Python for Finance and Data Science

سرفصل های دوره

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

1 - Introduction

1 - What does this course cover

2 - Disclaimer MUST WATCH

3 - How to get the most of this course

4 - Any questions or problems Reach out

2 - Installation and Jupyter Notebook Basics

5 - Download Anaconda Set Up Jupyter Notebook

6 - Jupyter Notebook Basics

3 - Python Fundamentals

7 - Variables Single Datatypes

8 - What you should NEVER do

9 - Typecasting User Input

10 - Practice Time

11 - Arithmetic Operators

12 - Comparison Operators Logical Operators

13 - Indentations IfStatements

14 - Practice Time

15 - Lists as objects with methods in Python

16 - List Slicing Indexing

17 - Difference between lists tuples

18 - Dictionaries

19 - For loops

20 - Combining lists loops List comprehension

21 - While loop

22 - Practice Time

23 - Practice your knowledge with a common Interview question

24 - Functions

4 - Fundamentals of Pandas

25 - Setting up a DataFrame and DataFrame properties

26 - Adding columns and using dictionaries for DataFrame initialization

27 - New columns based on calculations

28 - Data Selection with iloc

29 - Data Selection with loc

30 - Data Filtering with Boolean Masks and Boolean Indexing

5 - Applied Financial Data Analysis

31 - Pulling stock prices and OHLC data

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

33 - Return calculation with shift and pctchange

34 - Important functions diff dropna rolling

35 - Very important argument axis0 or axis1

36 - nlargest and nsmallest

37 - Bringing together Dataframes Concat

38 - Combining Time Series and OHLC in general

39 - Resampling Data

40 - Resampling OHLC Data

41 - Plotting in Pandas

42 - Iterating over a dataframe Iterrows

43 - Performance Comparison Iterrows vs Vectorization

44 - Return calculation deep dive

45 - Practice Task Plot the yearly returns of the SP500

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

6 - Portfolio Analysis and Portfolio Management with Python

47 - Portfolio Analysis Introduction

48 - Variance Standarddeviation Covariance and Correlation

49 - Portfolio Return and Risk

50 - Portfolio Expected Return and Portfolio Risk using Python

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

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

52 - portfolio-analysis.zip

53 - Efficient Frontier Minimum Variance Portfolio and dominant Portfolios

7 - Introduction to Backtesting Trading Strategies

54 - Introduction and the Strategy

55 - Coding the Trading Strategy iterative approach

55 - backtest-iteratively.zip

56 - Vectorizing the Backtest

56 - backtest-vectorized.zip

8 - Project I Momentum Trading Strategies

57 - Crosssectional Momentum Part I Survivorship Bias Handling

58 - Crosssectional Momentum Part II Constructing and Backtesting

58 - momentum-s-p500-full.zip

59 - TimeSeries Momentum

9 - Project II Backtesting JPMorgans Volatility Index VIX based Strategy

60 - Backtesting JPMorgans Volatility Index VIX based Strategy

60 - jpm.zip

10 - Project III Stock Market Analysis Interactive Dashboards with Streamlit

61 - Brief Intro to Streamlit

62 - Streamlit Portfolio Analysis Dashboard

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

63 - equity-index-streamlit.zip

11 - Project IV Machine Learning applied on Stock Data

64 - A Machine Learning Model which potentially outperformed the SP500

64 - ml-logit-strategy.zip

65 - Least Squares Moving Average Trading Strategy

65 - lsma.zip

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

66 - Iterative Approach

66 - backtest-adv-iterative.zip

67 - Vectorized Approach

68 - Results Analysis

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

69 - Recap on Matrix Operations Expected return and Portfolio Risk

70 - Optimization of Portfolio weights

14 - Extra Chapter Pandas SQL

71 - The mighty Intersection between Pandas and SQL

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

73 - Build your own Finance DB using Pandas SQL

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

74 - MACD indicator explained.txt

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

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

76 - Thank you and something to take along

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تولید کننده: Udemy-Training