Python for Financial Analysis and Algorithmic Trading
16h 54m 20s
English
Paid
Course 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!
Read more about the course
We'll cover the following topics used by financial professionals:
- Python Fundamentals
- NumPy for High Speed Numerical Processing
- Pandas for Efficient Data Analysis
- Matplotlib for Data Visualization
- Using pandas-datareader and Quandl for data ingestion
- Pandas Time Series Analysis Techniques
- Stock Returns Analysis
- Cumulative Daily Returns
- Volatility and Securities Risk
- EWMA (Exponentially Weighted Moving Average)
- Statsmodels
- ETS (Error-Trend-Seasonality)
- ARIMA (Auto-regressive Integrated Moving Averages)
- Auto Correlation Plots and Partial Auto Correlation Plots
- Sharpe Ratio
- Portfolio Allocation Optimization
- Efficient Frontier and Markowitz Optimization
- Types of Funds
- Order Books
- Short Selling
- Capital Asset Pricing Model
- Stock Splits and Dividends
- Efficient Market Hypothesis
- Algorithmic Trading with Quantopian
- Futures Trading
Requirements:
- Some knowledge of programming (preferably Python)
- Ability to Download Anaconda (Python) to your computer
- Basic Statistics and Linear Algebra will be helpful
Who this course is for:
- Someone familiar with Python who wants to learn about Financial Analysis!
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
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| # | Title | Duration |
|---|---|---|
| 1 | Introduction to Course | 02:13 |
| 2 | Course Overview Lecture (DON'T SKIP THIS!) | 03:33 |
| 3 | Course Installation Guide | 08:49 |
| 4 | Welcome to the Python Crash Course | 00:20 |
| 5 | Introduction to Crash Course | 01:17 |
| 6 | Python Crash Course Part One | 19:01 |
| 7 | Python Crash Course Part Two | 13:38 |
| 8 | Python Crash Course Part Three | 15:03 |
| 9 | Python Crash Course Exercises | 04:14 |
| 10 | Python Crash Course Exercise Solutions | 09:07 |
| 11 | Welcome to NumPy | 00:24 |
| 12 | Introduction to NumPy | 01:38 |
| 13 | NumPy Arrays | 15:49 |
| 14 | Numpy Operations | 04:20 |
| 15 | Numpy Indexing | 10:55 |
| 16 | NumPy Review Exercise | 04:11 |
| 17 | Numpy Exercise Solutions | 09:53 |
| 18 | Welcome to Pandas | 00:23 |
| 19 | Introduction to Pandas | 02:40 |
| 20 | Series | 06:59 |
| 21 | DataFrames | 15:35 |
| 22 | DataFrames Part Two | 17:00 |
| 23 | DataFrames Part Three | 09:03 |
| 24 | Missing Data | 06:15 |
| 25 | Group By with Pandas | 06:38 |
| 26 | Merging, Joining, and Concatenating DataFrames | 09:11 |
| 27 | Pandas Common Operations | 12:13 |
| 28 | Data Input and Output | 13:51 |
| 29 | General Pandas Review Exercises | 03:07 |
| 30 | General Pandas Exercise Solutions | 12:54 |
| 31 | Welcome to Visualization | 00:24 |
| 32 | Introduction to Visualization in Python | 01:49 |
| 33 | Matplotlib Basics - Part One | 18:46 |
| 34 | Matplotlib Basics - Part Two | 15:32 |
| 35 | Matplotlib Part Three | 11:44 |
| 36 | Matplotlib Exercise | 03:43 |
| 37 | Matplotlib Exercise Solutions | 10:09 |
| 38 | Pandas Visualization Overview | 12:08 |
| 39 | Pandas Time Series Visualization | 17:33 |
| 40 | Pandas Visualization Exercise Overview | 01:19 |
| 41 | Pandas Visualization Exercise Solutions | 08:52 |
| 42 | Introduction to Data Sources | 01:22 |
