DECK · Deckers Outdoor Corporation
Stock Universe · mercado bursátil globalQuantitative Analysis of DECK · DECK Algorithmic Swing Trading Strategy 0 +11475.61% 1TRAD-NVW6
This analytical study provides a comprehensive quantitative evaluation of the automated trading logic engineered for DECK (DECK) on a 15-minute execution interval. Over a backtested timeline spanning 7.06 years from September 2019 through October 2026, the strategy generated 107 closed trade events, achieving an exceptional win rate of 80.37 percent alongside a profit factor of 4.38. With a maximum historical drawdown capped at 16.55 percent and a top-three winner gross profit contribution of just 17.13 percent, the statistical distribution demonstrates remarkable structural balance rather than reliance on single outlier windfalls. Designed as decision logic for execution via automated trading bots or quantitative trading agents, the model maintains a selective swing-trading rhythm with an average holding period of 274.79 hours (approximately 11.45 days) and a modest frequency of 15.16 trades per year. The strategy currently holds the rank 1 position for the DECK ticker within the DevioLab framework with a quantitative score of 82.69.
Análisis al 17.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
1. Strategy profile
The quantitative model designated as DECK 0 +11475.61% 1TRAD-NVW6 is a specialized, medium-horizon algorithmic trading strategy operating on the 15-minute timeframe for DECK (DECK). Across a multi-year historical backtest covering 7.06 years between September 16, 2019, and October 6, 2026, the strategy registered 107 completed trades. Out of these closed positions, 86 yielded positive closed performance while 21 resulted in net losses, establishing an overall hit rate of 80.37 percent. The strategy achieves an overall DevioLab score of 82.69, ranking first among all evaluated algorithmic models for the DECK ticker. The strategy logic is specifically structured to function as the core signal engine for execution infrastructure such as automated trading bots, algorithmic trading robots, and autonomous trading agents. Rather than engaging in hyper-active market participation, the algorithm filters market noise on the 15-minute chart to identify structural price movements. The total cumulative sum of closed trade gains across the full evaluation window reached 11,475.61 percent, which translates to an annualized return metric of 232.73 percent. With a profit factor of 4.38, the algorithm demonstrates a robust statistical edge where total historical gross profits substantially outweigh historical gross losses.
2. Trading rhythm and position duration
Analyzing the temporal dynamics of the strategy reveals a distinct swing-trading profile rather than an intraday scalping approach. Despite sampling data on a 15-minute bar frequency, the algorithm executes with high restraint, generating an average of 15.16 completed trades per year, or approximately 1.67 trades per active month. The time elapsed between trade exits averages 23.02 days, with a median spacing of 18.58 days. This indicates that trade generation occurs in measured, periodic waves rather than continuous exposure. In terms of position holding time, trades remain open for an average duration of 274.79 hours, which equals roughly 11.45 calendar days. The median holding duration is slightly shorter at 167.75 hours, or approximately 6.99 days. The gap between the median and mean holding times indicates that while the majority of positions resolve within a week, select swing setups extend into multi-week holdings to capture sustained momentum. For automated trading bots and trading agents, this selective pacing dramatically reduces exposure to transient micro-structure noise while preserving capital for high-conviction setup configurations.
3. Quality of historical results
The quality and structural health of a quantitative strategy depend heavily on how historical profitability is distributed across its trade population. The model recorded an average return per completed trade of +5.01 percent, closely aligned with its median trade return of +4.11 percent. The proximity of the mean to the median is a strong statistical indicator of consistent performance, showing that overall returns are driven by a broad cohort of typical trades rather than skewed by extreme positive anomalies. The strategy's single best trade recorded a gain of +43.80 percent, whereas its single worst trade registered a loss of -14.30 percent. Crucially, the top three winning trades collectively account for only 17.13 percent of total gross profit. In quantitative research, a low profit concentration ratio—typically below 20 percent—is a hallmark of institutional structural validity. It proves that the strategy's high profit factor of 4.38 is supported by systemic edge across dozens of trades over several market cycles, rather than depending on a handful of isolated, unrepeatable market surges.
