Hands-on Python for Finance

Name: Hands-on Python for Finance
Rating: 4.0 (67 reviews)

The practical guide to using data-driven algorithms in Finance

Created byPackt Publishing

Last updated 3/2019

English

What you'll learn

General programing skills in Python and working with common Python interfaces
Using Numpy, Pandas and matplotlib to manipulate, analyze and visualize data
Understand the Time value of money applications and project selection
Getting and with working data, time series forecasting methods and linear models
Understand Correlation and portfolio construction
Be comfortable with Monte Carlo Simulation, Value at Risk and Options Valuation

Course content

6 sections • 36 lectures • 5h 25m total length

The Course Overview2:58
This video will give you an overview about the course.
Installing the Anaconda Platform3:34
In this video, we walk through installation of Anaconda, a popular scientific Python platform. The software is free to install and use.
• Download installation package
• Install software
• Verify installation
Launching the Python Environment6:47
In this video, we will briefly look at several Python interfaces.
• Demonstrate running programs and Python shell
• Demonstrate ipython, a more advanced interactive shell
• Demonstrate Jupyter Notebook the interface frequently use
String and Number Objects12:45
In this video, introduce basic string functionality with an emphasis on formatting output
• Learn String definition and structure
• Use the format function
• Format output for presentation
Python Lists5:22
In this video, we will talk about one of Python’s advanced data structures, the list.
• Learn about the list basics
• Access elements in a list
• Add, modify and delete elements
Python Dictionaries (Dicts)4:34
In this video, we will talk about one of Python’s advanced data structures, the dict.
• Learn about the structure of a dict, key/value pairs
• Access items in a dict
• Add, modify and delete elements
Repetition in Python (For Loops)8:37
In this video, we will introduce the idea of repletion in programs with the for loop; code blocks are also discussed.
• Learn about the For loop basics and code blocks
• Use the range function
• Combine for loop with formatting output
Branching Logic in Python (If Blocks)8:04
In this video, we will talk about making decisions in your program with the if..elif.. else blocks of code.
• Basic if statement block
• Use If with elif blocks
• Use If elif else full example
Introduction to Functions in Python8:21
In this video, we will talk about one creating reusable code with functions in Python.
• Encapsulatecomplex code in a function
• A simple function example with documentation string
• Access help on any function

Introduction to NumPy Arrays9:24
In this video, we walk through the basics of the NumPy array data structure including creation, assessment and manipulation.
• Create sample arrays
• Determine size dimensions
• Display, manipulate and access data within an array
NumPy – A Deeper Dive9:17
Continuing our introduction to NumPy with special placeholder arrays, mathematical operations and arrays created with random numbers.
• Demonstrate NumPy placeholder arrays
• Understand the examples of vectorized mathematical operations
• Introduce the NumPy random module
Pandas – Part I9:52
Introduction to the Pandas DataFrame data structure and common operations.
• Move from arrays into DataFrames
• Compare the two structures
• Learn about common operations in DataFrames
Pandas – Part II10:02
Let’s dive deeper into the Pandas DataFrame data structure.
• Learn the mathematical operations
• Understand convenience functions
Introduction to Scipy.stats11:38
Overview of common statistical methods available in the Scipy.stats module.
• Walkthrough several examples of statistical operations available
• Create a function for displaying summary statistics
Matplotlib – Part I8:07
Introduction to the fundamental graphing library in Python: Matplotlib.
• Construct basic plots used in data analysis
• Learn basic line plots, scatter plots and histograms
Matplotlib – Part II16:33
In this video, we will see how to add annotations and customize graphs.
• Start with a basic plot
• Add code to control the output
• Explore built-in graph styles

Present Value of a Stream of Cash Flows7:05
In this video, we walk through the basics of calculating present value of single and multiple cashflows.
• Learn the basic example
• Create a present value function
• Value a stream of future cashflows today
Future Value of Single and Multiple Cash Flows12:55
In this video, we will discuss future value concepts of fixed and uncertain rates of return. The video ends with an introduction to NumPy built-in functions for time value of money calculations.
• Learn the basic example
• Create a future value function
• Value current holdings with constant and uncertain returns
Net Present Value of a Project5:39
Introduction to comparing capital budgeting projects with net present value
• Manually calculate net present value
• Compare manual method with NumPy’s built-in function
Internal Rate of Return3:32
In this video, we will get a comparison and relationship of net present value to internal rate of return.
• Use NumPy’s built-in IRR function
• Compare IRR to NPV
Introduction to Amortization8:44
Walkthrough of enumeration of retiring long term debt in the form a table that tracks loan balance, interest and principal payments and cumulative interest paid
• Demonstrate the NumPypmt function to calculate monthly payments
• Build Pandas DataFrame to store relevant loan outputs
• Graph loan balance and cumulative interest over time
Creating an Amortization Application8:58
In this video, use parts of the previous video to create a reusable amortization application allowing users to compare loan scenarios.
• Create a function that outputs amortization table and loan summary data
• Compare several loan scenarios to demonstrate functionality

