DCRUSDT
Crypto market · BinanceQuantitative Strategy Analysis for DCR · DCR: Evaluating the 15-Minute Model
A detailed quantitative evaluation of the DCR 215000 +283291.17% 1TRAD-ULM9 trading strategy configured for the 15-minute timeframe on DCR. Holding a DevioLab score of 49.73 and ranking 4th among evaluated models for this asset, the algorithm pairs a high win rate of 72.73% and a profit factor of 7.86 across a 6.03-year historical dataset against a substantial maximum drawdown of 61.82%.
Analysis as of 13.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The DCR 215000 +283291.17% 1TRAD-ULM9 quantitative algorithm operates as a non-core system designed for spot-style trading on DCR, utilizing 15-minute candlestick structures. Based on DevioLab performance metrics, the strategy achieves a score of 49.73, placing it 4th among tested models for DCR. Over a dataset spanning 6.03 years from July 30, 2020, to August 11, 2026, the strategy generated an overall historical return of 247540.16%, corresponding to an annualized metric of 1110.5%.
Trading rhythm and position duration
Across the entire 6.03-year historical window, the algorithm executed 110 completed trades, translating to an average frequency of 18.23 trades per year. Despite taking signals from a 15-minute chart resolution, the low yearly transaction volume demonstrates that the entry mechanism is highly selective rather than high-frequency or scalping-oriented. Exact holding hours and duration between exits are not recorded in this dataset, but the modest trade frequency highlights extended periods of market observation between triggers.
Quality of historical results
The strategy recorded 80 winning trades against 30 losing trades, achieving a win rate of 72.73%. The average return per trade stands at 10.57%, closely aligned with a median trade return of 9.43%, indicating a well-distributed performance profile without excessive dependence on single outliers. The profit factor reached 7.86. The best individual trade delivered a gain of 178.08%, while the top three winning positions accounted for 22.46% of total gross profit.
Risk, drawdown and losing behavior
Historical risk parameters reveal significant equity curve volatility. The maximum drawdown reached 61.82%, and the single worst losing trade resulted in a loss of -58.16%. However, the model demonstrated resilience against consecutive drawdowns, capping its longest losing streak at 3 trades, compared to a longest winning streak of 12 trades. The elevated win rate provides a statistical cushion, but a peak-to-trough decline exceeding 60% underscores substantial equity risk.
Behavior through time and yearly stability
Over the full 6.03-year backtest horizon, the model accumulated a return of 247540.16%. Detailed yearly breakdown figures are not present in this dataset; however, the long-term baseline of 18.23 trades per year reflects a patient entry methodology. The strategy aims to capture major price expansions on DCR while remaining idle during lower-conviction market regimes.
Strengths and limitations
Key strengths include an exceptional profit factor of 7.86, a solid win rate of 72.73%, a short maximum losing streak of 3 trades, and a well-balanced profit structure where the top three trades represent only 22.46% of gross gains. Primary limitations stem from a steep maximum drawdown of 61.82%, a severe single-trade loss of -58.16%, a modest overall sample size of 110 trades, and a complete lack of trading activity since June 2024.
DevioLab analytical conclusion
With a DevioLab score of 49.73 and a rank of 4th for DCR, the strategy demonstrates strong historical trade efficiency paired with high drawdown exposure. The high win rate and robust profit factor highlight effective setup filtering, though the recent multi-month dormancy and substantial peak-to-trough risk temper its overall analytical standing.
Data scope and methodology
This analysis relies on historical 15-minute spot data for DCR covering the period from July 30, 2020, to August 11, 2026 (6.03 years). All cited statistics represent simulated historical trade outcomes. They serve analytical research purposes only, do not represent live Binance trading account returns, and do not guarantee future performance.