ZKUSDT
Mercado cripto · BinanceQuantitative Strategy Analysis: ZK · ZK 15-Minute Model Features an 89.83% Win Rate and 3,385.18% Historical Yield
This quantitative research report evaluates the performance metrics of the rank-1 strategy for ZK on the 15-minute chart, designated internally as ZK 215000 +3385.18% 1TRAD-AXS3. Operating over a 2.18-year backtested historical sample from June 17, 2024, to August 21, 2026, the model generated 59 completed trades with a cumulative return of 3,385.18% and a profit factor of 2.50. The strategy exhibits an exceptionally high win rate of 89.83% across 53 winning trades and 6 losing trades, supported by an unusually balanced profit distribution where the top three winning trades account for only 19.89% of gross profits. However, the system exhibits an asymmetric loss profile, with a worst single trade loss of -22.00% contributing to a maximum historical drawdown of 23.12%. With an average trade gain of +6.92% closely tracking a median trade gain of +6.93%, the system demonstrates consistent trade-level performance, though its low trade frequency of 27.10 trades per year requires careful statistical consideration regarding sample size and execution consistency.
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Strategy profile
The quantitative trading strategy analyzed in this report targets the ZK asset on a 15-minute timeframe within the cryptocurrency market. Designated as a core balanced strategy with a selection status of core1_balanced_best, the model has achieved the top rank (rank 1) for the ZK token with a DevioLab score of 71.52. Over a backtested evaluation window spanning 2.18 years from June 17, 2024, to August 21, 2026, the strategy generated a total cumulative return of 3,385.18% across 59 completed trades. The system operates with selective execution logic, translating into an average frequency of 27.10 trades per year. Out of the 59 total trades executed, 53 resulted in profitable exits while 6 closed in losses, establishing an overall win rate of 89.83%. The profit factor stands at 2.50, signaling that gross profits were two and a half times larger than gross losses over the entire sample period. These historical metrics provide a structured quantitative baseline, reflecting simulated closed-trade performance rather than live account returns or future guarantees.
Trading rhythm and position duration
Despite operating on a low-timeframe 15-minute chart, the strategy exhibits a highly restrained execution frequency. Generating approximately 27.10 trades per year, the model completes roughly two to three trades per month on average. This low transaction frequency indicates that the strategy's signal conditions are strict, filtering out the vast majority of short-term market noise on the 15-minute timeframe to capture specific high-probability structural setups. Specific holding duration metrics, including average holding hours, median holding hours, and days between exits, are unpopulated in the primary dataset. Consequently, while position duration cannot be measured in explicit hours, the combination of a 15-minute bar interval and an annual frequency of 27.10 trades implies that trades are not executed in rapid scalping sequences. Instead, positions are established selectively and held through complete price swings before exiting. The modest trade count of 59 over more than two years highlights a patient systematic rhythm that prioritizes signal quality over trade volume.
Quality of historical results
The statistical distribution of closed trade outcomes reveals remarkable internal symmetry alongside a high win rate. The strategy achieved an average trade return of +6.92% and a median trade return of +6.93%. The virtually non-existent gap between the mean and median returns demonstrates that historical profitability was not driven by extreme right-tail outliers or skewed by a handful of anomalous trades. Further supporting this structural consistency, the three largest winning trades generated 19.89% of total gross profit. In many systematic trading strategies, top winners frequently account for 40% to 60% of total gains, making performance vulnerable to missing a few critical moves. Here, less than one-fifth of gross profits came from the top three trades, confirming a broad distribution of performance across the 53 winning trades. The strategy's single best trade achieved a gain of +41.08%, while the worst trade logged a loss of -22.00%, framing the full historical payoff spectrum.
Risk, drawdown and losing behavior
While the strategy boasts an impressive 89.83% win rate and a profit factor of 2.50, its risk statistics reveal important structural trade-offs. The maximum historical drawdown reached 23.12%, occurring alongside a worst single trade loss of -22.00%. Because losing trades occurred infrequently—accounting for only 6 out of 59 total trades—the system maintained an exceptional longest winning streak of 42 consecutive trades, while its longest losing streak was capped at just 1 trade. However, the magnitude of the single worst trade (-22.00%) compared to the average winning trade (+6.92%) highlights an asymmetric tail risk profile. While 89.83% of trades close in profit, the occasional losing trade can erode the gains of roughly three average winning positions. The maximum drawdown of 23.12% reflects this dynamics, demonstrating that equity contractions in this system stem primarily from individual deep loss events rather than extended series of consecutive losing trades.
Behavior through time and yearly stability
Evaluating performance stability across distinct temporal regimes requires examining trade distributions over the entire 2.18-year historical history from June 17, 2024, through August 21, 2026. In the provided statistical scope, the yearly breakdown data array is empty, which limits direct annual sub-period comparisons of returns, trade counts, or win rates for individual calendar years. What can be established from the aggregate data is that the total cumulative gain of 3,385.18% was realized across 59 completed trades over the full 2.18-year period. On an annualized basis, this represents a trade delivery rate of 27.10 trades per year. Because individual yearly sub-breakdowns are unavailable in the source data, it cannot be proven whether trade generation was evenly distributed across 2024, 2025, and 2026, or whether historical market activity concentrated trade opportunities within specific high-volatility windows.
Strengths and limitations
The primary strength of this strategy lies in its high win rate of 89.83% paired with a profit factor of 2.50 and top-ranking performance (rank 1) for ZK on DevioLab. Its trade outcome profile is exceptionally consistent, as reflected by the near identical mean (+6.92%) and median (+6.93%) trade returns, alongside low reliance on outlier trades (top three winners represent only 19.89% of gross profit). The longest winning streak of 42 trades underscores the high frequency of positive trade closures. Conversely, the strategy's primary limitation is its asymmetric loss payoff structure. With a worst single trade loss of -22.00% against an average win of +6.92%, a rare adverse exit can impact cumulative returns significantly. Additionally, the total sample size of 59 trades over 2.18 years provides a modest statistical foundation, meaning individual trade outcomes carry higher relative statistical weight than they would in high-frequency models.
DevioLab analytical conclusion
With a DevioLab score of 71.52, the strategy stands as the highest-ranked core model for ZK in this backtested dataset. The quantitative analysis reveals a highly selective strategy that trades infrequently on the 15-minute timeframe (27.10 trades per year) while achieving an 89.83% success rate across closed positions. The strategy's mathematical structure is characterized by robust trade-level return symmetry and balanced profit distribution, avoiding heavy dependence on single windfall gains. However, quantitative analysts must consider the system's asymmetric downside risk, evidenced by a worst trade loss of -22.00% and a peak drawdown of 23.12%. Overall, the historical statistics reflect a highly effective setup for capturing multi-percent price moves on ZK, provided risk control mechanisms are understood relative to the strategy's rare but deeper exit losses.
Data scope and methodology
All figures presented in this study are derived from simulated backtest results for the ZK asset on a 15-minute timeframe covering the period from June 17, 2024, to August 21, 2026 (2.18 years). Returns are calculated strictly based on closed historical trades and do not represent live trading results, actual account equity curves, or direct Binance exchange returns. The calculations do not incorporate real-world execution factors such as order book slippage, variable exchange fee structures, latency, or funding rates. Metrics such as position holding hours and detailed yearly breakdowns were unpopulated in the source statistics and omitted from sub-period analysis to maintain absolute empirical fidelity. Past backtested performance is provided solely for quantitative research and educational evaluation and is not indicative of future results or investment advice.