JASMYUSDT
Crypto market · BinanceQuantitative Evaluation of JASMY · JASMY: 15-Minute Algorithmic Model Architecture and Performance Profile
This empirical report evaluates the historical backtest performance of the core 15-minute algorithmic trading strategy for JASMY across a 4.74-year evaluation window from November 2021 through August 2026. Attaining a DevioLab Score of 80.58 and ranking first among strategies evaluated for this asset, the model completed 318 closed trades with a 75.16% win rate and an overall profit factor of 5.00. The strategy demonstrates substantial distribution balance, with its top three winning trades contributing just 4.27% of gross profits, confirming that equity growth is driven by repeated operational edge rather than isolated tail events. However, structural drawdown exposure is bounded by a maximum peak-to-trough decline of 29.86%, matching its worst single loss. With 159 completed trades logged since June 1, 2024, generating a cumulative 301.16% return over that recent sub-period, the system displays consistent statistical robustness across both bear and bull market regimes.
Analysis as of 31.08.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The evaluated quantitative algorithm, designated within the DevioLab framework as JASMY 215000 +49320.96% 1TRAD-UFM6, operates on a 15-minute price bar resolution targeting the cryptocurrency asset JASMY. Across a total evaluation sample spanning 4.74 years from November 22, 2021, to August 19, 2026, the strategy executed 318 completed trade cycles. Within the DevioLab scoring framework, this model earned an analytical score of 80.58, positioning it as the top-ranked strategy for the JASMY asset profile. Over the complete historical dataset, the strategy registered a cumulative simulated trade sum of 49,320.96%, yielding an annualized performance metric of 1,081.75%. The primary baseline parameters reflect a highly selective trading engine that balances trade frequency with structural statistical persistence, establishing a consistent historical trajectory across changing market cycles.
2. Trading rhythm and position duration
Despite operating on a low timeframe 15-minute price series, the model does not exhibit the characteristics of high-frequency scalping. The average position holding time is 28.36 hours, with a median holding duration of 19.50 hours. This confirms that individual positions typically remain open for nearly a full calendar day, capturing intra-day to multi-day swing moves rather than transient micro-structure noise. Trade generation occurs at a measured frequency: the strategy averages 67.08 trades per year, corresponding to approximately 5.68 completed positions per active month. Exits are spaced out across historical execution windows, displaying an average interval between trade closures of 5.46 days and a median exit interval of 3.47 days. This temporal spacing indicates that the algorithm spends considerable time out of the market or holding single positions patiently, allowing price movement to develop over multiple daily cycles before triggering exit criteria.
3. Quality of historical results
The operational efficiency of the system is highlighted by a high hit rate combined with favorable trade asymmetry. Out of 318 completed operations, 239 resulted in gains, representing a win rate of 75.16%, while 79 trades closed at a loss. The system recorded a profit factor of 5.00, meaning gross generated gains were five times larger than gross accumulated losses. The average trade gain across all closed positions stands at +2.37%, while the median trade metric is +3.00%. A key statistical takeaway is the structural distribution of returns: the best historical trade produced a gain of +17.71%, whereas the top three largest winning trades collectively accounted for only 4.27% of total gross profit. This remarkably low concentration percentage proves that historical performance was not driven by reliance on a few extreme tail-risk outliers, but rather by the consistent accumulation of moderate, repeated winning trades.
4. Risk, drawdown and losing behavior
Risk characteristics present a clear portrait of structural boundary parameters within the algorithm's execution profile. The strategy recorded a maximum peak-to-trough drawdown of 29.86%. Notably, this maximum historical drawdown figure exactly matches the strategy's single worst trade loss of -29.86%, indicating that the most severe equity contraction experienced during the 4.74-year evaluation period stemmed from a single uncharacteristic adverse trade event rather than a compounding series of successive failures. The system demonstrated strong streak resistance, recording a maximum consecutive losing streak of 5 trades compared to a maximum winning streak of 19 consecutive profitable trades. While the overall win rate of 75.16% provides robust equity recovery capabilities, the presence of an extreme -29.86% single-trade loss highlights the necessity of managing tail risk and understanding that high win rates can occasionally co-exist with sharp, localized drawdown spikes.
5. Behavior through time and yearly stability
Analyzing annual performance metrics reveals structural adaptability across varying market conditions from 2021 through 2026. In late 2021, the strategy executed 15 trades with a 66.67% win rate, yielding a cumulative return sum of +24.22%. During 2022, a period marked by broad crypto market declines, the strategy expanded its trading activity to 70 trades, posting a 71.43% win rate and a cumulative return sum of +174.44%. In 2023, trade frequency moderated to 39 trades, but efficiency increased sharply to an 84.62% win rate (+136.03% sum). The calendar year 2024 represented the peak operational output, recording 88 trades with an 84.09% win rate and a cumulative sum of +275.66%. In 2025, performance remained solid across 81 trades with a 70.37% win rate (+107.92% sum), while the partial 2026 data window logged 25 trades with a 60.00% win rate (+35.89% sum). Performance is broadly distributed across multiple years rather than concentrated in a single isolated period.
6. Recent period since 2024-06-01 versus full history
Evaluating recent execution statistics from June 1, 2024, through August 19, 2026, provides an important validation window for model persistence. Out of the 318 total historical trades, 159 completed trades—exactly 50.0% of the entire historical dataset—occurred within this recent window. During this modern period, the strategy generated a cumulative return sum of 301.16%. Comparing this to the long-term historical baseline demonstrates that the model's trade frequency and return generation accelerated significantly during the recent sample. The strong performance in the post-June 2024 window confirms that the algorithm's statistical edge has remained active and highly responsive to contemporary market volatility rather than suffering from alpha degradation or regime breakdown.
7. Strengths and limitations
The principal analytical strengths of this strategy include a high baseline win rate (75.16%), an exceptional profit factor of 5.00, and a healthy profit distribution where the top three trades contribute less than 5% of total gross gains. Furthermore, the extensive sample of 159 recent trades confirms ongoing statistical relevance. Conversely, the model exhibits explicit structural limitations. The primary risk factor lies in its worst-case loss metric of -29.86%, which matches the maximum historical drawdown and indicates that stop-loss execution or adverse slippage in low-liquidity conditions could impact equity curves. Additionally, while annual returns remained positive throughout every calendar year evaluated, the win rate in 2026 dropped to 60.00%, illustrating that short-term win rates naturally fluctuate around the long-term mean.
8. DevioLab analytical conclusion
With a DevioLab Score of 80.58 and the rank of #1 for the JASMY ticker, this 15-minute algorithmic model represents a highly effective quantitative framework. The empirical data demonstrate a strategy built upon high win probability, patient holding durations averaging nearly 28.4 hours, and well-distributed returns that avoid reliance on single-event windfalls. Traders analyzing this model should weigh its robust profit factor of 5.00 against the reality of its -29.86% peak single-trade risk profile. Overall, the statistical evidence across both full-history and recent sub-samples confirms a mature strategy architecture with strong historical persistence.
9. Data scope and methodology
This analysis is based entirely on simulated backtest statistics for the JASMY 215000 +49320.96% 1TRAD-UFM6 model operating on 15-minute price data between November 22, 2021, and August 19, 2026. All metrics, including trade counts, holding times, win rates, and return percentages, reflect closed transaction calculations from historical price sequences. The start date of November 22, 2021, marks the beginning of the strategy backtest dataset and does not indicate the underlying asset's creation or listing date. Percentage gains represent unleveraged sum calculations across closed trades and do not model order execution slippage, exchange fee structures, or dynamic position sizing. Historical simulated performance provides statistical context for research purposes and does not guarantee future operational results.