Mô tả

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, 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 FULL 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 & 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 don’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'  to get started! I am excited and looking forward to see you inside the course :-)

Bạn sẽ học được gì

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

Yêu cầu

  • 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

Nội dung khoá học

15 sections

Introduction

4 lectures
What does this course cover?
01:18
Disclaimer [MUST WATCH!]
01:25
How to get the most of this course?
00:20
Any questions or problems? Reach out!
00:20

Installation and Jupyter Notebook Basics

2 lectures
Download Anaconda & Set Up Jupyter Notebook
03:09
Jupyter Notebook Basics
01:20

Python Fundamentals

18 lectures
Variables & Single Datatypes
04:30
What you should NEVER do
01:32
Typecasting & User Input
07:05
Practice Time :-)
01:24
Arithmetic Operators
00:56
Comparison Operators / Logical Operators
02:01
Indentations & If-Statements
03:20
Practice Time :-)
02:52
Lists as objects with methods in Python
02:08
List Slicing & Indexing
02:42
Difference between lists & tuples
01:08
Dictionaries
01:12
For loops
03:24
Combining lists & loops: List comprehension
01:21
While loop
01:47
Practice Time :-)
02:08
Practice your knowledge with a common Interview question!
01:45
Functions
04:03

Fundamentals of Pandas

6 lectures
Setting up a DataFrame and DataFrame properties
03:28
Adding columns and using dictionaries for DataFrame initialization
02:58
New columns based on calculations
03:29
Data Selection with iloc
04:20
Data Selection with loc
03:38
Data Filtering with Boolean Masks and Boolean Indexing
04:24

Applied Financial Data Analysis

18 lectures
Pulling stock prices and OHLC data
03:58
Quick Recap on what we did in the last chapter
02:00
Return calculation with shift and pct_change
06:14
Important functions: diff, dropna, rolling
05:49
Very important argument: axis=0 or axis=1
03:27
Quiz Time!
1 question
nlargest and nsmallest
02:07
Bringing together Dataframes: Concat
03:52
Combining Time Series and OHLC in general
04:54
Resampling Data
03:19
Resampling OHLC Data
01:51
Plotting in Pandas
03:35
Iterating over a dataframe: Iterrows
05:44
Performance Comparison: Iterrows vs. Vectorization
02:39
Return calculation deep dive
11:20
Your turn!
1 question
Practice Task: Plot the yearly returns of the S&P500
00:28
Solution to the Practice Task: Plot yearly returns of the S&P500
02:42

Portfolio Analysis and Portfolio Management with Python

7 lectures
Portfolio Analysis Introduction
00:47
Variance, Standarddeviation, Covariance and Correlation
04:54
Portfolio Return and Risk
02:48
Portfolio Expected Return and Portfolio Risk using Python
03:53
Use the Dot Product to calculate Portfolio Return and Portfolio Risk
07:34
Application to real data: Portfolio of Microsoft, Coca Cola and Tesla
10:36
Efficient Frontier, Minimum Variance Portfolio and dominant Portfolios
13:17

Introduction to Backtesting Trading Strategies

3 lectures
Introduction and the Strategy
01:19
Coding the Trading Strategy (iterative approach)
18:09
Vectorizing the Backtest
12:10

Project I: Momentum Trading Strategies

3 lectures
Cross-sectional Momentum Part I: Survivorship Bias Handling
08:19
Cross-sectional Momentum Part II: Constructing and Backtesting
11:01
Time-Series Momentum
14:26

Project II: Backtesting JPMorgans Volatility Index (VIX) based Strategy

1 lectures
Backtesting JPMorgans Volatility Index (VIX) based Strategy
18:04

Project III: Stock Market Analysis Interactive Dashboards with Streamlit

3 lectures
Brief Intro to Streamlit
18:09
Streamlit Portfolio Analysis Dashboard
11:11
Streamlit Dashboard showing the Top and Worst S&P500 Index performers
19:36

Project IV: Machine Learning applied on Stock Data

2 lectures
A Machine Learning Model which (potentially) outperformed the S&P500
28:08
Least Squares Moving Average Trading Strategy
25:00

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

3 lectures
Iterative Approach
19:08
Vectorized Approach
23:10
Results Analysis
12:49

Project VI: Optimizing a Portfolio based on the Sharpe Ratio

2 lectures
Recap on Matrix Operations (Expected return and Portfolio Risk)
19:22
Optimization of Portfolio weights
24:18

Extra Chapter: Pandas & SQL

5 lectures
The mighty Intersection between Pandas and SQL
04:12
How to update an SQL Database with Pandas and SQL
16:52
Build your own Finance DB using Pandas & SQL!
14:01
Build a simple Stock recommendation System with your Finance DB
12:40
Build an Intraday Stock Price Database with Python and SQL
11:46

What I would like to give you on your way! Thank you :-)

1 lectures
Thank you and something to take along!
02:39

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