Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 4 Pairs Correlation Heatmap Example

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the Pairs Correlation Heatmap example found in chapter four of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 4 Turtle Trading Example

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the Turtle Trading example found in chapter four of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Steven Pressfield on Put Your Ass Where Your Heart Wants to Be, Part 1

In the book, Put Your Ass Where Your Heart Wants to Be, Steven Pressfield shares his inspiration and techniques to help us make the life-altering transformation.

These are some of my favorite takeaways from reading the book.

First and foremost, what does Steve mean when he says, “Put your ass where your heart wants to be?” He suggests we must station our physical body where our dream work will and must happen.

Want to program a computer? Sit down at the keyboard.

Want to paint? Step up before the easel.

Want to dance? Get our butt into the rehearsal studio.

Why is this simple advice so hard to do? Fear!

Fear will always be with us, so there is no way to eliminate it. The only thing we can do to counter the fear is to do the work. “Tremendous power lies in the simple, physical act of stationing our body at the epicenter of our dream.”

Sometimes, “Put Your Ass…” might mean a new physical location.

Want to work in country music? Consider Nashville, TN.

Want to act in movies? Consider Hollywood, CA.

Steve says, “Leave the Town or city where you live and move to the hub of the creative or entrepreneurial world where your dreams are most likely to come true.”

Another reason to relocate is to work with the people whom we can learn from. Role models and knowledgeable friends are what we all learn from. Those are the people we watch and copy.

For some fields, we might have to move because the new location is where the industry pros and masters congregate. Sometimes it is not for our hearts to be in the right place. Our body must be there too.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 4 Naïve Momentum Example

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the Naïve Momentum example found in chapter four of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 4 Dual Moving Average Example

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the Dual Moving Average example found in chapter four of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 3 Logistic Regression Example

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the Logistic Regression example found in chapter three of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.

Quantitative Finance Model using Donadio and Ghosh Learn Algorithmic Trading Chapter 3 Support Vector Machine Example

NOTE: This script is for learning purposes only and does not constitute a recommendation for buying or selling any stock mentioned in this script.

SUMMARY: This project aims to construct and test an algorithmic trading model and document the end-to-end steps using a template.

INTRODUCTION: This script aims to replicate the Support Vector Machine example found in chapter three of the book Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. The script seeks to validate the Python environment and package requirements for running these code examples successfully. The eventual goal is to integrate various example code segments from the book into an end-to-end algorithmic trading system.

Dataset ML Model: Time series analysis with numerical attributes

Dataset Used: Sharadar US Equities and Fund Prices from Quandl/Nasdaq Data Link

Source and Further Discussion of the Code Examples: https://github.com/PacktPublishing/Learn-Algorithmic-Trading

The HTML formatted report can be found here on GitHub.