ALGORITHMIC TRADING DENGAN STRATEGI MENGIKUTI TREN MENGGUNAKAN EXPONENTIAL MOVING AVERAGE DAN STOCHASTIC OSCILLATOR - Ahmad Juniar; Asep Juarna (Lembaga Penelitian dan Pengabdian Pada Masyarakat, 2026)
The restrictions on social interactions during the COVID-19 pandemic encouraged individuals to seek alternative sources of income. Advances in information technology have enabled investors to participate in financial markets through online platforms. According to data from the Indonesian Central Securities Depository (KSEI), the number of capital market investors in Indonesia increased between 2000 and 2022. Algorithmic trading refers to a systematic trading approach in which computer algorithms are utilized to generate trading signals and execute market orders automatically according to predefined trading rules. This approach employs technical analysis to forecast asset or commodity prices using historical price and trading volume data. This study develops an algorithmic trading system for cocoa futures, a commodity traded on the Intercontinental Exchange (ICE) New York and ICE London. The proposed model incorporates two technical indicators, namely the Exponential Moving Average (EMA) and the Stochastic Oscillator. The trading system was implemented using the MQL5 programming language on the MetaTrader 5 platform. Historical cocoa price data covering the period from 2024 to 2025 were obtained from Dukascopy Bank SA, a Swiss banking institution. The experimental results show that the proposed system achieved a win rate of 42.59% and a profit factor of 1.63, while maintaining a maximum drawdown of 8.52%, indicating a relatively low level of risk. Furthermore, the equity curve exhibited consistent growth and the histogram analysis demonstrated stable performance across most trading days. Based on established performance metrics, the proposed system is considered viable and sufficiently robust, as it successfully withstands multiple market cycles.