Importing Finance Data with Python from Free Web Sources

  • Course provided by Udemy
  • Study type: Online
  • Starts: Anytime
  • Price: See latest price on Udemy
Udemy

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.
  • 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 Curated for the Udemy Business collection 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 Statement s (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. Show more Show less Course content 9 sections • 93 lectures • 7h 44m total length Expand all sections Getting Started 3 lectures • 18min Tips: How to get the most out of this Course Preview 05:27 Course Overview Preview 03:32 Hands-on: Downloading CSV-files and import to Python Preview 08:55 Importing Financial Data from Web Source 1 19 lectures • 1hr 27min Intro 03:05 Installing the required Package 03:03 Historical Price and Volume Data for one Stock Preview 04:01 Setting specific Time Periods 06:06 Frequency Settings (Intraday) 07:38 Stock Splits and Dividends 11:51 Exporting to CSV / Excel 05:36 Importing many Stocks 04:17 Financial Indexes 05:53 Currencies / FX 03:14 Cryptocurrencies 03:10 Mutual Funds & ETFs 02:44 Treasury Yields 03:38 The Ticker Object 04:21 Stock Fundamentals, Meta Info and Performance Metrics 04:22 +++IMPORTANT NOTICE & ACTION REQUIRED (before you start with next Lecture!) +++ 00:18 Financials (Balance Sheet, Cashflows, P&L) 05:37 Put / Call Options 04:04 Streaming Real-time Data 04:01 Importing Financial Data from Web Source 2 11 lectures • 53min Intro / Get your API Key 05:26 Installing the required Package 02:17 Historical Price and Volume Data for one Stock 03:34 Setting specific Time Periods 03:22 Stock Splits and Dividends 06:47 Converting to DatetimeIndex 03:39 Frequency Settings (Intraday) 04:51 Real-time Data for many Stocks 02:30 Technical Indicators 08:57 Currencies / FX 06:43 Cryptocurrencies 05:21 Importing Financial Data from Web Source 3 12 lectures • 49min Intro / Register and get your API Key 06:13 Commands to install required packages 00:23 Installing the required Package 02:15 Connecting to the API/Server 04:10 Currencies / FX (incl. Bid/Ask) 05:45 Frequency Settings (Intraday) 02:45 Setting specific Time Periods 07:50 Stock Indexes (incl. Bid/Ask) 04:01 Commodities (incl. Bid/Ask) 04:15 Cryptocurrencies (incl. Bid/Ask) 02:28 Streaming high-frequency real-time Data (Part 1) 07:07 Streaming high-frequency real-time Data (Part 2) 02:08 Web Source 3b (for US and Canadian Residents) 7 lectures • 20min Intro / Register 01:59 Commands to install required packages 00:06 Installing the required Packages 02:22 Get your API Key and connect to the Server 05:21 Getting Historical Data 04:36 Frequency Settings (high-frequency Intraday Data) 02:53 Streaming high-frequency real-time Data 02:43 Importing Financial Data from Web Source 4 18 lectures • 1hr 16min Intro / Register and get your API Key 06:17 Introduction to the API (hands-on) 05:19 Getting Historical Stock Prices and Volume Data 03:58 Stock Splits and Dividends 08:02 Financial Indexes 03:52 Currencies / FX 04:24 Cryptocurrencies 02:51 Commodities 02:13 Mutual Funds & ETFs 03:35 Treasury Yields 02:37 Stock Fundamentals, Meta Info and Performance Metrics 07:40 Financials (Balance Sheet, Cashflows, P&L) 03:20 Fundamentals and Performance Metrics for Funds & ETFs 05:04 Bond Data: Fundamentals 03:15 Bonda Data: Ratings 01:26 Bond Data: Historical Prices and Yields 02:10 Bulk Download of Ticker Symbols for entire Exchanges 06:05 Bulk Download of Stock Prices, Stock Splits and Dividends 04:19 Installing Python and Download/Working with Templates 4 lectures • 36min Installing Anaconda 08:08 How to open a Jupyter Notebook 09:29 Working with Jupyter