Python for Finance and Data Science

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Algovibes YT

8:49:44

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1 - Introduction

1 - What does this course cover.mp4

01:18

2 - Disclaimer MUST WATCH.mp4

01:25

3 - How to get the most of this course.mp4

00:20

4 - Any questions or problems Reach out.mp4

00:20

2 - Installation and Jupyter Notebook Basics

5 - Download Anaconda Set Up Jupyter Notebook.mp4

03:09

6 - Jupyter Notebook Basics.mp4

01:20

3 - Python Fundamentals

7 - Variables Single Datatypes.mp4

04:30

8 - What you should NEVER do.mp4

01:32

9 - Typecasting User Input.mp4

07:05

10 - Practice Time.mp4

01:24

11 - Arithmetic Operators.mp4

00:56

12 - Comparison Operators Logical Operators.mp4

02:01

13 - Indentations IfStatements.mp4

03:20

14 - Practice Time.mp4

02:52

15 - Lists as objects with methods in Python.mp4

02:08

16 - List Slicing Indexing.mp4

02:42

17 - Difference between lists tuples.mp4

01:08

18 - Dictionaries.mp4

01:12

19 - For loops.mp4

03:24

20 - Combining lists loops List comprehension.mp4

01:21

21 - While loop.mp4

01:47

22 - Practice Time.mp4

02:08

23 - Practice your knowledge with a common Interview question.mp4

01:45

24 - Functions.mp4

04:03

4 - Fundamentals of Pandas

25 - Setting up a DataFrame and DataFrame properties.mp4

03:28

26 - Adding columns and using dictionaries for DataFrame initialization.mp4

02:58

27 - New columns based on calculations.mp4

03:29

28 - Data Selection with iloc.mp4

04:20

29 - Data Selection with loc.mp4

03:38

30 - Data Filtering with Boolean Masks and Boolean Indexing.mp4

04:24

5 - Applied Financial Data Analysis

31 - Pulling stock prices and OHLC data.mp4

03:58

32 - Quick Recap on what we did in the last chapter.mp4

02:00

33 - Return calculation with shift and pctchange.mp4

06:14

34 - Important functions diff dropna rolling.mp4

05:49

35 - Very important argument axis0 or axis1.mp4

03:27

36 - nlargest and nsmallest.mp4

02:07

37 - Bringing together Dataframes Concat.mp4

03:52

38 - Combining Time Series and OHLC in general.mp4

04:54

39 - Resampling Data.mp4

03:19

40 - Resampling OHLC Data.mp4

01:51

41 - Plotting in Pandas.mp4

03:35

42 - Iterating over a dataframe Iterrows.mp4

05:44

43 - Performance Comparison Iterrows vs Vectorization.mp4

02:39

44 - Return calculation deep dive.mp4

11:20

45 - Practice Task Plot the yearly returns of the SP500.mp4

00:28

46 - Solution to the Practice Task Plot yearly returns of the SP500.mp4

02:42

6 - Portfolio Analysis and Portfolio Management with Python

47 - Portfolio Analysis Introduction.mp4

00:47

48 - Variance Standarddeviation Covariance and Correlation.mp4

04:54

49 - Portfolio Return and Risk.mp4

02:48

50 - Portfolio Expected Return and Portfolio Risk using Python.mp4

03:53

51 - Use the Dot Product to calculate Portfolio Return and Portfolio Risk.mp4

07:34

52 - Application to real data Portfolio of Microsoft Coca Cola and Tesla.mp4

10:36

52 - portfolio-analysis.zip

53 - Efficient Frontier Minimum Variance Portfolio and dominant Portfolios.mp4

13:17

7 - Introduction to Backtesting Trading Strategies

54 - Introduction and the Strategy.mp4

01:19

55 - Coding the Trading Strategy iterative approach.mp4

18:09

55 - backtest-iteratively.zip

56 - Vectorizing the Backtest.mp4

12:10

56 - backtest-vectorized.zip

8 - Project I Momentum Trading Strategies

57 - Crosssectional Momentum Part I Survivorship Bias Handling.mp4

08:19

58 - Crosssectional Momentum Part II Constructing and Backtesting.mp4

11:01

58 - momentum-s-p500-full.zip

59 - TimeSeries Momentum.mp4

14:26

9 - Project II Backtesting JPMorgans Volatility Index VIX based Strategy

60 - Backtesting JPMorgans Volatility Index VIX based Strategy.mp4

18:04

60 - jpm.zip

10 - Project III Stock Market Analysis Interactive Dashboards with Streamlit

61 - Brief Intro to Streamlit.mp4

18:09

62 - Streamlit Portfolio Analysis Dashboard.mp4

11:11

63 - Streamlit Dashboard showing the Top and Worst SP500 Index performers.mp4

19:36

63 - equity-index-streamlit.zip

11 - Project IV Machine Learning applied on Stock Data

64 - A Machine Learning Model which potentially outperformed the SP500.mp4

28:08

64 - ml-logit-strategy.zip

65 - Least Squares Moving Average Trading Strategy.mp4

25:00

65 - lsma.zip

12 - Project V An advanced guide to Backtesting and Optimization on over 500 Stocks

66 - Iterative Approach.mp4

19:08

66 - backtest-adv-iterative.zip

67 - Vectorized Approach.mp4

23:10

68 - Results Analysis.mp4

12:49

13 - Project VI Optimizing a Portfolio based on the Sharpe Ratio

69 - Recap on Matrix Operations Expected return and Portfolio Risk.mp4

19:22

70 - Optimization of Portfolio weights.mp4

24:18

14 - Extra Chapter Pandas SQL

71 - The mighty Intersection between Pandas and SQL.mp4

04:12

72 - How to update an SQL Database with Pandas and SQL.mp4

16:52

73 - Build your own Finance DB using Pandas SQL.mp4

14:01

74 - Build a simple Stock recommendation System with your Finance DB.mp4

12:40

74 - MACD indicator explained.txt

75 - Build an Intraday Stock Price Database with Python and SQL.mp4

11:46

15 - What I would like to give you on your way Thank you

76 - Thank you and something to take along.mp4

02:39

Description

Learn Python Programming and apply Financial Data Science to REAL data - from Beginner to Professional

What You'll Learn?

