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    • Python Financial Analysis and Algorithmic Trading

    Development May 18, 2020
    Python Financial Analysis and Algorithmic Trading

    Python Financial Analysis and Algorithmic Trading:

    This study covers Python financial analysis and algorithmic trading. You will study Python financial analysis by practicing NumPy, Matplotlib, Pandas, Finance, Quantopian, and much more for algorithmic trading with Python. This study will conduct you through everything you need to know to use Python for finance and algorithmic trading. We will start by mastering the fundamentals of Python, and then advance to learn about the numerous core libraries used in the Py-Finance Ecosystem, including Pandas, NumPy, Jupyter, Matplotlib, Quantopian, Zipline, Statsmodels, and much more. Are you fascinated by how people use Python to administer meticulous business analysis and persevere algorithmic speculation, then this is the right course is for you.

    NumPy Features:

    NumPy is the elemental bundle for scientific computing with Python, a library for the Python, combining support for large, multi-dimensional arrays and matrices, with a comprehensive collection of high-level arithmetical functions to operate on certain arrays.

    • A compelling N-dimensional array object
    • Tools for integrating C/C++ and Fortran code
    • Sophisticated (broadcasting) functions
    • Helpful linear algebra, Fourier transform, & random number capabilities

    Matplotlib Features:

    Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python and its digital arithmetic extension NumPy. It renders an object-oriented API for embedding plots into applications utilizing general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. Matplotlib makes easy things easy and hard things possible.

    • Create: Develop publication-quality plots with just a few lines of code, use interactive figures that can zoom, pan, update.
    • Customize: Take full control of line styles, font properties, axes attributes, export, and embed to a number of file formats and interactive environments.
    • Extend: Explore tailored functionality provided by third-party packages, learn more about Matplotlib through the various external education resources.

    What you’ll learn:

    • Exercise ARIMA models on time series statistics
    • Use NumPy to promptly work with numerical data
    • Calculate financial statistics, such as daily returns, volatility, cumulative returns
    • Use Pandas to interpret and visualize data
    • Practice exponentially weighted starting averages
    • Learn how to use Statsmodels for time series analysis
    • Determine the Sharpe ratio
    • Optimize portfolio allocations
    • Use Matplotlib to generate custom plots
    • Learn the capital asset pricing model
    • Conduct algorithmic exchanging on Quantopian
    • Study about the effective business hypothesis

    Requirements:

    • Basic understanding of Python programming language
    • Fundamental Statistics and Linear Algebra will be applicable
    • Ability to download Anaconda (Python) to your computer

    We will incorporate the subsequent topics adopted by financial specialists:

    • Python fundamentals
    • ARIMA (Auto-Regressive Integrated Moving Averages)
    • EWMA (Exponentially Weighted Moving Average)
    • ETS (Error-Trend-Seasonality)
    • NumPy for high-speed digital processing
    • Pandas for effective data analysis
    • Matplotlib for data visualization
    • Using Pandas-DataReader & Quandl for data ingestion
    • Autocorrelation plots and biased autocorrelation plots
    • Pandas time series analysis procedures
    • Algorithmic exchanging with Quantopian
    • Cumulative daily returns
    • Stock returns analysis
    • Volatility & protection risk
    • Efficient frontier & Markowitz optimization
    • Portfolio allocation optimization
    • Sharpe ratio
    • Statsmodels
    • Types of funds
    • Order books
    • Short selling
    • The capital asset pricing model
    • Stock splits & returns
    • Efficient business philosophy
    • Futures speculation

    Who this study is for:

    • Python developers who want to study about Financial Analysis

    Author: Jose Portilla
    Language: English
    Size: 2.44GB

    Study Topics:

