Title:
RISK CONTROL IN ALGORITHMIC TRADING USING FIBONACCI SERIES

Authors:
Saeed Tabar, Maryam Gholamalitabar, Lotfollah Najjar, and David Volkman

Abstract:
Algorithmic trading has attracted a great deal of attention in stock market especially after 2000. It offers numerous advantages over traditional trading such as less transaction cost, higher speed, and higher accuracy. One of the important building blocks of each trading strategy in algorithmic trading is the risk control part. It protects a strategy from losing the obtained benefits. In this article, a new algorithm using Fibonacci summation series is proposed to better control the amount of loss in trading. The research results show that the proposed algorithm leads to a higher benefit compared to traditional algorithms such as Simple Moving Average with Average True Range Stop-Loss and Exponential Moving Average with Average True Range Stop-Loss.