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 Dynamic Risk Allocation example found in chapter six 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: Sample GOOG stock data available with the book

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. [https://github.com/daines-analytics/quant-finance-projects/tree/master/py_quantfinance_donadio_ghosh_learn_algorithmic_trading]

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 Static Risk Allocation example found in chapter six 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: Sample GOOG stock data available with the book

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 Statistical Arbitrage example found in chapter five 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 Volatility Adjusted Trend Following example found in chapter five 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.