Tag: quantitative finance

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.

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 K-nearest Neighbors 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.

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 Ridge 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.

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 LASSO 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.

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 Ordinary Least Squares (OLS) 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.

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 seasonality example found in chapter two 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.

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 STDEV and MOM examples found in chapter two 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.

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 BBANDS and RSI examples found in chapter two 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.