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Mercado cripto · BinanceARB · ARB Quantitative Strategy Analysis: High-Win-Rate Swing Framework Demonstrates 6.88 Profit Factor and Distributed Return Drivers
This quantitative research report evaluates the simulated historical performance of the ARB 215000 +9517.92% 1TRAD-AMS5 algorithmic trading strategy applied to ARB · ARB on a 15-minute chart over a 2.56-year dataset extending from March 23, 2023, to October 12, 2025. Generating a DevioLab score of 77.14 and holding the rank 4 position among evaluated algorithms for this ticker, the strategy exhibits a highly disciplined statistical profile characterized by a 77.88% win rate across 113 closed trades, a profit factor of 6.88, and a maximum drawdown bounded at 19.90%. A defining characteristic of the system is its minimal reliance on extreme tail events, with the three largest winning trades accounting for only 11.17% of total gross profit, paired with a median trade return of +4.92% that comfortably exceeds its average trade return of +4.61%. Operating with an average position duration of 60.20 hours and a trade frequency of 3.53 trades per active month, the strategy translates granular sub-hourly price structure into multi-day swing positions that have maintained performance continuity through recent market regimes, as evidenced by 58 completed trades producing a cumulative closed trade return sum of +282.48% since June 1, 2024.
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Strategy profile
The algorithmic model designated ARB 215000 +9517.92% 1TRAD-AMS5 is a specialized quantitative trading strategy configured for ARB · ARB on the 15-minute execution interval. Spanning a historical evaluation window of 2.56 years from March 23, 2023, through October 12, 2025, the strategy captures price dynamics across 113 completed trade cycles. Within the DevioLab quantitative evaluation framework, this system achieves a score of 77.14, placing it at rank 4 for the ARB asset class. Rather than engaging in hyper-frequent intraday turnover, the strategy utilizes the granular resolution of the 15-minute bar to establish selective position entries, averaging approximately 44.16 trades per year or 3.53 trades per active month. Across the full backtested history, the strategy achieved 88 winning trades against 25 losing trades, culminating in a cumulative closed-trade return sum of +9517.92% and an annualized equivalent return metric of 1195.06%. These results reflect a structural design oriented toward capturing sustained medium-term price swings while filtering out lower-conviction market noise.
Trading rhythm and position duration
Despite utilizing a 15-minute price chart for signal generation, the strategy operates as a dedicated swing trading framework rather than a high-frequency or scalping system. The historical trade metrics demonstrate a pronounced multi-day holding pattern, with an average position duration of 60.20 hours (approximately 2.51 days) and a median position duration of 39.00 hours (1.63 days). The right-skewed relationship between average and median holding times indicates that while the strategy regularly resolves trades within two days, it routinely extends duration during persistent directional trends to maximize profit extraction. In terms of trade pacing, the system exits positions at an average interval of 8.33 days, with a median exit separation of 5.99 days. This temporal spacing confirms that market engagement is highly selective; the algorithm spends substantial periods out of the market or holding single active positions, avoiding over-trading during chop or low-conviction phases. The contrast between short bar resolution and multi-day holding times underscores a methodology designed to refine execution timing without sacrificing the broader trend capture.
Quality of historical results
An examination of payoff distribution reveals structural strength and consistency across historical trade results. The strategy achieves a elevated win rate of 77.88%, supported by an average trade return of +4.61% and a median trade return of +4.92%. Crucially, the median return exceeding the average return demonstrates that positive performance is driven by a repeatable core distribution of profitable trades rather than skewed by a few extreme positive outliers. This organic return generation is further validated by the profit factor of 6.88, which indicates that total gross profits substantially outweighed total gross losses over the 2.56-year dataset. Furthermore, profit concentration remains exceptionally well-balanced: the top three winning trades generated just 11.17% of aggregate gross profit. The best individual trade generated a gain of +28.71%, while the worst single trade suffered a loss of -17.92%. Because no single trade dominates the overall equity curve, the statistical evidence implies that the strategy's expectancy is broad-based and resilient across different price legs.
