Python for Financial Analysis and Algorithmic Trading

Python for Financial Analysis and Algorithmic Trading

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 17 Hours | 2.41 GB

Learn numpy, pandas, matplotlib, quantopian, finance, and more for algorithmic trading with Python!

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 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
Table of Contents

Course Introduction
1 Introduction to Course
2 Course Overview Lecture (DON’T SKIP THIS!)
3 Did you skip the last lecture Please go back and view it
4 Course FAQ

Course Materials and Set-up
5 Course Installation Help Notes
6 Course Installation Guide

Python Crash Course
7 Python Crash Course Exercise Solutions
8 Welcome to the Python Crash Course
9 Introduction to Crash Course
10 Python Crash Course Part One
11 Python Crash Course Part Two
12 Python Crash Course Part Three
13 Python Crash Course Exercises

NumPy
14 Welcome to NumPy
15 Introduction to NumPy
16 NumPy Arrays
17 Numpy Operations
18 Numpy Indexing
19 NumPy Review Exercise
20 Numpy Exercise Solutions

General Pandas Overview
21 Welcome to Pandas
22 Introduction to Pandas
23 Series
24 DataFrames
25 DataFrames Part Two
26 DataFrames Part Three
27 Missing Data
28 Group By with Pandas
29 Merging, Joining, and Concatenating DataFrames
30 Pandas Common Operations
31 Data Input and Output
32 General Pandas Review Exercises
33 General Pandas Exercise Solutions

Visualization with Matplotlib and Pandas
34 Welcome to Visualization
35 Introduction to Visualization in Python
36 Matplotlib Basics – Part One
37 Matplotlib Basics – Part Two
38 Matplotlib Part Three
39 Matplotlib Exercise
40 Matplotlib Exercise Solutions
41 Pandas Visualization Overview
42 Pandas Time Series Visualization
43 Pandas Visualization Exercise Overview
44 Pandas Visualization Exercise Solutions

Data Sources
45 Note on Pandas Datareader
46 Introduction to Data Sources
47 Pandas DataReader
48 Quandl

Pandas with Time Series Data
49 Welcome to Pandas for Time Series
50 Introduction to Time Series with Pandas
51 Datetime Index
52 Time Resampling
53 Time Shifts
54 Pandas Rolling and Expanding

Capstone Stock Market Analysis Project
55 Welcome to the Capstone Project!
56 Stock Market Analysis Project
57 Stock Market Analysis Project Solutions Part One
58 Python Stock Market Analysis Solutions – Part Two
59 Stock Market Analysis Project Solutions Part Three
60 Stock Market Analysis Project Solutions Part Four

Time Series Analysis
61 Quick Note on Second Milk Difference
62 Discussion on choosing PDQ
63 Welcome to Time Series Analysis
64 Introduction to Time Series
65 Time Series Basics
66 Introduction to Statsmodels
67 ETS Theory
68 EWMA Theory
69 EWMA Code Along
70 ETS Code Along
71 ARIMA Theory
72 ACF and PACF
73 ARIMA with Statsmodels
74 ARIMA Code Part Two
75 ARIMA Code Part Three
76 ARIMA Code Part Four

Python Finance Fundamentals
77 Welcome to Finance Fundamentals
78 Introduction to Python Finance Fundamentals
79 Sharpe Ratio Slides
80 Portfolio Allocation Code Along Part One
81 Portfolio Allocation Code Along Part Two
82 Portfolio Optimization
83 Portfolio Optimization Code Along One
84 Portfolio Optimization Code Along Two
85 Portfolio Optimization Code Along Three
86 Key Financial Topics
87 Types of Funds
88 Order Books
89 Short Selling
90 CAPM – Capital Asset Pricing Model
91 CAPM Code Along
92 Stock Splits and Dividends
93 EMH

Basics of Algorithmic Trading with Quantopian
94 Note on get fundamentals
95 Quantopian Pipeline – Masking and Classifiers
96 Welcome to the Quantopian Section
97 Introduction to Quantopian
98 Quantopian Research Basics
99 Quantopian Algorithms Basics Part One
100 Quantopian Algorithms Basics Part Two
101 First Trading Algorithm – Part One
102 First Trading Algorithm – Part Two
103 Trading Algorithm Exercise
104 Trading Algorithm Exercise Solutions Part One
105 Trading Algorithm Exercise Solutions Part Two
106 Quantopian Pipelines Factors
107 Quantopian Pipelines Filters

Advanced Quantopian and Trading Algorithms
108 Quick note
109 Welcome to Trading Algorithms
110 Pipeline Trading Algorithm Example – Code Along – Part One
111 Pipeline Trading Algorithm – Code Along – Part Two
112 Pipeline Trading Algorithm Code along Part Three
113 Leverage
114 Hedging
115 Hedging- Part Two
116 Portfolio Analysis with PyFolio
117 Stock Sentiment Analysis Project
118 What are Futures
119 Futures on Quantopian
120 Futures on Quantopian Part Two

BONUS OFFERS
121 Bonus Lecture Coupons