Python for Financial Analysis and Algorithmic Trading

16h 54m 20s
English
Paid

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

Watch Online Python for Financial Analysis and Algorithmic Trading

Join premium to watch
Go to premium
# 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

Similar courses to Python for Financial Analysis and Algorithmic Trading

Feature Flags: Transform Your Product Development Workflow
Python and Django Full Stack Web Developer Bootcamp

Python and Django Full Stack Web Developer Bootcampudemy

Category: Python, Django
Duration 31 hours 54 minutes 39 seconds
Conduct a Choice-Based Conjoint Analysis for Netflix with Python

Conduct a Choice-Based Conjoint Analysis for Netflix with Pythonzerotomastery.io

Category: Python
Duration 1 hour 39 minutes 35 seconds
Secure APIs with FastAPI and the Microsoft Identity Platform

Secure APIs with FastAPI and the Microsoft Identity PlatformTalkpython

Category: Python
Duration 1 hour 45 minutes 17 seconds
100 MENTAL MODELS & THINK BETTER

100 MENTAL MODELS & THINK BETTERWisdom Theory

Category: Others
Duration 38 hours 58 minutes 27 seconds
Effective PyCharm (2021 edition)

Effective PyCharm (2021 edition)Talkpython

Category: Python, Other (Tools)
Duration 7 hours 30 minutes 43 seconds
Break Into Tech And Become A Software Engineer

Break Into Tech And Become A Software EngineerBrian Jenney

Category: Others
Duration 1 hour 49 minutes 25 seconds
Python Mega Course: Learn Python in 60 Days, Build 20 Apps

Python Mega Course: Learn Python in 60 Days, Build 20 Appsudemy

Category: Python
Duration 51 hours 19 minutes 24 seconds
Django 2.1 & Python | The Ultimate Web Development Bootcamp

Django 2.1 & Python | The Ultimate Web Development Bootcampudemy

Category: Python, MongoDB
Duration 9 hours 52 minutes 1 second