Opening and Reading a .CSV File8:54
In this video, we see how to open and work with data stored in CSV files.
• Demonstrate several methods of accessing data stored in CSV files
Getting and Evaluating Data12:40
We introduce the pandasdatareader library and use it to download and manipulate data.
• Download data from Yahoo finance
• Learn data transformations
• Understand data visualization
Moving Average Forecasting9:20
In this video we learn how to create and evaluate a moving average forecasting model.
• Create a moving average forecasting model with downloaded data
• Evaluate the forecasting model
Forecasting with Single Exponential Smoothing10:47
In this video, we will introduce another library’ statsmodels, and use its built-in single exponential smoothing model.
• Use statsmodels exponential smoothing model to forecast the price of gold
• Demonstrate the model with various values of a smoothing constant
Creating and Testing a Simple Trading System7:50
In this video, we will develop a simple signals based trading system and back test it for effectiveness.
• Create a fast and slow moving average forecast
• Create a signal and system return column
• Compare visually, the trading system to buy and hold strategy

Valuing Securities with Pricing Models12:11
In this video, we introduce basic models for stock valuation.
• Calculate expected return
• Value securities using dividends and expected future value
• Value security using PE ratio
Finding Correlations Between Securities10:36
Securities have a tendency to move together to some degree. This video demonstrates how to measure this relationship numerically and visually.
• Download data from Yahoo finance
• Data transformations
• Create table of correlations and graph relationships
Linear Regression13:22
Some variables have predictive power over others. In this video, we demonstrate and interpret a simple linear regression model with two libraries.
• Open and clean dataset
• Examine relationship with scatter plot
• Create linear model and use it to predict specific values
Calculating Beta and Expected Return8:30
Beta is used as a simple measurement of a stock’s riskiness relative to the market as a whole. This video derives Beta using two distinct methods.
• Download market and security data
• Transform data
• Calculate Beta with linear regression and with NumPycov function
Constructing Portfolios Along the Efficient Frontier11:54
Diversification can mute risk in the stock market. This video demonstrates the impact of diversification with a two security portfolio.
• Calculate expected return and volatility of a two security portfolio
• Create table with various weights, returns and volatilities
• Graph volatility vs. expected return

Introduction to Monte Carlo8:09
In this video, compare deterministic ending values with probabilistic ending values of a portfolio.
• Calculate table of ending values
• Calculate table of ending values based on probability distribution
Monte Carlo Simulation12:11
In this video, we will create a full simulation and examine results.
• Create NumPy array to store returns
• Create NumPy array to store yearly ending values
• Run simulation and examine results
Using Monte Carlo Technique to Calculate Value at Risk10:40
In this video, explore Value at Risk, calculate parametric VaR with simple formula and via Monte Carlo simulation.
• Calculate VaR deterministically
• Calculate VaR using Monte Carlo method
Putting It All Together – Monte Simulation Application5:12
In this video, put concepts discussed in previous videos together in a module that can be imported and used in encapsulated form.
• Combine methods of previous videos into functions of MonteCarlo class
• Demonstrate functionality

Requirements

We will use step-by-step tutorials that blend financial and programming concepts. Viewers will work along with the tutorial to create working examples.

Description

Did you know Python is the one of the best solution to quantitatively analyse your finances by taking an overview of your timeline? This hands-on course helps both developers and quantitative analysts to get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

You will begin with a primer to Python and its various data structures.Then you will dive into third party libraries. You will work with Python libraries and tools designed specifically for analytical and visualization purposes. Then you will get an overview of cash flow across the timeline. You will also learn concepts like Time Series Evaluation, Forecasting, Linear Regression and also look at crucial aspects like Linear Models, Correlation and portfolio construction. Finally, you will compute Value at Risk (VaR) and simulate portfolio values using Monte Carlo Simulation which is a broader class of computational algorithms.

With numerous practical examples through the course, you will develop a full-fledged framework for Monte Carlo, which is a class of computational algorithms and simulation-based derivatives and risk analytics.

About the Author

Matthew Macarty has taught graduate and undergraduate business school students for over 15 years and currently teaches at Bentley University. He has taught courses in statistics, quantitative methods, information systems and database design.

Who this course is for:

This course is for developers and analysts with some background in programming language and are interested in a concrete framework for using Python to augment or replace spreadsheet applications for financial tasks.

Hands-on Python for Finance

What you'll learn

Explore related topics

Course content

Python Programming Primer9 lectures • 1hr 1min

The Python Data Environment7 lectures • 1hr 15min

Time Value of Money6 lectures • 47min

Time Series Evaluation and Forecasting5 lectures • 50min

Linear Models, Correlation, and Valuation5 lectures • 57min

Build a Monte Carlo Simulation App4 lectures • 36min

Requirements

Description

Who this course is for: