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加密市场 · BinanceSUI · SUI Quantitative Analysis: High-Precision Returns and Outlier Reliance in Strategy Profile 1TRAD-NEZ3
This quantitative research report evaluates the historical backtested performance of the 15-minute algorithmic strategy SUI 215000 +21336.64% 1TRAD-NEZ3 on SUI. Operating across a historical data scope of approximately 3.3 years from May 2023 through August 2026, the strategy generated an overall cumulative historical profit of 21,336.64% across a highly constrained sample of 25 completed trades. Characterized by an extraordinary 96.00% win rate (24 winning trades against a single losing trade) and a peak drawdown restricted to 1.93%, the model achieves a DevioLab Score of 85.79, ranking first among evaluated systems for this ticker. However, deeper mathematical examination exposes critical structural attributes: 58.58% of total gross profit is concentrated within just three trades, and the average trade (+30.45%) substantially deviates from the median trade (+10.79%). Furthermore, with zero trade activity recorded since June 1, 2024, the strategy presents a statistical profile defined by ultra-high selection filters, notable profit concentration, and an absence of recent empirical trade data.
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Strategy profile
The algorithmic trading model designated as SUI 215000 +21336.64% 1TRAD-NEZ3 is designed for the cryptocurrency asset SUI (SUI) using a 15-minute timeframe execution canvas. Within the DevioLab quantitative evaluation framework, the strategy holds the number one rank for SUI, accumulating a DevioLab Score of 85.79 under the core balanced selection baseline. The historical dataset supporting this evaluation spans 3.30 years, originating on May 3, 2023, and extending through August 21, 2026. Across this multi-year evaluation window, the strategy executed a total of 25 completed trades, delivering a cumulative historical return of 21,336.64% and an annualized performance metric of 2,845.65%. The underlying engine operates as an extremely selective system where market engagements are exceptionally sparse, yielding substantial return accumulation from a minimal number of historical exposure cycles.
Trading rhythm and position duration
Despite operating on a 15-minute chart interval, the strategy exhibits an extraordinarily low operational cadence. Over the 3.3-year historical window, the model completed 25 trades, translating to an average frequency of 7.57 trades per year. Detailed granular metrics regarding average holding hours, median duration, and spacing between trade exits are unrecorded within the primary dataset. However, the macro trading rhythm clearly demonstrates that the system does not engage in frequent intraday turnover. The juxtaposition of a 15-minute execution interval with fewer than eight completed trades per year implies that signal triggers require highly specific market configurations. Traders analyzing this model must recognize that position entries are rare events, separated by extended periods of complete inactivity rather than continuous back-and-forth market exposure.
Quality of historical results
The mathematical quality of the strategy's trades displays a stark divergence between consistency of hit rate and distribution of trade magnitude. On a hit-rate basis, the system achieved 24 winning trades out of 25 total executions, representing a 96.00% win rate and establishing a profit factor of 4.06. However, analyzing return distribution reveals substantial positive skewness. The overall average trade return stands at +30.45%, whereas the median trade return is lower at +10.79%. This gap is explained by extreme top-end winners, led by a single best trade of +258.78%. Furthermore, the top three winning trades generated 58.58% of the strategy's total gross profit. While the 96% accuracy reflects extraordinary loss containment across 24 trades, the cumulative financial performance relies heavily on a tiny cluster of outlier moves.
Risk, drawdown and losing behavior
Risk metrics within the historical dataset show exceptional downside containment. The maximum historical drawdown recorded across the entire 3.3-year history was strictly limited to 1.93%. This maximum drawdown figure aligns directly with the single losing trade present in the historical record, which registered at -1.93%. The strategy sustained a single maximum losing streak of 1 trade against a maximum winning streak of 24 consecutive profitable trades. Because the worst historical trade and the maximum drawdown are virtually identical, the backtested data indicate that open trade exposure rarely drifted into negative territory during execution. However, because the historical risk profile is constructed on only one losing event, the sample size for adverse market behavior is statistically minimal.
Behavior through time and yearly stability
Evaluating temporal stability for this strategy requires interpreting a sample distributed across 3.30 years. Annualized breakdown data for individual calendar years are not explicitly detailed in the source tables, limiting granular year-over-year performance comparisons. However, mapping 25 total completed trades across 3.3 years demonstrates an average production rate of roughly two trades per calendar quarter. The historical return profile of 21,336.64% was therefore built through episodic jumps when individual trades resolved, rather than a smooth, continuous linear yield curve. The overall history establishes that historical gains were achieved through patient, low-frequency participation rather than uniform monthly accumulation.
Strengths and limitations
The primary structural strengths of the SUI 215000 +21336.64% 1TRAD-NEZ3 model lie in its historical accuracy and equity curve preservation. A 96.00% win rate, a 4.06 profit factor, and a maximum drawdown capped at 1.93% demonstrate exceptional backtested efficiency. Conversely, the strategy possesses distinct analytical limitations. First, the total trade sample is exceptionally small at 25 trades over 3.3 years, reducing overall statistical confidence. Second, profit generation is heavily concentrated, with three trades producing 58.58% of gross gains and the single best trade reaching +258.78%. Third, the total absence of completed trades since June 1, 2024, leaves the strategy without recent operational validation, requiring analysts to weigh historical excellence against extended periods of zero trade output.
DevioLab analytical conclusion
With a DevioLab Score of 85.79 and a rank of 1 for SUI, the 1TRAD-NEZ3 strategy represents a highly specialized quantitative model focused on extreme selectivity. Historically, the strategy succeeded in producing a cumulative return of 21,336.64% while keeping drawdown below 2.00%. However, quantitative assessment emphasizes that this performance is derived from a sparse sample of 25 trades, where outcome distribution is right-skewed by a few massive winners. Furthermore, prolonged operational inactivity since mid-2024 highlights that the system requires rare market conditions to operate. Market participants evaluating this strategy must balance its backtested precision against its low trade frequency, outlier dependency, and recent empirical dormancy.
Data scope and methodology
This quantitative analysis is based exclusively on backtested historical closed-trade data for SUI (SUI) on the 15-minute timeframe between May 3, 2023, and August 21, 2026. All reported statistics, including the 21,336.64% total cumulative return, 96.00% win rate, and 1.93% maximum drawdown, represent historical simulated outcomes derived from closed trades. Order execution assumptions, exchange fees, slippage, and real-time order book liquidity are not modeled in the dataset. Historical backtested results do not guarantee future performance and do not constitute financial or investment advice.