Performance Evaluation of a Rule-Based Algorithmic Trading Framework: Evidence From 8,772 Live Cryptocurrency Bitcoin Trades
DOI:
https://doi.org/10.59188/jurnalsostech.v6i8.32954Keywords:
Algorithmic trading, Cryptocurrency trading, Quantitative trading, Live trading, Portfolio performanceAbstract
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.
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