Notebooks 14:00 Downloading and Working with Templates 04:17 Appendix 1: Pandas Crash Course 18 lectures • 2hr 2min Intro to Tabular Data / Pandas 05:03 Tabular Data Cheat Sheets 00:00 Download of Datasets (csv files) 00:03 First Steps (Inspection of Data, Part 1) 11:10 First Steps (Inspection of Data, Part 2) 08:45 Built-in Functions, Attributes and Methods 08:26 Make it easy: TAB Completion and Tooltip 08:57 Selecting Columns 08:01 Selecting Rows with iloc 07:42 Selecting Rows with loc 05:11 Pandas Series 06:44 Importing Time Series Data from csv-files 08:16 Converting strings to datetime objects with pd.to_datetime() 08:53 Initial Analysis / Visualization of Time Series 05:41 Indexing and Slicing Time Series 07:25 Initial Inspection and Visualization of Financial Time Series 05:32 Normalizing Time Series to a Base Value (100) 06:31 Hands-on: Importing Excel-Files to Python 09:47 What´s next? 1 lecture • 3min Get your special BONUS here! 02:36 Instructor Alexander Hagmann Data Scientist | Finance Professional | Entrepreneur 4.6 Instructor Rating 4,843 Reviews 44,928 Students 8 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). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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  • 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 Curated for the Udemy Business collection 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 Statement s (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. Show more Show less Course content 9 sections • 93 lectures • 7h 44m total length Expand all sections Getting Started 3 lectures • 18min Tips: How to get the most out of this Course Preview 05:27 Course Overview Preview 03:32 Hands-on: Downloading CSV-files and import to Python Preview 08:55 Importing Financial Data from Web Source 1 19 lectures • 1hr 27min Intro 03:05 Installing the required Package 03:03 Historical Price and Volume Data for one Stock Preview 04:01 Setting specific Time Periods 06:06 Frequency Settings (Intraday) 07:38 Stock Splits and Dividends 11:51 Exporting to CSV / Excel 05:36 Importing many Stocks 04:17 Financial Indexes 05:53 Currencies / FX 03:14 Cryptocurrencies 03:10 Mutual Funds & ETFs 02:44 Treasury Yields 03:38 The Ticker Object 04:21 Stock Fundamentals, Meta Info and Performance Metrics 04:22 +++IMPORTANT NOTICE & ACTION REQUIRED (before you start with next Lecture!) +++ 00:18 Financials (Balance Sheet, Cashflows, P&L) 05:37 Put / Call Options 04:04 Streaming Real-time Data 04:01 Importing Financial Data from Web Source 2 11 lectures • 53min Intro / Get your API Key 05:26 Installing the required Package 02:17 Historical Price and Volume Data for one Stock 03:34 Setting specific Time Periods 03:22 Stock Splits and Dividends 06:47 Converting to DatetimeIndex 03:39 Frequency Settings (Intraday) 04:51 Real-time Data for many Stocks 02:30 Technical Indicators 08:57 Currencies / FX 06:43 Cryptocurrencies 05:21 Importing Financial Data from Web Source 3 12 lectures • 49min Intro / Register and get your API Key 06:13 Commands to install required packages 00:23 Installing the required Package 02:15 Connecting to the API/Server 04:10 Currencies / FX (incl. Bid/Ask) 05:45 Frequency Settings (Intraday) 02:45 Setting specific Time Periods 07:50 Stock Indexes (incl. Bid/Ask) 04:01 Commodities (incl. Bid/Ask) 04:15 Cryptocurrencies (incl. Bid/Ask) 02:28 Streaming high-frequency real-time Data (Part 1) 07:07 Streaming high-frequency real-time Data (Part 2) 02:08 Web Source 3b (for US and Canadian Residents) 7 lectures • 20min Intro / Register 01:59 Commands to install required packages 00:06 Installing the required Packages 02:22 Get your API Key and connect to the Server 05:21 Getting Historical Data 