Python For Finance And Data Science

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

Requirements

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

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, 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 professionalMy 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 ProgrammingAn Introduction to one of the most powerful Data Science and Financial Data Analysis Libraries: PandasA FULL guide into applied Financial Data AnalysisA FULL guide into Portfolio Analysis and Portfolio Management with Python on real stock dataYou will learn to quantitatively analyze you own portfolio and give it a reality check! :-)An Introduction to Backtesting Trading Strategies and VectorizationOptimizing a Portfolio using state of the art toolsAdvanced Trading Strategies using concepts of Optimization and Machine LearningBuilding state of the art and beautiful Interactive Finance DashboardLearn about the powerful Intersection of Pandas & SQL and use it to leverage your knowledgeWhy 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 possibleOutstanding 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 :-)

Overview

Section 1: Introduction

Lecture 1 What does this course cover?

Lecture 2 Disclaimer [MUST WATCH!]

Lecture 3 How to get the most of this course?

Lecture 4 Any questions or problems? Reach out!

Section 2: Installation and Jupyter Notebook Basics

Lecture 5 Download Anaconda & Set Up Jupyter Notebook

Lecture 6 Jupyter Notebook Basics

Section 3: Python Fundamentals

Lecture 7 Variables & Single Datatypes

Lecture 8 What you should NEVER do

Lecture 9 Typecasting & User Input

Lecture 10 Practice Time :-)

Lecture 11 Arithmetic Operators

Lecture 12 Comparison Operators / Logical Operators

Lecture 13 Indentations & If-Statements

Lecture 14 Practice Time :-)

Lecture 15 Lists as objects with methods in Python

Lecture 16 List Slicing & Indexing

Lecture 17 Difference between lists & tuples

Lecture 18 Dictionaries

Lecture 19 For loops

Lecture 20 Combining lists & loops: List comprehension

Lecture 21 While loop

Lecture 22 Practice Time :-)

Lecture 23 Practice your knowledge with a common Interview question!

Lecture 24 Functions

Section 4: Fundamentals of Pandas

Lecture 25 Setting up a DataFrame and DataFrame properties

Lecture 26 Adding columns and using dictionaries for DataFrame initialization

Lecture 27 New columns based on calculations

Lecture 28 Data Selection with iloc

Lecture 29 Data Selection with loc

Lecture 30 Data Filtering with Boolean Masks and Boolean Indexing

Section 5: Applied Financial Data Analysis

Lecture 31 Pulling stock prices and OHLC data

Lecture 32 Quick Recap on what we did in the last chapter

Lecture 33 Return calculation with shift and pct_change

Lecture 34 Important functions: diff, dropna, rolling

Lecture 35 Very important argument: axis=0 or axis=1

Lecture 36 nlargest and nsmallest

Lecture 37 Bringing together Dataframes: Concat

Lecture 38 Combining Time Series and OHLC in general

Lecture 39 Resampling Data

Lecture 40 Resampling OHLC Data

Lecture 41 Plotting in Pandas

Lecture 42 Iterating over a dataframe: Iterrows

Lecture 43 Performance Comparison: Iterrows vs. Vectorization

Lecture 44 Return calculation deep dive

Lecture 45 Practice Task: Plot the yearly returns of the S&P500

Lecture 46 Solution to the Practice Task: Plot yearly returns of the S&P500

Section 6: Portfolio Analysis and Portfolio Management with Python

Lecture 47 Portfolio Analysis Introduction

Lecture 48 Variance, Standarddeviation, Covariance and Correlation

Lecture 49 Portfolio Return and Risk

Lecture 50 Portfolio Expected Return and Portfolio Risk using Python

Lecture 51 Use the Dot Product to calculate Portfolio Return and Portfolio Risk

Lecture 52 Application to real data: Portfolio of Microsoft, Coca Cola and Tesla

Lecture 53 Efficient Frontier, Minimum Variance Portfolio and dominant Portfolios

Section 7: Introduction to Backtesting Trading Strategies

Lecture 54 Introduction and the Strategy

Lecture 55 Coding the Trading Strategy (iterative approach)

Lecture 56 Vectorizing the Backtest

Section 8: Project I: Momentum Trading Strategies

Lecture 57 Cross-sectional Momentum Part I: Survivorship Bias Handling

Lecture 58 Cross-sectional Momentum Part II: Constructing and Backtesting

Lecture 59 Time-Series Momentum

Section 9: Project II: Backtesting JPMorgans Volatility Index (VIX) based Strategy

Lecture 60 Backtesting JPMorgans Volatility Index (VIX) based Strategy

Section 10: Project III: Stock Market Analysis Interactive Dashboards with Streamlit

Lecture 61 Brief Intro to Streamlit

Lecture 62 Streamlit Portfolio Analysis Dashboard

Lecture 63 Streamlit Dashboard showing the Top and Worst S&P500 Index performers

Section 11: Project IV: Machine Learning applied on Stock Data

Lecture 64 A Machine Learning Model which (potentially) outperformed the S&P500

Lecture 65 Least Squares Moving Average Trading Strategy

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

Lecture 66 Iterative Approach

Lecture 67 Vectorized Approach

Lecture 68 Results Analysis

Section 13: Project VI: Optimizing a Portfolio based on the Sharpe Ratio

Lecture 69 Recap on Matrix Operations (Expected return and Portfolio Risk)

Lecture 70 Optimization of Portfolio weights

Section 14: Extra Chapter: Pandas & SQL

Lecture 71 The mighty Intersection between Pandas and SQL

Lecture 72 How to update an SQL Database with Pandas and SQL

Lecture 73 Build your own Finance DB using Pandas & SQL!

Lecture 74 Build a simple Stock recommendation System with your Finance DB

Lecture 75 Build an Intraday Stock Price Database with Python and SQL

Section 15: What I would like to give you on your way! Thank you :-)

Lecture 76 Thank you and something to take along!

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