    • 01 Course Introduction
      1. 1 1.01 Introduction to Course
      2. 2 1.02 Course Overview Lecture (DON'T SKIP THIS!)
    • 02 Course Materials and Set-up
      1. 1 2.01 Course Installation Guide
    • 03 Python Crash Course
      1. 1 3.01 Welcome to the Python Crash Course
      2. 2 3.02 Introduction to Crash Course
      3. 3 3.03 Python Crash Course Part One
      4. 4 3.04 Python Crash Course Part Two
      5. 5 3.05 Python Crash Course Part Three
      6. 6 3.06 Python Crash Course Exercises
      7. 7 3.07 Python Crash Course Exercise Solutions
    • 04 NumPy
      1. 1 4.01 Welcome to NumPy
      2. 2 4.02 Introduction to NumPy
      3. 3 4.03 NumPy Arrays
      4. 4 4.04 Numpy Operations
      5. 5 4.05 Numpy Indexing
      6. 6 4.06 NumPy Review Exercise
      7. 7 4.07 Numpy Exercise Solutions
    • 05 General Pandas Overview
      1. 1 5.01 Welcome to Pandas
      2. 2 5.02 Introduction to Pandas
      3. 3 5.03 Series
      4. 4 5.04 DataFrames
      5. 5 5.05 DataFrames Part Two
      6. 6 5.06 DataFrames Part Three
      7. 7 5.07 Missing Data
      8. 8 5.08 Group By with Pandas
      9. 9 5.09 Merging, Joining, and Concatenating DataFrames
      10. 10 5.10 Pandas Common Operations
      11. 11 5.11 Data Input and Output
      12. 12 5.12 General Pandas Review Exercises
      13. 13 5.13 General Pandas Exercise Solutions
    • 06 Visualization with Matplotlib and Pandas
      1. 1 6.01 Welcome to Visualization
      2. 2 6.02 Introduction to Visualization in Python
      3. 3 6.03 Matplotlib Basics - Part One
      4. 4 6.04 Matplotlib Basics - Part Two
      5. 5 6.05 Matplotlib Part Three
      6. 6 6.06 Matplotlib Exercise
      7. 7 6.07 Matplotlib Exercise Solutions
      8. 8 6.08 Pandas Visualization Overview
      9. 9 6.09 Pandas Time Series Visualization
      10. 10 6.10 Pandas Visualization Exercise Overview
      11. 11 6.11 Pandas Visualization Exercise Solutions
    • 07 Data Sources
      1. 1 7.01 Introduction to Data Sources
      2. 2 7.02 Pandas DataReader
      3. 3 7.03 Quandl
    • 08 Pandas with Time Series Data
      1. 1 8.01 Welcome to Pandas for Time Series
      2. 2 8.02 Introduction to Time Series with Pandas
      3. 3 8.03 Datetime Index
      4. 4 8.04 Time Resampling
      5. 5 8.05 Time Shifts
      6. 6 8.06 Pandas Rolling and Expanding
    • 09 Capstone Stock Market Analysis Project
      1. 1 9.01 Welcome to the Capstone Project!
      2. 2 9.02 Stock Market Analysis Project
      3. 3 9.03 Stock Market Analysis Project Solutions Part One
      4. 4 9.04 Python Stock Market Analysis Solutions - Part Two
      5. 5 9.05 Stock Market Analysis Project Solutions Part Three
      6. 6 9.06 Stock Market Analysis Project Solutions Part Four
    • 10 Time Series Analysis
      1. 1 10.01 Welcome to Time Series Analysis
      2. 2 10.02 Introduction to Time Series
      3. 3 10.03 Time Series Basics
      4. 4 10.04 Introduction to Statsmodels
      5. 5 10.05 ETS Theory
      6. 6 10.06 EWMA Theory
      7. 7 10.07 EWMA Code Along
      8. 8 10.08 ETS Code Along
      9. 9 10.09 ARIMA Theory
      10. 10 10.10 ACF and PACF
      11. 11 10.11 ARIMA with Statsmodels
      12. 12 10.12 ARIMA Code Part Two
      13. 13 10.13 ARIMA Code Part Three
      14. 14 10.14 ARIMA Code Part Four
    • 11 Python Finance Fundamentals
      1. 1 11.01 Welcome to Finance Fundamentals
      2. 2 11.02 Introduction to Python Finance Fundamentals
      3. 3 11.03 Sharpe Ratio Slides
      4. 4 11.04 Portfolio Allocation Code Along Part One
      5. 5 11.05 Portfolio Allocation Code Along Part Two
      6. 6 11.06 Portfolio Optimization
      7. 7 11.07 Portfolio Optimization Code Along One
      8. 8 11.08 Portfolio Optimization Code Along Two
      9. 9 11.09 Portfolio Optimization Code Along Three
      10. 10 11.10 Key Financial Topics
      11. 11 11.11 Types of Funds
      12. 12 11.12 Order Books
      13. 13 11.13 Short Selling
      14. 14 11.14 CAPM - Capital Asset Pricing Model
      15. 15 11.15 CAPM Code Along
      16. 16 11.16 Stock Splits and Dividends
      17. 17 11.17 EMH
    • 12 Basics of Algorithmic Trading with Quantopian
      1. 1 12.01 Welcome to the Quantopian Section
      2. 2 12.02 Introduction to Quantopian
      3. 3 12.03 Quantopian Research Basics
      4. 4 12.04 Quantopian Algorithms Basics Part One
      5. 5 12.05 Quantopian Algorithms Basics Part Two
      6. 6 12.06 First Trading Algorithm - Part One
      7. 7 12.07 First Trading Algorithm - Part Two
      8. 8 12.08 Trading Algorithm Exercise
      9. 9 12.09 Trading Algorithm Exercise Solutions Part One
      10. 10 12.10 Trading Algorithm Exercise Solutions Part Two
      11. 11 12.11 Quantopian Pipelines Factors
      12. 12 12.12 Quantopian Pipelines Filters
      13. 13 12.13 Quantopian Pipeline - Masking and Classifiers
    • 13 Advanced Quantopian and Trading Algorithms
      1. 1 13.01 Welcome to Trading Algorithms
      2. 2 13.02 Pipeline Trading Algorithm Example - Code Along - Part One
      3. 3 13.03 Pipeline Trading Algorithm - Code Along - Part Two
      4. 4 13.04 Pipeline Trading Algorithm Code along Part Three
      5. 5 13.05 Leverage
      6. 6 13.06 Hedging
      7. 7 13.07 Hedging- Part Two
      8. 8 13.08 Portfolio Analysis with PyFolio
      9. 9 13.09 Stock Sentiment Analysis Project
      10. 10 13.10 What are Futures
      11. 11 13.11 Futures on Quantopian
      12. 12 13.12 Futures on Quantopian Part Two

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