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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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