| 43 | Pandas DataReader | 04:38 |
| 44 | Quandl | 10:22 |
| 45 | Welcome to Pandas for Time Series | 00:14 |
| 46 | Introduction to Time Series with Pandas | 00:59 |
| 47 | Datetime Index | 09:40 |
| 48 | Time Resampling | 12:49 |
| 49 | Time Shifts | 05:59 |
| 50 | Pandas Rolling and Expanding | 17:54 |
| 51 | Welcome to the Capstone Project! | 00:31 |
| 52 | Stock Market Analysis Project | 06:39 |
| 53 | Stock Market Analysis Project Solutions Part One | 20:26 |
| 54 | Python Stock Market Analysis Solutions - Part Two | 09:37 |
| 55 | Stock Market Analysis Project Solutions Part Three | 16:54 |
| 56 | Stock Market Analysis Project Solutions Part Four | 08:24 |
| 57 | Welcome to Time Series Analysis | 00:35 |
| 58 | Introduction to Time Series | 02:52 |
| 59 | Time Series Basics | 04:00 |
| 60 | Introduction to Statsmodels | 12:30 |
| 61 | ETS Theory | 04:17 |
| 62 | EWMA Theory | 02:50 |
| 63 | EWMA Code Along | 14:25 |
| 64 | ETS Code Along | 06:25 |
| 65 | ARIMA Theory | 09:34 |
| 66 | ACF and PACF | 06:21 |
| 67 | ARIMA with Statsmodels | 11:43 |
| 68 | ARIMA Code Part Two | 14:00 |
| 69 | ARIMA Code Part Three | 06:50 |
| 70 | ARIMA Code Part Four | 14:15 |
| 71 | Welcome to Finance Fundamentals | 00:37 |
| 72 | Introduction to Python Finance Fundamentals | 00:50 |
| 73 | Sharpe Ratio Slides | 07:17 |
| 74 | Portfolio Allocation Code Along Part One | 15:32 |
| 75 | Portfolio Allocation Code Along Part Two | 06:45 |
| 76 | Portfolio Optimization | 05:15 |
| 77 | Portfolio Optimization Code Along One | 14:45 |
| 78 | Portfolio Optimization Code Along Two | 07:47 |
| 79 | Portfolio Optimization Code Along Three | 16:33 |
| 80 | Key Financial Topics | 01:08 |
| 81 | Types of Funds | 06:10 |
| 82 | Order Books | 14:36 |
| 83 | Short Selling | 02:36 |
| 84 | CAPM - Capital Asset Pricing Model | 05:20 |
| 85 | CAPM Code Along | 12:11 |
| 86 | Stock Splits and Dividends | 03:17 |
| 87 | EMH | 02:01 |
| 88 | Welcome to the Quantopian Section | 00:25 |
| 89 | Introduction to Quantopian | 09:28 |
| 90 | Quantopian Research Basics | 16:47 |
| 91 | Quantopian Algorithms Basics Part One | 16:18 |
| 92 | Quantopian Algorithms Basics Part Two | 17:18 |
| 93 | First Trading Algorithm - Part One | 16:48 |
| 94 | First Trading Algorithm - Part Two | 16:45 |
| 95 | Trading Algorithm Exercise | 04:51 |
| 96 | Trading Algorithm Exercise Solutions Part One | 12:37 |
| 97 | Trading Algorithm Exercise Solutions Part Two | 02:39 |
| 98 | Quantopian Pipelines Factors | 17:00 |
| 99 | Quantopian Pipelines Filters | 05:59 |
| 100 | Quantopian Pipeline - Masking and Classifiers | 09:19 |
| 101 | Welcome to Trading Algorithms | 00:49 |
| 102 | Pipeline Trading Algorithm Example - Code Along - Part One | 13:35 |
| 103 | Pipeline Trading Algorithm - Code Along - Part Two | 10:28 |
| 104 | Pipeline Trading Algorithm Code along Part Three | 19:29 |
| 105 | Leverage | 12:49 |
| 106 | Hedging | 14:19 |
| 107 | Hedging- Part Two | 14:55 |
| 108 | Portfolio Analysis with PyFolio | 15:21 |
| 109 | Stock Sentiment Analysis Project | 16:24 |
| 110 | What are Futures? | 09:03 |
| 111 | Futures on Quantopian | 18:21 |
| 112 | Futures on Quantopian Part Two | 20:35 |
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