4. Risk, drawdown and losing behavior
Managing downside exposure is critical for long-term quantitative capital preservation. Over its complete 7.06-year test history, the strategy experienced a maximum equity drawdown of 16.55 percent. When evaluated alongside a cumulative return metric exceeding 11,000 percent, this drawdown profile demonstrates notable downside control. The strategy achieved a maximum winning streak of 12 consecutive positive trades, while its maximum losing streak was contained to just 2 consecutive adverse trades. The restriction of losing runs to a maximum of two trades indicates that the algorithm's filtering logic successfully disengages or stays out of adverse market environments before compounding losses occur. The worst single trade loss of -14.30 percent represents a controlled downside event relative to the average positive swing potential. This constrained drawdown environment makes the model well-suited for deployment via automated trading robots, where minimizing deep drawdown equity curves is essential for sustaining risk-adjusted stability.
5. Behavior through time and yearly stability
A granular examination of the yearly performance breakdown illustrates how the strategy adapted across varying market regimes from 2020 through late 2026. In 2020, the algorithm completed 7 trades with a perfect 100 percent win rate, yielding a combined return of +136.86 percent. Activity increased in 2021 with 16 trades resulting in a 75.00 percent win rate and +65.13 percent total return. Performance strengthened further in 2022, a historically challenging year for broad stock indices, where the strategy logged 19 trades, an 84.21 percent win rate, and +107.06 percent in summed gains. In 2023, the system produced 13 trades with a 92.31 percent win rate (+66.77 percent), followed by 2024 with 18 trades, an 88.89 percent win rate, and +81.38 percent return. In 2025 and 2026, trade output remained steady at 17 trades per year, though win rates moderated to 64.71 percent (+41.96 percent) and 70.59 percent (+36.69 percent), respectively. Across all seven annual periods, the strategy maintained a positive net sum of returns, proving consistent profitability across both expansionary and contractionary market cycles.
6. Recent period since 2024-06-01 versus full history
Evaluating recent historical performance from June 1, 2024, through the dataset end date of October 6, 2026, provides a clear view of current strategy efficacy. During this recent window of approximately 2.3 years, the strategy executed 44 completed trades, generating a cumulative return sum of +137.09 percent. This represents approximately 41.1 percent of all 107 total backtest trades, showing an increase in trading frequency relative to the earlier years of the dataset. The strategy's ability to maintain a strong positive yield (+137.09 percent over 44 trades) during recent market conditions confirms that its underlying signal logic has retained operational validity rather than decaying over time. Comparing this recent window against the full 7.06-year baseline shows that the algorithm continues to capture recurring price structures on DECK without requiring parameter re-optimization or structural overhaul.
7. Strengths and limitations
The primary structural strengths of this quantitative strategy lie in its high historical win rate of 80.37 percent, its low top-three profit concentration of 17.13 percent, and its contained maximum drawdown of 16.55 percent. The high profit factor of 4.38 combined with a maximum losing streak of only 2 trades highlights effective risk management and high signal accuracy. Furthermore, the moderate holding duration of 274.79 hours shields automated trading agents from unnecessary high-frequency turnover while remaining nimble enough to capture multi-day trends. Conversely, a primary limitation is the modest overall trade sample size of 107 closed trades over 7.06 years. While 107 trades offer sufficient statistical relevance for swing strategies, the low annualized frequency of 15.16 trades means statistical conclusions require longer time horizons to manifest in live execution. Additionally, live execution via trading bots must account for market friction, gap risk during overnight sessions, and potential execution slippage during volatile earnings announcements.
8. DevioLab analytical conclusion
The quantitative evaluation of DECK 0 +11475.61% 1TRAD-NVW6 highlights a highly disciplined, risk-conscious swing trading algorithm for DECK stock on the 15-minute bar interval. Holding the top rank for the asset with a DevioLab score of 82.69, the strategy stands out for its high win rate (80.37 percent), stable return distribution, and constrained drawdown profile (16.55 percent). Rather than relying on sporadic, massive market spikes, the strategy delivers steady, broad-based returns across multiple years and varying market environments. For quantitative developers and traders seeking decision logic for automated trading bots, trading robots, or algorithmic execution models, the strategy presents a statistically sound foundation characterized by strong payoff metrics, minimal consecutive loss clusters, and consistent recent historical performance.
9. Data scope and methodology
All metrics and statistical figures presented in this research paper are derived directly from backtested closed trade logs for DECK stock on a 15-minute execution interval from September 16, 2019, through October 6, 2026. The dataset encompasses 107 completed trade events. The recent performance sub-sample includes all trades with exit timestamps on or after June 1, 2024. Returns reflect simulated closed trade percentages and do not account for external transaction fees, commission structures, or potential execution slippage. Historical backtested results are intended solely for quantitative analysis and algorithmic strategy research; they do not represent guaranteed future performance or personalized financial advice.