04:36 Frequency Settings (high-frequency Intraday Data) 02:53 Streaming high-frequency real-time Data 02:43 Importing Financial Data from Web Source 4 18 lectures • 1hr 16min Intro / Register and get your API Key 06:17 Introduction to the API (hands-on) 05:19 Getting Historical Stock Prices and Volume Data 03:58 Stock Splits and Dividends 08:02 Financial Indexes 03:52 Currencies / FX 04:24 Cryptocurrencies 02:51 Commodities 02:13 Mutual Funds & ETFs 03:35 Treasury Yields 02:37 Stock Fundamentals, Meta Info and Performance Metrics 07:40 Financials (Balance Sheet, Cashflows, P&L) 03:20 Fundamentals and Performance Metrics for Funds & ETFs 05:04 Bond Data: Fundamentals 03:15 Bonda Data: Ratings 01:26 Bond Data: Historical Prices and Yields 02:10 Bulk Download of Ticker Symbols for entire Exchanges 06:05 Bulk Download of Stock Prices, Stock Splits and Dividends 04:19 Installing Python and Download/Working with Templates 4 lectures • 36min Installing Anaconda 08:08 How to open a Jupyter Notebook 09:29 Working with Jupyter Notebooks 14:00 Downloading and Working with Templates 04:17 Appendix 1: Pandas Crash Course 18 lectures • 2hr 2min Intro to Tabular Data / Pandas 05:03 Tabular Data Cheat Sheets 00:00 Download of Datasets (csv files) 00:03 First Steps (Inspection of Data, Part 1) 11:10 First Steps (Inspection of Data, Part 2) 08:45 Built-in Functions, Attributes and Methods 08:26 Make it easy: TAB Completion and Tooltip 08:57 Selecting Columns 08:01 Selecting Rows with iloc 07:42 Selecting Rows with loc 05:11 Pandas Series 06:44 Importing Time Series Data from csv-files 08:16 Converting strings to datetime objects with pd.to_datetime() 08:53 Initial Analysis / Visualization of Time Series 05:41 Indexing and Slicing Time Series 07:25 Initial Inspection and Visualization of Financial Time Series 05:32 Normalizing Time Series to a Base Value (100) 06:31 Hands-on: Importing Excel-Files to Python 09:47 What´s next? 1 lecture • 3min Get your special BONUS here! 02:36 Instructor Alexander Hagmann Data Scientist | Finance Professional | Entrepreneur 4.6 Instructor Rating 4,843 Reviews 44,928 Students 8 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). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'67813ec91b5953d3',m:'a0a0e4b20e7bcb824c413d51912e9e84467da70f-1627843755-1800-AX3qi8mx4kWsck+4ysyE+wrUl3CyVSOHk+rlN+d692Kqe9tbINK3PpgH1KRFvx98i3r5vzRmaX+LVD681rM24Jtbn+OrcZahRW0NVhCnfIh2HYsdP05x/HP3OvdPT4WIyDtJ4/bcr/O7J05SSAl6OGhhFgcvKyRfOzZGOXdMcQzlxHIvIO/CQ8lIKO+WMgN91Q==',s:[0x569664e397,0x8d8d3a0951],}})();
  • 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 Curated for the Udemy Business collection 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 Statement s (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. Show more Show less Course content 9 sections • 93 lectures • 7h 44m total length Expand all sections Getting Started 3 lectures • 18min Tips: How to get the most out of this Course Preview 05:27 Course Overview Preview 03:32 Hands-on: Downloading CSV-files and import to Python Preview 08:55 Importing Financial Data from Web Source 1 19 lectures • 1hr 27min Intro 03:05 Installing the required Package 03:03 Historical Price and Volume Data for one Stock Preview 04:01 Setting specific Time Periods 06:06 Frequency Settings (Intraday) 07:38 Stock Splits and Dividends 11:51 Exporting to CSV / Excel 05:36 Importing many Stocks 04:17 Financial Indexes 05:53 Currencies / FX 03:14 Cryptocurrencies 03:10 Mutual Funds & ETFs 02:44 Treasury Yields 03:38 The Ticker Object 04:21 Stock Fundamentals, Meta Info and Performance Metrics 04:22 +++IMPORTANT NOTICE & ACTION REQUIRED (before you start with next Lecture!) +++ 00:18 Financials (Balance Sheet, Cashflows, P&L) 05:37 