Python for Financial Analysis and Algorithmic Trading
What you’ll learn
Use NumPy to quickly work with Numerical Data
Use Pandas for Analyze and Visualize Data
Use Matplotlib to create custom plots
Learn how to use statsmodels for Time Series Analysis
Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
Use Exponentially Weighted Moving Averages
Use ARIMA models on Time Series Data
Calculate the Sharpe Ratio
Optimize Portfolio Allocations
Understand the Capital Asset Pricing Model
Learn about the Efficient Market Hypothesis
Conduct algorithmic Trading on Quantopian
Requirements
Some knowledge of programming (preferably Python)
Ability to Download Anaconda (Python) to your computer
Basic Statistics and Linear Algebra will be helpful
Description
Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you!
This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We’ll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!
We’ll cover the following topics used by financial professionals:
Python FundamentalsNumPy for High Speed Numerical ProcessingPandas for Efficient Data AnalysisMatplotlib for Data VisualizationUsing pandas-datareader and Quandl for data ingestionPandas Time Series Analysis TechniquesStock Returns AnalysisCumulative Daily ReturnsVolatility and Securities RiskEWMA (Exponentially Weighted Moving Average)StatsmodelsETS (Error-Trend-Seasonality)ARIMA (Auto-regressive Integrated Moving Averages)Auto Correlation Plots and Partial Auto Correlation PlotsSharpe RatioPortfolio Allocation Optimization Efficient Frontier and Markowitz OptimizationTypes of FundsOrder BooksShort SellingCapital Asset Pricing ModelStock Splits and DividendsEfficient Market HypothesisAlgorithmic Trading with QuantopianFutures Trading
Overview
Section 1: Course Introduction
Lecture 1 Introduction to Course
Lecture 2 Course Overview Lecture (DON’T SKIP THIS!)
Lecture 3 Did you skip the last lecture? Please go back and view it!
Lecture 4 Course FAQ
Section 2: Course Materials and Set-up
Lecture 5 Note on yml File
Lecture 6 Course Installation Guide
Section 3: Python Crash Course
Lecture 7 Welcome to the Python Crash Course
Lecture 8 Introduction to Crash Course
Lecture 9 Python Crash Course Part One
Lecture 10 Python Crash Course Part Two
Lecture 11 Python Crash Course Part Three
Lecture 12 Python Crash Course Exercises
Lecture 13 Python Crash Course Exercise Solutions
Section 4: NumPy
Lecture 14 Welcome to NumPy
Lecture 15 Introduction to NumPy
Lecture 16 NumPy Arrays
Lecture 17 Numpy Operations
Lecture 18 Numpy Indexing
Lecture 19 NumPy Review Exercise
Lecture 20 Numpy Exercise Solutions
Section 5: General Pandas Overview
Lecture 21 Welcome to Pandas
Lecture 22 Introduction to Pandas
Lecture 23 Series
Lecture 24 DataFrames
Lecture 25 DataFrames Part Two
Lecture 26 DataFrames Part Three
Lecture 27 Missing Data
Lecture 28 Group By with Pandas
Lecture 29 Merging, Joining, and Concatenating DataFrames
Lecture 30 Pandas Common Operations
Lecture 31 Data Input and Output
Lecture 32 General Pandas Review Exercises
Lecture 33 General Pandas Exercise Solutions
Section 6: Visualization with Matplotlib and Pandas
Lecture 34 Welcome to Visualization
Lecture 35 Introduction to Visualization in Python
Lecture 36 Matplotlib Basics – Part One
Lecture 37 Matplotlib Basics – Part Two
Lecture 38 Matplotlib Part Three
Lecture 39 Matplotlib Exercise
Lecture 40 Matplotlib Exercise Solutions
Lecture 41 Pandas Visualization Overview
Lecture 42 Pandas Time Series Visualization
Lecture 43 Pandas Visualization Exercise Overview
Lecture 44 Pandas Visualization Exercise Solutions
Section 7: Data Sources
Lecture 45 Introduction to Data Sources
Lecture 46 Note on Pandas Datareader
Lecture 47 Pandas DataReader
Lecture 48 Quandl
Section 8: Pandas with Time Series Data
Lecture 49 Welcome to Pandas for Time Series
Lecture 50 Introduction to Time Series with Pandas
Lecture 51 Datetime Index
Lecture 52 Time Resampling
Lecture 53 Time Shifts
Lecture 54 Pandas Rolling and Expanding
Section 9: Capstone Stock Market Analysis Project
Lecture 55 Welcome to the Capstone Project!
