Tag: finance

Introduction

Setting up the Development Environment

• Programming locally vs online: Anaconda and Jupyter

Python Programming Fundamentals

• Control structures, data types, functions, data structures and operators

Extending Python’s Capabilities

• Modules and Packages

Your first Python Application

• Estimating beginning and ending dates and times

Accessing External Data with Python

• Importing and exporting, reading and writing CSV data
• Accessing data in an SQL database

Organizing Data Using Arrays and Vectors in Python

• NumPy and vectorized functions

Visualizing Data with Python

• Matplotlib for 2D and 3D plotting, pyplot, and SciPy

Analyzing Data with Python

• Data analysis with scipy.stats and pandas
• Importing and exporting financial data (Excel, website data, etc.)

Simulating Asset Price Trajectories

• Monte Carlo simulation

Asset Allocation and Portfolio Optimization

• Performing capital allocation, asset allocation, and risk assessment

Risk Analysis and Investment Performance

• Defining and solving portfolio optimization problems

Fixed-Income Analysis and Option Pricing

• Performing fixed-income analysis and option pricing

Financial Time Series Analysis

• Analyzing time series data in financial markets

Taking Your Python Application into Production

• Integrating your application with Excel and other web applications

Application Performance

• Optimizing your application
• Parallel Computing and Multiprocessing

Troubleshooting

Closing Remarks

Duration

28 hours (usually 4 days including breaks)

Requirements

• Experience with Python programming
• General familiarity with finance concepts
• Basic familiarity with statistics and mathematical concepts

Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use Python, Keras, and TensorFlow to create deep learning models for finance
• Build their own deep learning stock price prediction model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

Introduction

Understanding the Fundamentals of Artificial Intelligence and Machine Learning

Understanding Deep Learning

• Overview of the Basic Concepts of Deep Learning
• Differentiating Between Machine Learning and Deep Learning
• Overview of Applications for Deep Learning

Overview of Neural Networks

• What are Neural Networks
• Neural Networks vs Regression Models
• Understanding Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Connections
• Working with Neurons, Layers, and Input and Output Data
• Understanding Single Layer Perceptrons
• Differences Between Supervised and Unsupervised Learning
• Learning Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Back Propagation
• Understanding Long Short-Term Memory (LSTM)
• Exploring Recurrent Neural Networks in Practice
• Exploring Convolutional Neural Networks in practice
• Improving the Way Neural Networks Learn

Overview of Deep Learning Techniques Used in Finance

• Neural Networks
• Natural Language Processing
• Image Recognition
• Speech Recognition
• Sentimental Analysis

Exploring Deep Learning Case Studies for Finance

• Pricing
• Portfolio Construction
• Risk Management
• High Frequency Trading
• Return Prediction

Understanding the Benefits of Deep Learning for Finance

Exploring the Different Deep Learning Libraries for Python

• TensorFlow
• Keras

Setting Up Python with the TensorFlow for Deep Learning

• Installing the TensorFlow Python API
• Testing the TensorFlow Installation
• Setting Up TensorFlow for Development
• Training Your First TensorFlow Neural Net Model

Setting Up Python with Keras for Deep Learning

Building Simple Deep Learning Models with Keras

• Creating a Keras Model
• Understanding Your Data
• Specifying Your Deep Learning Model
• Compiling Your Model
• Fitting Your Model
• Working with Your Classification Data
• Working with Classification Models
• Using Your Models

Working with TensorFlow for Deep Learning for Finance

• Preparing the Data
  • Downloading the Data
  • Preparing Training Data
  • Preparing Test Data
  • Scaling Inputs
  • Using Placeholders and Variables
• Specifying the Network Architecture
• Using the Cost Function
• Using the Optimizer
• Using Initializers
• Fitting the Neural Network
• Building the Graph
  • Inference
  • Loss
  • Training
• Training the Model
  • The Graph
  • The Session
  • Train Loop
• Evaluating the Model
  • Building the Eval Graph
  • Evaluating with Eval Output
• Training Models at Scale
• Visualizing and Evaluating Models with TensorBoard

Hands-on: Building a Deep Learning Model for Stock Price Prediction Using Python

Extending your Company’s Capabilities

• Developing Models in the Cloud
• Using GPUs to Accelerate Deep Learning
• Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion

