Manage Finance Data with Python & Pandas: Unique Masterclass

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Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 9.78 GB | Duration: 25.5 hours

Create Interactive Price Charts with Technical Indicators (Volume, OHLC, Candlestick, SMA etc.

What you'll learn
Step into the Financial Analyst role and give advice on a client´s financial Portfolio (Final Project)
Import large Financial Datasets / historical Prices from Web Sources and analyze, aggregate and visualize them
Calculate Return, Risk, Correlation and Rolling Statistics for Stocks, Indexes and Portfolios
Create, analyze and optimize financial Portfolios and understand the use of the Sharpe Ratio
Intuitively understand Modern Portfolio Theory (CAPM, Beta, Alpha, CML, SML, Risk Diversification) with Real Data examples
)
Create Financial Indexes (price-, equal- and value- weighted) and understand the difference between Price Return and Total Return
Easily switch between daily, weekly, monthly and annual returns and understand the benefits of log returns
Start from Zero and learn all the Basics of the powerful Pandas Library
Requirements
No specific Finance knowledge needed! The course intuitively explains the major fundamentals of Finance and Portfolio Theorie based on data examples.
Ideally some Spreadsheet Basics/Programming Basics (not mandatory, the course guides you through the 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
Some high school level math skills would be great (not mandatory, but it helps)
Description
The Finance and Investment Industry is experiencing a dramatic change driven by ever increasing processing power & connectivity and the introduction of powerful Machine Learning tools. The Finance and Investment Industry more and more shifts from a math/formula-based business to a data-driven business.
What can you do to keep pace?
No matter if you want to dive deep into Machine Learning, or if you simply want to increase productivity at work when handling Financial Data, there is the very first and most important step: Leave Excel behind and manage your Financial Data with Python and Pandas!
Pandas is the Excel for Python and learning Pandas from scratch is almost as easy as learning Excel. Pandas seems to be more complex at a first glance, as it simply offers so much more functionalities. The workflows you are used to do with Excel can be done with Pandas more efficiently. Pandas is a high-level coding library where all the hardcore coding stuff with dozens of coding lines are running automatically in the background. Pandas operations are typically done in one line of code! However, it is important to learn and master Pandas in a way that
you understand what is going on
you are aware of the pitfalls (Don´ts)
you know best practices (Dos)
MANAGE FINANCE DATA WITH PYTHON & PANDAS best prepares you to master the new challenges and to stay ahead of your peers, fellows and competitors! Coding with Python/Pandas is one of the most in-Demand skills in Finance.
This course is one of the most practical courses on Udemy with 200 Coding Exercises and a Final Project. You are free to select your individual level of difficulty. If you have no experience with Pandas at all, Part 1 will teach you all essentials (From Zero to Hero).
Part 2 - The Core of this Course
Import Financial Data from Free Web Sources, Excel- and CSV-Files
Calculate Risk, Return and Correlation of Stocks, Indexes and Portfolios
Calculate simple Returns, log Returns and annualized Returns & Risk
Create your own customized Financial Index (price-weighted vs. equal-weighted vs. value-weighted)
Understand the difference between Price Return and Total Return
Create, analyze and optimize Stock Portfolios
Calculate Sharpe Ratio, Systematic Risk, Unsystematic Risk, Beta and Alpha for Stocks, Indexes and Portfolios
Understand Modern Portfolio Theory, Risk Diversification and the Capital Asset Pricing Model (CAPM)
Forward-looking Mean-Variance Optimization (MVO) and its pitfalls
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Code:

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Code:

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