ALGORITHM TRADING 101: Trading made simple for everyone: Chapter 4: Automated Trading Machine Neural Network Enhanced Technical Indicator

by Jacinta Chan

With computational trading algorithms, which can learn from multiple sources of information, technical trading rules evolved to include artificial intelligence machine learning method using variants of neural networks. Backpropagation neural network outperforms common technical analysis indicators and traditional statistical models. Neural networks are used in machine learning by inputting past historical price data and technical indicators to predict the next output. The training is performed to achieve the lowest mean error between the predicted output and the target which is the actual close.This chapter shows how to incorporate neural network for decision making into AMA’. It architecture of Artificial Neural Networks (ANN) Trading ModelHow Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX) can be used for tradingHow NARX – Close and Adjustable Moving Average’ (N-CAMA’) Trading Model can be put together to create a hybrid model that is more robust than the average.

67 pages, Kindle Edition

Published October 13, 2023

About the author

Jacinta Chan has a PhD in financial statistics, lectures on investing topics at the university level, and spent most of her career as a futures and equities dealer. She is author
of Algorithm Trading 101: Trading Made Simple For Everyone https://a.co/d/bRWyeJa
https://www.amazon.com/Algorithm-Trad...

Automated Trading Machine: How to develop your own trading model, Palgrave Macmillan, Singapore, 2018

Financial Times Guide to Technical Analysis: How to trade like a professional, Financial Times Prentice Hall, London, 2011.

Everything Technical Analysis: How to trade like a professional, Prentice Hall

She is currently researching neural network trading systems.
She can be reached at chanpmjacinta@gmail.com.