  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
