](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 120 24"%3E%3C/svg%3E){kind=small}
![Volkswagon](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32"%3E%3C/svg%3E){kind=small}
![adidas](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 42 28"%3E%3C/svg%3E){kind=small}
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.
Course content
- Preview05:27
- Preview03:32
- Preview08:55
Instructor
![Alexander Hagmann](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 2,752 Reviews
- 30,309 Students
- 7 Courses
Alexander is a Data Scientist and Finance Professional with more than 10 years of experience in the Finance and Investment Industry.
He is also a Bestselling Udemy Instructor for
- Data Analysis/Manipulation with Pandas
- (Financial) Data Science
- Python for Business and Finance
- Algorithmic Trading
Alexander started his career in the traditional Finance sector and moved step-by-step into Data-driven and Artificial Intelligence-driven Finance roles. He is currently working on cutting-edge Fintech projects and creates solutions for Algorithmic Trading and Robo Investing. And Alexander is excited to share his knowledge with others here on Udemy. Students who completed his courses work in the largest and most popular tech and finance companies all over the world.
Alexander´s courses have one thing in common: Content and concepts are practical and real-world proven. The clear focus is on acquiring skills and understanding concepts rather than memorizing things.
Alexander holds a Master´s degree in Finance and passed all three CFA Exams (he is currently no active member of the CFA Institute).