Tag: Python Programming

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

Introduction

Data Science in Depth

• What is Plotly? What is Dash?
• Pandas overview
• Numpy overview

Plotly Basics

• Plots
• Heatmaps

Preparing the Development Environment

• Installing and configuring Plotly
• Installing and configuring Dash

Dash Core Components

• Using drowdown and slider components
• Uploading CSV, XLS, and images
• Working with Dash layouts
• Converting Plotly plots to dashboards
• Using callbacks
• Working with inputs and outputs

Dash Dashboards

• Pulling API data
• Building a binance dashboard
• Connecting Dash components
• Using alpha vantage
• Cleaning data
• Controlling callbacks
• Updating graphs
• Working with layout updating

Deployment

• Working with app authorization
• Deploying with Heroku

Summary and Conclusion

• Introduction to Python
• Controlling the flow of your program
• Working with lists
• Working with the dictionary data type
• Manipulating strings
• Pattern matching with regular expressions
• Reading, writing and managing files
• Debugging your code
• Pulling information from the internet (web scraping)
• Working with Excel, Word, and PDF Documents
• Working with CSV and JSON
• Keeping time
• Scheduling tasks
• Launching programs
• Sending emails and other messages
• Manipulating images
• GUI Automation
• Closing remarks

Introduction

Algorithmic Trading Core Concepts

• What is algorithmic trading?
• Markets and trading
• Textual data and analysis

Python, R, and Stata

• Stock trading
• Bond trading
• Investment analysis

Preparing the Development Environment

• Installing Quandl
• Installing quantmod
• Installing and configuring Stata

Algorithmic Trading and Python

• Importing data
• Using Quandl
• Working with financial data
• Creating databases for financial data

Algorithmic Trading and R

• Importing data
• Using quantmod
• Working with regressions

Algorithmic Trading and Stata

• Importing and cleaning data
• Testing strategies
• Working with regressions

Summary and Conclusion

Introduction

• Overview of Tableau
• Fundamentals of Python, R, and SQL

Getting Started

• Setting up the development environment
• Understanding software integration

Data Analysis with Python

• Python fundamentals and programming
• Importing libraries and datasets
• Wrangling data
• Data normalization and formatting
• Exploratory data analysis
• Performing regression analysis
• Model development and evaluation
• Visualizing Data

Data Analysis with R

• R fundamentals and programming
• Preparing data
• Classifying and working with data in R
• Using functions
• Visualizing Data

Data Analysis with SQL

• Setting up the database
• Connecting Python and SQL
• Connecting R and SQL
• SQL aggregations and joins
• Querying the database
• Manipulating data

Data Visualization Using Tableau

• Tableau design principles for visualization
• Creating dashboards, charts, and tables
• Mapping techniques
• Regressions in R and Tableau
• Advanced analytics with R and Tableau
• Practical examples and use cases

Troubleshooting