Learn how to code in Python from scratch
Be a PRO in Data Analysis in specific Financial Data
Build and Backtest Trading Strategies with Python
Understand and Optimize the Return and Risk profile of your Portfolio
Compare stocks and Portfolio in terms of their Sharpe ratio
Have an outstanding technical skillset to apply for a quant job in a financial institution or data based company
Be able to perform in depth Investment Analysis
Solve real-world problems using Python
Visualize your data in interactive Dashboards
Learn about best practices and relevant practice advice working with financial data
Be able to compare stocks
Understand the difference between Log returns and returns
Optimize weights by using the concept of the Efficient Frontier
Leverage Algebra concepts to do powerful calculations
Learn to use the powerful intersection of Pandas & SQL to build, maintain and leverage Databases
Understand how you can leverage Algebra to make powerful computations

Who is this for?

Business and Finance students who look for an opportunity to attain a high in demand skillset

People who are interested in applied Financial Analysis

People who want to get a better understanding of there own portfolio

People who are interested in Finance, Data Science and Analytics

Hands-on oriented people

People who want to build a highly valuable skillset

People who want to understand the statistics and Algebra behind Portfolio Analysis

What You Need to Know?

No programming experience required. We are starting from Zero.

It helps to have a basic understanding of the stock market but it isn't mandatory

More details

Description
Are you ready to revolutionize your understanding of Finance and Data Science?
Dive into the world of Python for Finance and Data Science, where cutting-edge technology meets the dynamic field of financial analysis.
In this comprehensive course, I will guide you through the essential principles and practical techniques that will supercharge your financial analysis skills. Whether you're an aspiring financial professional, data scientist, quant-oriented or simply eager to expand your knowledge, this course will empower you to extract valuable insights from financial data and make informed decisions.
Harness the power of Python, the industry's leading programming language for data analysis and automation. Explore the intricacies of financial data retrieval, preprocessing, manipulation and gain the tools to transform raw data into compelling visualizations and intuitive dashboards.
Discover how to implement Portfolio Analysis and Portfolio optimization techniques, all using Python. Uncover hidden patterns in the data, build and backtest trading strategies, and explore algorithmic trading possibilities.
But it doesn't stop there! This course goes beyond finance by incorporating essential data science concepts. You'll master the art of Data manipulation,Â Portfolio Analysis, Applied Financial Analysis, Backtesting and uncover critical business insights.

Get ready for hands-on exercises, real-world examples, and expert guidance from an actively working quant finance professional
My engaging curriculum ensures a seamless learning experience as I am equipping you with the skills to excel in the fast-paced world of finance and Data Science.

Don't miss this opportunity to transform your career and gain a competitive edge in the financial or data industry. Enroll now and unleash the full potential of Python for Finance and Data Science!

What will YOU learn in specific?
Fundamental Python Programming
An Introduction to one of the most powerful Data Science and Financial Data Analysis Libraries: Pandas
A FULL guide into applied Financial Data Analysis
A FULLÂ guide into Portfolio Analysis and Portfolio Management with Python on real stock data
You will learn to quantitatively analyze you own portfolio and give it a reality check! :-)
An Introduction to Backtesting Trading Strategies and Vectorization
Optimizing a Portfolio using state of the art tools
Advanced Trading Strategies using concepts of Optimization and Machine Learning
Building state of the art and beautiful Interactive Finance Dashboard
Learn about the powerful Intersection of Pandas &Â SQL and use it to leverage your knowledge

Why this course and no other one?
I am actively working in the field of quant Finance covering Data Science and quantitive Finance topics since several years and wrote my Master Thesis in quantitative Finance - I know what's relevant in practice but also what is relevant to cover to level up!
I have taught Python for Finance and Automated Trading topics to over 75.000 people on YouTube and countless people privately.
You will get a lot of Quizzes, Exercises to apply what I taught and I will give you relevant tips and practical advise. I challenge you to solve all of the provided exercises! :-)
There is no single time filler in this course. We are getting straight to the topics and I am being as brief as possible but also taking my time to be as specific as possible
Outstanding support: If you donâ€™t understand something, you feel you are stuck or you simply want to connect with me just write me a message and I am getting back to you as soon as possible!

What are you waiting for? Click 'Enroll now'Â to get started! I am excited and looking forward to see you inside the course :-)
Who this course is for:
Business and Finance students who look for an opportunity to attain a high in demand skillset
People who are interested in applied Financial Analysis
People who want to get a better understanding of there own portfolio
People who are interested in Finance, Data Science and Analytics
Hands-on oriented people
People who want to build a highly valuable skillset
People who want to understand the statistics and Algebra behind Portfolio Analysis

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Algovibes is one of the biggest YouTube channels in the financial programming niche. Having more than 75.000 subscribers following topics are covered:- Data Science- Algorithmic Trading- Automatization of Trading Strategies- Tradingbot constructionThe channel not only covers backtesting various trading strategies but also goes into deploying the trading strategies to live trading.Besides that the channel owner uses various approaches out of the Data Science field to find and show insights on the stock and cryptocurrency market.The channel owner has several years of both academic and professional coding experience with a main focus on Python.

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