Risk, drawdown and losing behavior
Risk characteristics across the backtest period highlight robust defensive mechanics. The maximum historical drawdown was restricted to 19.90%, a modest figure when evaluated against the strategy's total cumulative percentage returns and the inherent volatility of crypto markets. Losing trade behavior reveals controlled adverse exposure, with the longest consecutive losing streak reaching only 3 trades, compared to a maximum winning streak of 11 trades. The maximum single-trade loss of -17.92% represents a significant adverse excursion relative to the average loss, yet the strategy's high win rate (77.88%) prevents isolated drawdowns from compounding into structural equity impairment. The high profit factor (6.88) combined with short losing streaks suggests that the algorithm effectively terminates non-performing trades before multi-trade loss sequences can build momentum, preserving equity capital during unfavorable market regimes.
Behavior through time and yearly stability
Tracking annual execution metrics demonstrates persistent historical profitability alongside shifting operational frequency across market cycles. In 2023 (covering performance from March 23 onward), the strategy generated 31 trades with 26 wins and 5 losses, establishing a peak annual win rate of 83.87% and a aggregate trade sum of +126.05%. In 2024, trade activity accelerated to 51 completed trades, yielding 39 wins and 12 losses for a win rate of 76.47% and a cumulative trade sum of +229.57%. Through the 2025 period up to October 12, the strategy registered 31 trades with 23 wins and 8 losses, posting a win rate of 74.19% and a trade sum of +165.70%. While win rate normalized slightly from the initial 83.87% level toward ~74-76% in subsequent years, absolute profit production expanded due to increased trade frequency. This consistency across three consecutive calendar years indicates that the algorithm's performance drivers are durable rather than tied to a single transient regime.
Recent period since 2024-06-01 versus full history
Evaluating recent historical performance provides crucial insight into the strategy's current alignment with market structure. In the window from June 1, 2024, through October 12, 2025, the strategy completed 58 trades, representing 51.3% of all trades recorded in the full dataset. Over this recent sample, the cumulative sum of closed-trade returns reached +282.48%. Comparing this to the complete historical log reveals that recent trade frequency (~3.5 trades per month) matches the baseline long-term average, while return velocity has remained strong. The substantial volume of completed trades within this window provides a statistically relevant sample size, confirming that the algorithm did not enter a period of stagnation or elevated loss frequency in recent market conditions, maintaining its structural trade profile.
Strengths and limitations
The primary quantitative strength of this trading system lies in its robust payoff efficiency, reflected in the 6.88 profit factor, 77.88% win rate, and controlled 19.90% maximum drawdown. The minimal reliance on outlier trades (top 3 winners accounting for only 11.17% of gross profit) and a median return (+4.92%) that outpaces the average return (+4.61%) confirm high consistency in trade distribution. Conversely, material limitations exist within the sample size and trade parameters. With 113 completed trades over 2.56 years, the total sample remains relatively compact, which introduces potential statistical estimation variance compared to systems with thousands of executions. Additionally, the worst single trade loss of -17.92% indicates that during sharp, unhedged market shocks, individual loss magnitudes can expand significantly, requiring strict adherence to operational risk controls.
DevioLab analytical conclusion
The DevioLab score of 77.14 and rank 4 position reflect a highly balanced, robust quantitative framework for ARB · ARB. The strategy successfully bridges lower-timeframe execution mechanics with medium-term trend capture, generating favorable expectancy without over-exposing equity to high transaction frequency or extreme profit concentration. Its ability to limit maximum historical drawdown to 19.90% while maintaining a profit factor of 6.88 demonstrates disciplined risk containment. However, quantitative analysts must evaluate these findings within the context of the 113-trade sample size. While historical evidence strongly supports the efficiency of the underlying model, continuous monitoring of trade execution parameters remains essential to confirm ongoing regime alignment.
Data scope and methodology
This quantitative research document is derived strictly from historical simulated backtest data generated for the strategy ARB 215000 +9517.92% 1TRAD-AMS5 on the ARB · ARB asset operating on a 15-minute chart interval. The evaluation history spans from March 23, 2023, to October 12, 2025. All performance metrics, including total return sums, win rates, drawdown figures, holding durations, and profit concentration calculations, reflect non-leveraged closed trade calculations derived within this specific testing environment. The historical start date marks the beginning of the strategy dataset and does not correspond to the initial asset listing or creation date. These simulated research results do not reflect live account execution, actual transaction fees, slippage, or order book liquidity limitations, and past historical performance provides no guarantee of future trading outcomes.