Put / Call Options 04:04 Streaming Real-time Data 04:01 Importing Financial Data from Web Source 2 11 lectures • 53min Intro / Get your API Key 05:26 Installing the required Package 02:17 Historical Price and Volume Data for one Stock 03:34 Setting specific Time Periods 03:22 Stock Splits and Dividends 06:47 Converting to DatetimeIndex 03:39 Frequency Settings (Intraday) 04:51 Real-time Data for many Stocks 02:30 Technical Indicators 08:57 Currencies / FX 06:43 Cryptocurrencies 05:21 Importing Financial Data from Web Source 3 12 lectures • 49min Intro / Register and get your API Key 06:13 Commands to install required packages 00:23 Installing the required Package 02:15 Connecting to the API/Server 04:10 Currencies / FX (incl. Bid/Ask) 05:45 Frequency Settings (Intraday) 02:45 Setting specific Time Periods 07:50 Stock Indexes (incl. Bid/Ask) 04:01 Commodities (incl. Bid/Ask) 04:15 Cryptocurrencies (incl. Bid/Ask) 02:28 Streaming high-frequency real-time Data (Part 1) 07:07 Streaming high-frequency real-time Data (Part 2) 02:08 Web Source 3b (for US and Canadian Residents) 7 lectures • 20min Intro / Register 01:59 Commands to install required packages 00:06 Installing the required Packages 02:22 Get your API Key and connect to the Server 05:21 Getting Historical Data 04:36 Frequency Settings (high-frequency Intraday Data) 02:53 Streaming high-frequency real-time Data 02:43 Importing Financial Data from Web Source 4 18 lectures • 1hr 16min Intro / Register and get your API Key 06:17 Introduction to the API (hands-on) 05:19 Getting Historical Stock Prices and Volume Data 03:58 Stock Splits and Dividends 08:02 Financial Indexes 03:52 Currencies / FX 04:24 Cryptocurrencies 02:51 Commodities 02:13 Mutual Funds & ETFs 03:35 Treasury Yields 02:37 Stock Fundamentals, Meta Info and Performance Metrics 07:40 Financials (Balance Sheet, Cashflows, P&L) 03:20 Fundamentals and Performance Metrics for Funds & ETFs 05:04 Bond Data: Fundamentals 03:15 Bonda Data: Ratings 01:26 Bond Data: Historical Prices and Yields 02:10 Bulk Download of Ticker Symbols for entire Exchanges 06:05 Bulk Download of Stock Prices, Stock Splits and Dividends 04:19 Installing Python and Download/Working with Templates 4 lectures • 36min Installing Anaconda 08:08 How to open a Jupyter Notebook 09:29 Working with Jupyter Notebooks 14:00 Downloading and Working with Templates 04:17 Appendix 1: Pandas Crash Course 18 lectures • 2hr 2min Intro to Tabular Data / Pandas 05:03 Tabular Data Cheat Sheets 00:00 Download of Datasets (csv files) 00:03 First Steps (Inspection of Data, Part 1) 11:10 First Steps (Inspection of Data, Part 2) 08:45 Built-in Functions, Attributes and Methods 08:26 Make it easy: TAB Completion and Tooltip 08:57 Selecting Columns 08:01 Selecting Rows with iloc 07:42 Selecting Rows with loc 05:11 Pandas Series 06:44 Importing Time Series Data from csv-files 08:16 Converting strings to datetime objects with pd.to_datetime() 08:53 Initial Analysis / Visualization of Time Series 05:41 Indexing and Slicing Time Series 07:25 Initial Inspection and Visualization of Financial Time Series 05:32 Normalizing Time Series to a Base Value (100) 06:31 Hands-on: Importing Excel-Files to Python 09:47 What´s next? 1 lecture • 3min Get your special BONUS here! 02:36 Instructor Alexander Hagmann Data Scientist | Finance Professional | Entrepreneur 4.6 Instructor Rating 4,843 Reviews 44,928 Students 8 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). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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  • Downloading Historical Prices for Currencies (FOREX), Cryptocurrencies, Bonds & more Saving / Storing the Data locally Pandas Coding Crash Course Curated for the Udemy Business collection 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 Statement s (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. Show more Show less Course content 9 sections • 93 lectures • 7h 44m total length Expand all sections Getting Started 3 lectures • 18min Tips: How to get the most out of this Course Preview 05:27 Course Overview Preview 03:32 Hands-on: Downloading CSV-files and import to Python Preview 08:55 Importing Financial Data from Web Source 1 19 lectures • 1hr 27min Intro 03:05 Installing the required Package 03:03 Historical Price and Volume Data for one Stock Preview 04:01 Setting specific Time Periods 06:06 Frequency Settings (Intraday) 07:38 Stock Splits and Dividends 11:51 Exporting to CSV / Excel 05:36 Importing many Stocks 04:17 Financial Indexes 05:53 Currencies / FX 03:14 Cryptocurrencies 03:10 Mutual Funds & ETFs 02:44 Treasury Yields 03:38 The Ticker Object 04:21 Stock Fundamentals, Meta Info and Performance Metrics 04:22 +++IMPORTANT NOTICE & ACTION REQUIRED (before you start with next Lecture!) +++ 00:18 Financials (Balance Sheet, Cashflows, P&L) 05:37 Put / Call Options 04:04 Streaming Real-time Data 04:01 Importing Financial Data from Web Source 2 11 lectures • 53min Intro / Get your API Key 05:26 Installing the required Package 02:17 Historical Price and Volume Data for one Stock 03:34 Setting specific Time Periods 03:22 Stock Splits and Dividends 06:47 Converting to DatetimeIndex 03:39 Frequency Settings (Intraday) 04:51 Real-time Data for many Stocks 02:30 Technical Indicators 08:57 Currencies / FX 06:43 Cryptocurrencies 05:21 Importing Financial Data from Web Source 3 12 lectures • 49min Intro / Register and get your API Key 06:13 Commands to install required packages 00:23 Installing the required Package 02:15 Connecting to the API/Server 04:10 Currencies / FX (incl. Bid/Ask) 05:45 Frequency Settings (Intraday) 02:45 Setting specific Time Periods 07:50 Stock Indexes (incl. Bid/Ask) 04:01 Commodities (incl. Bid/Ask) 04:15 Cryptocurrencies (incl. Bid/Ask) 02:28 Streaming high-frequency real-time Data (Part 1) 07:07 Streaming high-frequency real-time Data (Part 2) 02:08 Web Source 3b (for US and Canadian Residents) 7 lectures • 20min Intro / Register 01:59 Commands to install required packages 00:06 Installing the required Packages 02:22 Get your API Key and connect to the Server 05:21 Getting Historical Data 04:36 Frequency Settings (high-frequency Intraday Data) 02:53 Streaming high-frequency real-time Data 02:43 Importing Financial Data from Web Source 4 18 lectures • 1hr 16min Intro / Register and get your API Key 06:17 Introduction to the API (hands-on) 05:19 Getting Historical Stock Prices and Volume Data 03:58 Stock Splits and Dividends 08:02 Financial Indexes 03:52 Currencies / FX 04:24 Cryptocurrencies 02:51 Commodities 02:13 Mutual Funds & ETFs 03:35 Treasury Yields 02:37 Stock Fundamentals, Meta Info and Performance Metrics 07:40 Financials (Balance Sheet, Cashflows, P&L) 03:20 Fundamentals and Performance Metrics for Funds & ETFs 05:04 Bond Data: Fundamentals 03:15 Bonda Data: Ratings 01:26 Bond Data: Historical Prices and Yields 02:10 Bulk Download of Ticker Symbols for entire Exchanges 06:05 Bulk Download of Stock Prices, Stock Splits and Dividends 04:19 Installing Python and Download/Working with Templates 4 lectures • 36min Installing Anaconda 08:08 How to open a Jupyter Notebook 09:29 Working with Jupyter Notebooks 14:00 Downloading and Working with Templates 04:17 Appendix 1: Pandas Crash Course 18 lectures • 2hr 2min Intro to Tabular Data / Pandas 05:03 Tabular Data Cheat Sheets 00:00 Download of Datasets (csv files) 00:03 First Steps (Inspection of Data, Part 1) 11:10 First Steps (Inspection of