Duration

28 hours (usually 4 days including breaks)

Requirements

• Experience with R programming
• General familiarity with finance concepts
• Basic familiarity with statistics and mathematical concepts

Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in finance
• Use R to create deep learning models for finance
• Build their own deep learning stock price prediction model using R

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

Introduction

Understanding the Fundamentals of Artificial Intelligence and Machine Learning

Understanding Deep Learning

• Overview of the Basic Concepts of Deep Learning
• Differentiating Between Machine Learning and Deep Learning
• Overview of Applications for Deep Learning

Overview of Neural Networks

• What are Neural Networks
• Neural Networks vs Regression Models
• Understanding Mathematical Foundations and Learning Mechanisms
• Constructing an Artificial Neural Network
• Understanding Neural Nodes and Connections
• Working with Neurons, Layers, and Input and Output Data
• Understanding Single Layer Perceptrons
• Differences Between Supervised and Unsupervised Learning
• Learning Feedforward and Feedback Neural Networks
• Understanding Forward Propagation and Back Propagation
• Understanding Long Short-Term Memory (LSTM)
• Exploring Recurrent Neural Networks in Practice
• Exploring Convolutional Neural Networks in practice
• Improving the Way Neural Networks Learn

Overview of Deep Learning Techniques Used in Finance

• Neural Networks
• Natural Language Processing
• Image Recognition
• Speech Recognition
• Sentimental Analysis

Exploring Deep Learning Case Studies for Finance

• Pricing
• Portfolio Construction
• Risk Management
• High Frequency Trading
• Return Prediction

Understanding the Benefits of Deep Learning for Finance

Exploring the Different Deep Learning Packages for R

Deep Learning in R with Keras and RStudio

• Overview of the Keras Package for R
• Installing the Keras Package for R
• Loading the Data
  • Using Built-in Datasets
  • Using Data from Files
  • Using Dummy Data
• Exploring the Data
• Preprocessing the Data
  • Cleaning the Data
  • Normalizing the Data
  • Splitting the Data into Training and Test Sets
• Implementing One Hot Encoding (OHE)
• Defining the Architecture of Your Model
• Compiling and Fitting Your Model to the Data
• Training Your Model
• Visualizing the Model Training History
• Using Your Model to Predict Labels of New Data
• Evaluating Your Model
• Fine-Tuning Your Model
• Saving and Exporting Your Model

Hands-on: Building a Deep Learning Model for Stock Price Prediction Using R

Extending your Company’s Capabilities

• Developing Models in the Cloud
• Using GPUs to Accelerate Deep Learning
• Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion

Students will learn python programming language.

Requirements

• Some experience with excel would help but not essential

Description

This is a beginner level course designed specifically for accounting and business professionals who have no prior knowledge of computer programming.

Python is gaining popularity in the accounting and finance field. Despite professional willingness to learn it, most courses available online are more centric to IT background professionals.

It is a 1 hour and 18 minutes short course for accountants, auditors, and other finance professionals to learn python taking small steps at a time. The aim is to avoid technical jargon and setup complexities so the accountants can get straight to learning rather than frustrating installing software and setting up environments.

Practice exercises use Google Colab Notebook, so you don’t need to install anything on your computer. Google Colab is a cloud-based software that allows you to write your code online. All you need is just a google account.

Class notebooks are attached to either download or linked to open in Google Colab directly.

What we cover in this course:

· Introduction to Programming

· Introduction to Python

· Syntax or Grammar of Programming

· Variables

· Data Formats (Structured Data, Unstructured Data etc.)

· Data Types (String, Numbers, Dates, Boolean etc.)

· Operators(>, =, /, ==, & etc.)

· Conditional Statements (if, else, etc.)

· Loops (For, While)

· Functions

· Data Structures in Python (Lists, Dictionaries, Data Frames )

After completing this course

1. You will have an understanding of computer programming

2. Various Data Structures

3. Write basic Python syntax

4. Familiarity with a notebook, so you know where to write your code.

See you in Class!

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I want to credit the following resources in making this course.

Music:

Track: We Were Young — Vendredi [Audio Library Release]

Music provided by Audio Library Plus

Available at YouTube

Videos and Images:

Pixabay

Pexels

Videezy

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Who this course is for:

• Accounting and finance professionals, students in accounting and finance and people with general interest in python

Course content