Udemy Importing Finance Data with Python from Free Web Sources

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Download Free Download : Udemy Importing Finance Data with Python from Free Web Sources
mp4 | Video: h264,1920X1080 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:2.9 GB
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Files Included :

Spoiler

1 - Tips How to get the most out of this Course.mp4 (43.69 MB)
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2 - Course Overview.mp4 (18.89 MB)
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3 - Handson Downloading CSVfiles and import to Python.mp4 (66.64 MB)
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10 - Exporting to CSV Excel.mp4 (40.11 MB)
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11 - Importing many Stocks.mp4 (27.91 MB)
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12 - Financial Indexes.mp4 (36.77 MB)
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13 - Currencies FX.mp4 (14.77 MB)
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14 - Cryptocurrencies.mp4 (18.38 MB)
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15 - Mutual Funds & ETFs EV.ts (8.42 MB)
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16 - Treasury Yields.mp4 (20.41 MB)
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17 - The Ticker Object.mp4 (13.44 MB)
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18 - Stock Fundamentals Meta Info and Performance Metrics.mp4 (23.13 MB)
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20 - Financials Balance Sheet Cashflows P&L.mp4 (40.9 MB)
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21 - Put Call Options.mp4 (46.81 MB)
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22 - Streaming Realtime Data.mp4 (37.64 MB)
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4 - Intro.mp4 (7.77 MB)
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5 - Installing the required Package.mp4 (31.45 MB)
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6 - Historical Price and Volume Data for one Stock.mp4 (24.09 MB)
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7 - Setting specific Time Periods.mp4 (30.51 MB)
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8 - Frequency Settings Intraday.mp4 (55.02 MB)
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9 - Stock Splits and Dividends.mp4 (80.44 MB)
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23 - Intro Get your API Key.mp4 (24.97 MB)
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24 - Installing the required Package.mp4 (9.22 MB)
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25 - Historical Price and Volume Data for one Stock.mp4 (21.34 MB)
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26 - Setting specific Time Periods.mp4 (10.82 MB)
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27 - Stock Splits and Dividends.mp4 (32.85 MB)
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28 - Converting to DatetimeIndex.mp4 (23.77 MB)
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29 - Frequency Settings Intraday.mp4 (35.89 MB)
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30 - Realtime Data for many Stocks.mp4 (7.24 MB)
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31 - Technical Indicators.mp4 (72.93 MB)
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32 - Currencies FX.mp4 (49.91 MB)
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34 - Intro Register and get your API Key.mp4 (48.21 MB)
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36 - Installing the required Package.mp4 (9.34 MB)
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37 - Connecting to the APIServer.mp4 (10.58 MB)
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38 - Currencies FX incl BidAsk.mp4 (51.9 MB)
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39 - Frequency Settings Intraday.mp4 (11.71 MB)
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40 - Setting specific Time Periods.mp4 (41.12 MB)
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41 - Stock Indexes incl BidAsk.mp4 (29.88 MB)
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42 - Commodities incl BidAsk.mp4 (37.38 MB)
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43 - Cryptocurrencies incl BidAsk.mp4 (12.67 MB)
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44 - Streaming highfrequency realtime Data Part 1.mp4 (52.43 MB)
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45 - Streaming highfrequency realtime Data Part 2.mp4 (16.34 MB)
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46 - Intro Register.mp4 (27.41 MB)
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48 - Installing the required Packages.mp4 (9.78 MB)
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49 - Get your API Key and connect to the Server.mp4 (25.76 MB)
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49 - OANDA.zip (915 B)
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50 - Getting Historical Data.mp4 (21.65 MB)
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51 - Frequency Settings highfrequency Intraday Data.mp4 (19.79 MB)
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52 - Streaming highfrequency realtime Data.mp4 (8.12 MB)
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53 - Intro Register and get your API Key.mp4 (63.52 MB)
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54 - Introduction to the API handson.mp4 (49.52 MB)
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55 - Getting Historical Stock Prices and Volume Data.mp4 (20.52 MB)
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56 - Stock Splits and Dividends.mp4 (65.37 MB)
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57 - Financial Indexes.mp4 (32.64 MB)
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58 - Currencies FX.mp4 (34.57 MB)
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59 - Cryptocurrencies.mp4 (23.78 MB)
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60 - Commodities.mp4 (17.48 MB)
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61 - Mutual Funds & ETFs.mp4 (34.92 MB)
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62 - Treasury Yields.mp4 (28.43 MB)
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63 - Stock Fundamentals Meta Info and Performance Metrics.mp4 (65.46 MB)
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64 - Financials Balance Sheet Cashflows P&L.mp4 (33.05 MB)
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65 - Fundamentals and Performance Metrics for Funds & ETFs.mp4 (69.98 MB)
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66 - Bond Data Fundamentals.mp4 (14.09 MB)
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67 - Bonda Data Ratings.mp4 (4.12 MB)
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68 - Bond Data Historical Prices and Yields.mp4 (6.18 MB)
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69 - Bulk Download of Ticker Symbols for entire Exchanges.mp4 (50.67 MB)
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70 - Bulk Download of Stock Prices Stock Splits and Dividends.mp4 (34.51 MB)
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71 - Installing Anaconda.mp4 (69.88 MB)
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72 - How to open a Jupyter Notebook.mp4 (64.94 MB)
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73 - Working with Jupyter Notebooks.mp4 (57.96 MB)
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74 - Downloading and Working with Templates.mp4 (16.42 MB)
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74 - Templates.zip (8.31 KB)
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75 - Intro to Tabular Data Pandas.mp4 (21.36 MB)
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77 - Datasets.zip (977.59 KB)
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78 - First Steps Inspection of Data Part 1.mp4 (74.26 MB)
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79 - First Steps Inspection of Data Part 2.mp4 (32.74 MB)
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80 - Builtin Functions Attributes and Methods.mp4 (56.12 MB)
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81 - Make it easy TAB Completion and Tooltip.mp4 (34.13 MB)
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82 - Selecting Columns.mp4 (26.09 MB)
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83 - Selecting Rows with iloc.mp4 (60.26 MB)
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84 - Selecting Rows with loc.mp4 (33.6 MB)
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85 - Pandas Series.mp4 (28.89 MB)
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86 - Importing Time Series Data from csvfiles.mp4 (30.82 MB)
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87 - Converting strings to datetime objects with pdtodatetime.mp4 (66.24 MB)
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88 - Initial Analysis Visualization of Time Series.mp4 (25.74 MB)
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89 - Indexing and Slicing Time Series.mp4 (66.7 MB)
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90 - Initial Inspection and Visualization of Financial Time Series.mp4 (22.47 MB)
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91 - Normalizing Time Series to a Base Value 100.mp4 (35.78 MB)
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92 - Handson Importing ExcelFiles to Python.mp4 (81.5 MB)
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Importing Finance Data with Python from Free Web Sources
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 3.16 GB
Genre: eLearning Video | Duration: 93 lectures (7 hour, 43 mins) | Language: English​

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

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)

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!

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.

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