GRAMUSDT
Mercado cripto · BinanceGRAM 15-Minute Quantitative Strategy Analysis: Flawless Win Rate and Zero Drawdown Across Compact Historical Window
This quantitative evaluation examines the rank-1 strategy for GRAM on the 15-minute timeframe, which achieved a 100% historical win rate and a cumulative return of +29.57% over a 0.16-year backtest window spanning July to August 2026. Generating a DevioLab Score of 71.065 under the core2_independent_best model, the algorithm completed three trades without experiencing a single losing position or drawdown. While the statistical results exhibit exceptional payoff metrics—including a profit factor of 9.17 and a median trade gain of +8.48%—the analysis highlights critical sample-size constraints, as the entire historical record comprises just three executed positions over approximately 58 days.
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Strategy profile
The quantitative strategy evaluated in this report operates on the 15-minute execution interval for GRAM within the cryptocurrency market. Listed under the technical identifier designation GRAM 215000 +29.57% 1TRAD-RBG7, the model holds the number-one rank for the GRAM ticker on DevioLab with a composite score of 71.065. It was selected as a top-tier system under the core2_independent_best model evaluation logic. The dataset covers a historical observation window from July 2, 2026, through August 29, 2026, representing a total active span of 0.16 years (approximately 58 calendar days). Over this timeframe, the strategy produced a cumulative net return of +29.57%, which matches its annualized return metric of +29.57% given the condensed sample duration. The strategy executed a total of three completed trades, all three of which reached positive exit targets, yielding a 100% win rate and zero losing trades. With an average trade gain of +5.53% and a maximum drawdown of 0.00%, the strategy demonstrates an unblemished short-term execution profile on the 15-minute timeframe.
Trading rhythm and position duration
Despite operating on a lower-timeframe 15-minute bar chart, the strategy displays a low-frequency, swing-oriented position management cadence. Over the 0.16-year backtest period, the model averaged 18.71 trades per year on an annualized basis, corresponding to an active execution rate of 1.50 trades per month. Completed exits occurred at an average and median spacing of 25.11 days apart, confirming long idle periods between trade liquidations. Position holding times reveal a structural skew in trade management. The average holding duration across all closed trades stands at 230.81 hours (approximately 9.6 days), whereas the median holding duration is significantly lower at 74.75 hours (approximately 3.1 days). This statistical divergence indicates that while two of the trades exited within a relatively brief multi-day holding window, a third position remained open for a substantially longer duration—approaching nearly three weeks—before fulfilling its exit criteria. The strategy utilizes the 15-minute bar structure not for rapid intraday scalping, but for precise entry timing within extended multi-day swing holding periods.
Quality of historical results
The qualitative statistical shape of the strategy's trades is defined by absolute hit-rate efficiency and positive expected return per trade. Across three completed trades, the model recorded three winning outcomes (100.0% win rate) and zero losses, yielding an overall profit factor of 9.17. Because no losing trades occurred to establish a traditional loss denominator, the profit factor calculation reflects high positive variance relative to historical gross gains. Individual trade return metrics demonstrate tight payoff clustering rather than reliance on a single hyper-outlier. The strategy achieved a best trade of +8.4806% and a median trade of +8.4800%, while its worst trade closed at a positive gain of +1.6324%. The average trade gain stands at +5.5322%. Mathematically, the proximity of the median (+8.48%) to the best trade (+8.48%) shows that two of the three executed trades achieved virtually identical maximum profit realization, while the third trade locked in a smaller gain of +1.63%. By definition, 100% of the strategy's gross profit is concentrated within its top three winning trades, given that three represents the entire population of completed executions.
Risk, drawdown and losing behavior
From a risk perspective, the historical backtest records a maximum drawdown of 0.00%, accompanied by a maximum losing streak of zero trades and a maximum winning streak of three trades. Because every position closed in positive territory, the equity curve exhibited monotonic upward steps without experiencing peak-to-trough drawdowns between trade exits. Quantitative analysis requires contextualizing this zero-drawdown profile within the limits of the statistical sample. The absence of drawdown is the direct mathematical result of experiencing zero losing trades across a small sample of three closed positions over 58 days. It does not indicate that the strategy possesses an intrinsic structural guarantee against downside risk. In broader market environments or during prolonged adverse volatility cycles, unobserved downside exposure could manifest. Nevertheless, within the tested window, the risk profile reflects clean capital protection and disciplined trade resolution.
Behavior through time and yearly stability
The temporal distribution of the strategy's historical data is localized entirely within the year 2026. According to the yearly breakdown, all three completed trades occurred between July 2, 2026, and August 29, 2026. During this 2026 operational window, the strategy generated three wins out of three attempts, accumulating a summed closed-trade return of +16.5966% across individual trade gains. Because the available backtest history spans less than two full months in 2026, multi-year cross-sectional stability cannot be measured. The dataset provides no evidence regarding how the algorithm performs across seasonal shifts, annual regime changes, or differing macro environments in prior years. The recorded stability is perfectly consistent within its single active year, but remains strictly constrained by the brief temporal window of the underlying dataset.
Recent period since 2024-06-01 versus full history
Evaluating recent performance since June 1, 2024, reveals that 100% of the strategy's historical trade history falls within this recent window. Because the backtest dataset begins on July 2, 2026, all three completed trades post-date the June 2024 boundary. During this recent post-June 2024 window, the strategy recorded three trades, three wins, zero losses, and a summed closed-trade return of +16.5966%. The full-history cumulative profit of +29.57% reflects compound equity growth and full strategy metrics over the 0.16-year tenure. Because the full history and the recent window coincide entirely, there is no divergence between legacy performance and recent execution. While this confirms that the strategy's entire track record reflects modern market conditions, it simultaneously underscores that the system lacks historical validation prior to mid-2026.
Strengths and limitations
The primary analytical strength of this strategy lies in its historical precision and execution efficiency. Achieving a 100% win rate, a 0.00% drawdown, a profit factor of 9.17, and a median trade gain of +8.48% across its active window earned the model the number-one rank for GRAM on DevioLab with a score of 71.065. Furthermore, position management successfully generated gains ranging from +1.63% to +8.48% without suffering equity degradation. The dominant limitation of the strategy is its minimal sample size. With only three completed trades over a 0.16-year (58-day) dataset, the statistical sample lacks sufficient trade frequency to establish robust statistical significance. Small-sample metrics, while mathematically flawless, carry higher statistical variance when projected forward. Additionally, the divergence between average holding time (230.81 hours) and median holding time (74.75 hours) indicates that position duration can stretch significantly depending on market conditions, requiring patient capital allocation.
DevioLab analytical conclusion
The GRAM 15-minute quantitative strategy represents a highly disciplined, low-frequency swing system optimized for capital preservation and high-probability exits. Ranking first for the GRAM asset with a DevioLab Score of 71.065, the strategy demonstrated perfect historical execution during its 58-day backtest window, turning three out of three trades into positive return events for a cumulative gain of +29.57%. From a quantitative perspective, the system delivers an exceptional historical risk-reward balance, characterized by zero drawdown and a median gain of +8.48% per successful trade. However, institutional analysis must treat the 100% win rate as a function of a compact 3-trade dataset rather than an unassailable perpetual edge. Future observational tracking should focus on how the strategy maintains its high-payoff structure as total completed trades expand across broader market regimes.
Data scope and methodology
This analysis is derived exclusively from historical backtested trade logs for the GRAM strategy on the 15-minute timeframe covering the period from July 2, 2026, to August 29, 2026. All statistics, including the DevioLab Score (71.065), trade counts (3 total completed trades), holding times, drawdowns, and return percentages, reflect closed-trade performance generated within a simulated quantitative framework. Performance metrics do not account for real-world execution friction such as exchange order routing, slippage, variable maker/taker fee schedules, or liquidity constraints. Historical simulated results do not guarantee or predict future performance. This evaluation is provided strictly for quantitative research and educational analysis and does not constitute financial or investment advice.