面向金融和数据科学的Python

学习Python编程并将金融数据科学应用于真实数据——从初学者到专业人士，你准备好彻底改变你对金融和数据科学的理解了吗？深入金融和数据科学的Python世界，在这里，尖端技术与金融分析的动态领域相遇。在这个全面的课程中，我将指导你通过基本原则和实用技术，将增压你的财务分析技能。无论你是一名有抱负的金融专业人士、数据科学家、以量化为导向的人，还是仅仅渴望扩展你的知识，本课程都将使你能够从金融数据中提取有价值的见解，并做出明智的决策。利用Python的强大功能，Python是行业领先的数据分析和自动化编程语言。探索金融数据检索、预处理、操作的复杂性，并获得将原始数据转换为引人注目的可视化和直观仪表板的工具。了解如何使用Python实现投资组合分析和投资组合优化技术。发现数据中隐藏的模式，建立和回溯测试交易策略，探索算法交易的可能性。但不止于此！本课程融入了基本的数据科学概念，超越了金融范畴。您将掌握数据处理、投资组合分析、应用金融分析、回溯测试的艺术，并发现关键的商业洞察力。准备好动手练习、真实世界的例子和积极工作的量化金融专业人士的专家指导我的引人入胜的课程确保了无缝的学习体验，因为我为您提供了在快节奏的金融和数据科学世界中脱颖而出的技能。不要错过这个转变职业生涯并在金融或数据行业获得竞争优势的机会。立即注册，释放Python在金融和数据科学领域的全部潜力！具体会学到什么？基础Python编程介绍最强大的数据科学和金融数据分析库之一:PandasA应用金融数据分析完全指南使用Python对真实股票数据进行投资组合分析和投资组合管理的完全指南您将学习定量分析您自己的投资组合，并对其进行真实性检查！:-)回溯测试交易策略和矢量化介绍使用最先进的工具优化投资组合使用优化和机器学习概念的高级交易策略构建最先进和漂亮的交互式金融仪表板了解Pandas和SQL的强大交集，并使用它来利用您的知识为什么选择本课程而不是其他课程？几年来，我一直活跃在量化金融领域，涵盖数据科学和量化金融主题，并撰写了我的量化金融硕士论文——我知道什么与实践相关，但也知道什么与提升水平相关！我在YouTube上给超过75，000人讲授过Python的金融和自动交易主题，私下里也教过无数人。你会得到很多测验，练习来应用我教的东西，我会给你相关的提示和实用的建议。我挑战你解决所有提供的练习！:-)本课程没有单次填充。我们直接进入主题，我尽可能简短，但也尽可能花时间具体说明。杰出支持:如果你不明白某些事情，你觉得你卡住了，或者你只是想与我联系，请给我写消息，我会尽快回复你！你还在等什么？单击“立即注册”开始！我很兴奋，期待在课程中见到你

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言:英语|大小:3.15 GB 含课程文件 |时长:8小时50分钟

你会学到什么
学习如何从头开始用Python编码
成为特定财务数据的数据分析专家
用Python构建和回溯测试交易策略
了解并优化您投资组合的回报和风险状况
比较股票和投资组合的夏普比率
拥有出色的技术技能，可以申请金融机构或数据公司的量化工作
能够进行深入的投资分析
使用Python解决现实世界的问题
在交互式仪表板中可视化您的数据
了解使用财务数据的最佳实践和相关实践建议
能够比较股票
理解日志返回和返回之间的区别
通过使用有效边界的概念优化权重
利用代数概念进行强大的计算
学习使用Pandas和SQL的强大交叉来构建、维护和利用数据库
了解如何利用代数进行强大的计算

要求
不需要编程经验。我们从零开始。
对股市有一个基本的了解会有所帮助，但这不是强制性的

本课适用于寻找机会获得高需求技能组合的商业和金融学生，对应用金融分析感兴趣的人，希望更好地了解自己投资组合的人，对金融、数据科学和分析感兴趣的人，注重实践的人，希望建立高价值技能组合的人，希望理解投资组合分析背后的统计和代数的人

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