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All Journal Jurnal Sosial dan Teknologi Jurnal Indonesia Sosial Sains
Rama Pramasandy
HWR Algo Nusa
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Neuroscience Physics Social Sciences

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Adaptive Layered Buying Strategy Using Mode and Standard Deviation of Daily Price Range: Evidence from BBRI Indonesia Stock Market Cevi Herdian; Rama Pramasandy
Jurnal Indonesia Sosial Sains Vol. 7 No. 8 (2026): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v7i8.2467

Abstract

The Indonesian stock market exhibits substantial price volatility, making fixed-interval averaging strategies less effective under changing market conditions. This study proposes an adaptive averaging strategy based on the historical distribution of daily price ranges. Daily open, high, low, and close (OHLC) price data for Bank Rakyat Indonesia (BBRI), covering the period from November 2003 to July 2026 and comprising 5,602 observations, were analyzed. The adaptive buying interval was estimated using the mode of non-zero daily price ranges combined with two standard deviations. The resulting interval was then used to construct a layered buying strategy with exponential position sizing. Simulation results indicated that the proposed strategy substantially reduced the average acquisition cost while maintaining manageable capital requirements. Under a seven-level buying strategy, total capital deployment reached IDR 23.68 million, assuming purchases began in January 2025. Furthermore, the simulated portfolio generated a positive unrealized return of 59.28% under the historical price scenario. These findings suggest that statistical measures of price volatility provide a practical basis for determining adaptive averaging intervals in long-term equity investment. The proposed framework offers a simple yet robust alternative to conventional fixed-interval averaging strategies.
Performance Evaluation of a Rule-Based Algorithmic Trading Framework: Evidence From 8,772 Live Cryptocurrency Bitcoin Trades Cevi Herdian; Rama Pramasandy
Jurnal Sosial Teknologi Vol. 6 No. 8 (2026): Jurnal Sosial dan Teknologi
Publisher : CV. Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsostech.v6i8.32954

Abstract

Research on algorithmic trading has generally continued to rely on historical testing (backtesting), which often involves biases arising from unrealistic execution assumptions, such as idealized order fills and negligible slippage, and may fail to accurately reflect actual market conditions. Meanwhile, studies based on live trading with real capital remain limited, particularly those evaluating purely rule-based strategies over extended periods using a large number of executed transactions. This study aimed to address this research gap by evaluating the real-world performance of a rule-based quantitative trading framework using actual transaction data from a real-capital cryptocurrency trading account operating under live market conditions. A descriptive quantitative method was applied to 8,772 Bitcoin cryptocurrency transactions executed between September 2025 and July 2026. Performance was evaluated using standard portfolio performance metrics, including gross profit, net profit, cumulative return, profit factor, maximum drawdown, recovery factor, trading frequency, and win rate. The results showed that the proposed framework increased account equity from USD 30,056 to USD 76,376.54, representing a cumulative return of 154.11% over the evaluation period. The strategy also achieved a profit factor of 1.22, a recovery factor of 1.33, and an average trading frequency of 97 transactions per week, despite experiencing a maximum drawdown of 43.09%. These findings confirmed that rule-based strategies can maintain positive profitability and demonstrate resilience in recovering from adverse market conditions. By utilizing evidence from genuine live trading activities, this study provides practical validation and contributes empirical insights into the implementation of algorithmic trading strategies in volatile cryptocurrency markets.
Co-Authors Cevi Herdian Cevi Herdian