Data, Part 2) 08:45 Built-in Functions, Attributes and Methods 08:26 Make it easy: TAB Completion and Tooltip 08:57 Selecting Columns 08:01 Selecting Rows with iloc 07:42 Selecting Rows with loc 05:11 Pandas Series 06:44 Importing Time Series Data from csv-files 08:16 Converting strings to datetime objects with pd.to_datetime() 08:53 Initial Analysis / Visualization of Time Series 05:41 Indexing and Slicing Time Series 07:25 Initial Inspection and Visualization of Financial Time Series 05:32 Normalizing Time Series to a Base Value (100) 06:31 Hands-on: Importing Excel-Files to Python 09:47 What´s next? 1 lecture • 3min Get your special BONUS here! 02:36 Instructor Alexander Hagmann Data Scientist | Finance Professional | Entrepreneur 4.6 Instructor Rating 4,843 Reviews 44,928 Students 8 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). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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  • Saving / Storing the Data locally Pandas Coding Crash Course Curated for the Udemy Business collection 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 Statement s (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. Show more Show less Course content 9 sections • 93 lectures • 7h 44m total length Expand all sections Getting Started 3 lectures • 18min Tips: How to get the most out of this Course Preview 05:27 Course Overview Preview 03:32 Hands-on: Downloading CSV-files and import to Python Preview 08:55 Importing Financial Data from Web Source 1 19 lectures • 1hr 27min Intro 03:05 Installing the required Package 03:03 Historical Price and Volume Data for one Stock Preview 04:01 Setting specific Time Periods 06:06 Frequency Settings (Intraday) 07:38 Stock Splits and Dividends 11:51 Exporting to CSV / Excel 05:36 Importing many Stocks 04:17 Financial Indexes 05:53 Currencies / FX 03:14 Cryptocurrencies 03:10 Mutual Funds & ETFs 02:44 Treasury Yields 03:38 The Ticker Object 04:21 Stock Fundamentals, Meta Info and Performance Metrics 04:22 +++IMPORTANT NOTICE & ACTION REQUIRED (before you start with next Lecture!) +++ 00:18 Financials (Balance Sheet, Cashflows, P&L) 05:37 Put / Call Options 04:04 Streaming Real-time Data 04:01 Importing Financial Data from Web Source 2 11 lectures • 53min Intro / Get your API Key 05:26 Installing the required Package 02:17 Historical Price and Volume Data for one Stock 03:34 Setting specific Time Periods 03:22 Stock Splits and Dividends 06:47 Converting to DatetimeIndex 03:39 Frequency Settings (Intraday) 04:51 Real-time Data for many Stocks 02:30 Technical Indicators 08:57 Currencies / FX 06:43 Cryptocurrencies 05:21 Importing Financial Data from Web Source 3 12 lectures • 49min Intro / Register and get your API Key 06:13 Commands to install required packages 00:23 Installing the required Package 02:15 Connecting to the API/Server 04:10 Currencies / FX (incl. Bid/Ask) 05:45 Frequency Settings (Intraday) 02:45 Setting specific Time Periods 07:50 Stock Indexes (incl. Bid/Ask) 04:01 Commodities (incl. Bid/Ask) 04:15 Cryptocurrencies (incl. Bid/Ask) 02:28 Streaming high-frequency real-time Data (Part 1) 07:07 Streaming high-frequency real-time Data (Part 2) 02:08 Web Source 3b (for US and Canadian Residents) 7 lectures • 20min Intro / Register 01:59 Commands to install required packages 00:06 Installing the required Packages 02:22 Get your API Key and connect to the Server 05:21 Getting Historical Data 04:36 Frequency Settings (high-frequency Intraday Data) 02:53 Streaming high-frequency real-time Data 02:43 Importing Financial Data from Web Source 4 18 lectures • 1hr 16min Intro / Register and get your API Key 06:17 Introduction to the API (hands-on) 05:19 Getting Historical Stock Prices