GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading

TL;DR

This paper introduces GA-MSSR, a genetic algorithm-based trading system that optimizes technical indicator parameters to maximize risk-adjusted returns, demonstrating superior performance in forex trading with significant returns and reduced risk.

Contribution

The paper proposes a novel GA-based model that optimizes technical indicator parameters to maximize Sharpe and Sterling Ratios for forex trading, improving profitability and risk management.

Findings

Achieved up to 320% annual return on AUDUSD.

Outperformed benchmark models in risk factors.

Consistently positive returns across tested currency pairs.

Abstract

Foreign exchange is the largest financial market in the world, and it is also one of the most volatile markets. Technical analysis plays an important role in the forex market and trading algorithms are designed utilizing machine learning techniques. Most literature used historical price information and technical indicators for training. However, the noisy nature of the market affects the consistency and profitability of the algorithms. To address this problem, we designed trading rule features that are derived from technical indicators and trading rules. The parameters of technical indicators are optimized to maximize trading performance. We also proposed a novel cost function that computes the risk-adjusted return, Sharpe and Sterling Ratio (SSR), in an effort to reduce the variance and the magnitude of drawdowns. An automatic robotic trading (RoboTrading) strategy is designed with the proposed Genetic Algorithm Maximizing Sharpe and Sterling Ratio model (GA-MSSR) model. The experiment was conducted on intraday data of 6 major currency pairs from 2018 to 2019. The results consistently showed significant positive returns and the performance of the trading system is superior using the optimized rule-based features. The highest return obtained was 320% annually using 5-minute AUDUSD currency pair. Besides, the proposed model achieves the best performance on risk factors, including maximum drawdowns and variance in return, comparing to benchmark models. The code can be accessed at https://github.com/zzzac/rule-based-forextrading-system