Lecture 56 Stock Market Analysis Project
Lecture 57 Stock Market Analysis Project Solutions Part One
Lecture 58 Python Stock Market Analysis Solutions – Part Two
Lecture 59 Stock Market Analysis Project Solutions Part Three
Lecture 60 Stock Market Analysis Project Solutions Part Four
Section 10: Time Series Analysis
Lecture 61 Welcome to Time Series Analysis
Lecture 62 Introduction to Time Series
Lecture 63 Time Series Basics
Lecture 64 Introduction to Statsmodels
Lecture 65 ETS Theory
Lecture 66 EWMA Theory
Lecture 67 EWMA Code Along
Lecture 68 ETS Code Along
Lecture 69 ARIMA Theory
Lecture 70 ACF and PACF
Lecture 71 ARIMA with Statsmodels
Lecture 72 Quick Note on Second Milk Difference!
Lecture 73 ARIMA Code Part Two
Lecture 74 ARIMA Code Part Three
Lecture 75 ARIMA Code Part Four
Lecture 76 Discussion on choosing PDQ
Section 11: Python Finance Fundamentals
Lecture 77 Welcome to Finance Fundamentals
Lecture 78 Introduction to Python Finance Fundamentals
Lecture 79 Sharpe Ratio Slides
Lecture 80 Portfolio Allocation Code Along Part One
Lecture 81 Portfolio Allocation Code Along Part Two
Lecture 82 Portfolio Optimization
Lecture 83 Portfolio Optimization Code Along One
Lecture 84 Portfolio Optimization Code Along Two
Lecture 85 Portfolio Optimization Code Along Three
Lecture 86 Key Financial Topics
Lecture 87 Types of Funds
Lecture 88 Order Books
Lecture 89 Short Selling
Lecture 90 CAPM – Capital Asset Pricing Model
Lecture 91 CAPM Code Along
Lecture 92 Stock Splits and Dividends
Lecture 93 EMH
Section 12: Basics of Algorithmic Trading with Quantopian and Zipline
Lecture 94 Note on Quantopian and Zipline
Lecture 95 Welcome to the Quantopian Section
Lecture 96 Introduction to Quantopian
Lecture 97 Quantopian Algorithms Basics Part One
Lecture 98 Quantopian Algorithms Basics Part Two
Lecture 99 First Trading Algorithm – Part One
Lecture 100 First Trading Algorithm – Part Two
Lecture 101 Trading Algorithm Exercise
Lecture 102 Trading Algorithm Exercise Solutions Part One
Lecture 103 Trading Algorithm Exercise Solutions Part Two
Lecture 104 Quantopian Pipelines Factors
Lecture 105 Quantopian Pipelines Filters
Lecture 106 Quantopian Pipeline – Masking and Classifiers
Section 13: Advanced Quantopian and Trading Algorithms
Lecture 107 Welcome to Trading Algorithms
Lecture 108 Pipeline Trading Algorithm Example – Code Along – Part One
Lecture 109 Pipeline Trading Algorithm – Code Along – Part Two
Lecture 110 Quick note
Lecture 111 Pipeline Trading Algorithm Code along Part Three
Lecture 112 Leverage
Lecture 113 Hedging
Lecture 114 Hedging- Part Two
Lecture 115 Portfolio Analysis with PyFolio
Lecture 116 Stock Sentiment Analysis Project
Lecture 117 What are Futures?
Lecture 118 Futures on Quantopian
Lecture 119 Futures on Quantopian Part Two
Section 14: BONUS SECTION: THANK YOU!
Lecture 120 Bonus Lecture:
Someone familiar with Python who wants to learn about Financial Analysis!
Course Information:
Udemy | English | 16h 39m | 6.19 GB
Created by: Jose Portilla
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