and Volume Data 03:58 Stock Splits and Dividends 08:02 Financial Indexes 03:52 Currencies / FX 04:24 Cryptocurrencies 02:51 Commodities 02:13 Mutual Funds & ETFs 03:35 Treasury Yields 02:37 Stock Fundamentals, Meta Info and Performance Metrics 07:40 Financials (Balance Sheet, Cashflows, P&L) 03:20 Fundamentals and Performance Metrics for Funds & ETFs 05:04 Bond Data: Fundamentals 03:15 Bonda Data: Ratings 01:26 Bond Data: Historical Prices and Yields 02:10 Bulk Download of Ticker Symbols for entire Exchanges 06:05 Bulk Download of Stock Prices, Stock Splits and Dividends 04:19 Installing Python and Download/Working with Templates 4 lectures • 36min Installing Anaconda 08:08 How to open a Jupyter Notebook 09:29 Working with Jupyter Notebooks 14:00 Downloading and Working with Templates 04:17 Appendix 1: Pandas Crash Course 18 lectures • 2hr 2min Intro to Tabular Data / Pandas 05:03 Tabular Data Cheat Sheets 00:00 Download of Datasets (csv files) 00:03 First Steps (Inspection of Data, Part 1) 11:10 First Steps (Inspection of Data, Part 2) 08:45 Built-in Functions, Attributes and Methods 08:26 Make it easy: TAB Completion and Tooltip 08:57 Selecting Columns 08:01 Selecting Rows with iloc 07:42 Selecting Rows with loc 05:11 Pandas Series 06:44 Importing Time Series Data from csv-files 08:16 Converting strings to datetime objects with pd.to_datetime() 08:53 Initial Analysis / Visualization of Time Series 05:41 Indexing and Slicing Time Series 07:25 Initial Inspection and Visualization of Financial Time Series 05:32 Normalizing Time Series to a Base Value (100) 06:31 Hands-on: Importing Excel-Files to Python 09:47 What´s next? 1 lecture • 3min Get your special BONUS here! 02:36 Instructor Alexander Hagmann Data Scientist | Finance Professional | Entrepreneur 4.6 Instructor Rating 4,843 Reviews 44,928 Students 8 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). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'67813ec91b5953d3',m:'a0a0e4b20e7bcb824c413d51912e9e84467da70f-1627843755-1800-AX3qi8mx4kWsck+4ysyE+wrUl3CyVSOHk+rlN+d692Kqe9tbINK3PpgH1KRFvx98i3r5vzRmaX+LVD681rM24Jtbn+OrcZahRW0NVhCnfIh2HYsdP05x/HP3OvdPT4WIyDtJ4/bcr/O7J05SSAl6OGhhFgcvKyRfOzZGOXdMcQzlxHIvIO/CQ8lIKO+WMgN91Q==',s:[0x569664e397,0x8d8d3a0951],}})();
  • Pandas Coding Crash Course Curated for the Udemy Business collection 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 Statement s (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. Show more Show less Course content 9 sections • 93 lectures • 7h 44m total length Expand all sections Getting Started 3 lectures • 18min Tips: How to get the most out of this Course Preview 05:27 Course Overview Preview 03:32 Hands-on: Downloading CSV-files and import to Python Preview 08:55 Importing Financial Data from Web Source 1 19 lectures • 1hr 27min Intro 03:05 Installing the required Package 03:03 Historical Price and Volume Data for one Stock Preview 04:01 Setting specific Time Periods 06:06 Frequency Settings (Intraday) 07:38 Stock Splits and Dividends 11:51 Exporting to CSV / Excel 05:36 Importing many Stocks 04:17 Financial Indexes 05:53 Currencies / FX 03:14 Cryptocurrencies 03:10 Mutual Funds & ETFs 02:44 Treasury Yields 03:38 The Ticker Object 04:21 Stock Fundamentals, Meta Info and Performance Metrics 04:22 +++IMPORTANT NOTICE & ACTION REQUIRED (before you start with next Lecture!) +++ 00:18 Financials (Balance Sheet, Cashflows, P&L) 05:37 Put / Call Options 04:04 Streaming Real-time Data 04:01 Importing Financial Data from Web Source 2 11 lectures • 53min Intro / Get your API Key 05:26 Installing the required Package 02:17 Historical Price and Volume Data for one Stock 03:34 Setting specific Time Periods 03:22 Stock Splits and Dividends 06:47 Conver