Python for Financial Analysis and Algorithmic Trading

Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!
Python for Financial Analysis and Algorithmic Trading
File Size :
6.19 GB
Total length :
16h 39m



Jose Portilla


Last update




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

Python for Financial Analysis and Algorithmic Trading


Some knowledge of programming (preferably Python)
Ability to Download Anaconda (Python) to your computer
Basic Statistics and Linear Algebra will be helpful


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


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


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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