DPZ · Domino's Pizza, Inc.
Stock Universe · globaler AktienmarktDPZ Quantitative Strategy Analysis: High-Precision Swing Logic Achieving an 88.57% Win Rate and 8.75 Profit Factor
An in-depth quantitative examination of the top-ranked algorithmic trading strategy for DPZ stock reveals a selective, multi-day swing architecture. Operating across a 7.04-year evaluation window from September 2019 to September 2026, the strategy generated a cumulative historical return of 2,342.82% with an annualized return of 184.87%. Characterized by a high win rate of 88.57% across 70 completed trades, an exceptional profit factor of 8.75, and a tightly constrained maximum drawdown of 8.83%, the strategy demonstrates robust statistical balance. The logic exhibits low profit concentration, with its top three winning trades contributing just 18.87% of gross profits, confirming that performance is driven by consistent execution rather than isolated outliers. Designed for automated trading bots and algorithmic trading agents, this model trades infrequently, holding positions for an average of 10.8 days while maintaining a maximum historical losing streak of just one trade.
Analyse vom 21.10.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The algorithmic trading strategy designated for DPZ stock represents a highly refined quantitative model built on a 15-minute price evaluation interval. Evaluated over a comprehensive 7.04-year historical backtest spanning from September 16, 2019, to September 29, 2026, the strategy achieved a DevioLab score of 90.18, securing the number one rank among evaluated models for this ticker. Over the full history, the strategy produced a total simulated net return of 2,342.82%, translating to an annualized return metric of 184.87%. The strategy is engineered to function as the decision engine for automated trading bots, trading robots, and algorithmic trading agents, providing systematic entry and exit signals without discretionary interference. Rather than reacting to noise on short timeframes, the strategy leverages the 15-minute interval to pinpoint execution timing while capturing macro swing movements in DPZ stock. The resulting profile combines extreme selectivity with asymmetrical payout dynamics, minimizing market exposure while maximizing trade efficiency.
Trading rhythm and position duration
Despite utilizing 15-minute bar data for signal generation, the strategy operates as a multi-day swing model with an exceptionally measured trading frequency. Over the 7.04-year sample, the strategy completed exactly 70 trades, translating to an average of 9.95 trades per year, or approximately 1.21 trades per active month. Position duration data confirms that the strategy avoids short-term churning. The average holding period per position is 259.30 hours (approximately 10.8 days), with a median holding time of 216.00 hours (9.0 days). In terms of exit cadence, the average time between completed trades stands at 36.68 days, while the median gap between trade exits is 27.74 days. This reveals a rhythm characterized by prolonged periods of remaining in cash or holding winning positions through multi-week price swings. The extended holding duration relative to the low trade count demonstrates that the strategy's automated trading agent logic filters out minor market fluctuations, deploying capital only when high-conviction structural conditions align on the DPZ chart.
Quality of historical results
The quality of historical performance for this DPZ algorithm is defined by superior hit-rate performance paired with robust payoff metrics. Out of 70 completed trades, 62 ended in profit while only 8 resulted in losses, establishing a win rate of 88.57%. The strategy achieved a overall profit factor of 8.75, meaning gross profits exceeded gross losses by nearly ninefold. The average return per completed trade reached +4.96%, closely aligned with the median trade return of +4.03%, indicating a reliable and symmetrical baseline return distribution. The maximum single trade gain was +37.98%, whereas the single worst trade produced a loss of -8.83%. Crucially, the top three winning trades accounted for only 18.87% of total gross profit. In quantitative research, a low top-three profit concentration confirms that the strategy's equity growth was driven by widespread, repeated trade gains rather than reliance on a few lucky outliers. This structural breadth reinforces the reliability of the underlying signal logic when executed via automated trading bots.
Risk, drawdown and losing behavior
Risk containment is one of the strategy's most prominent statistical features. The historical maximum drawdown recorded over the entire 7.04-year period was 8.83%, which precisely matches the loss observed in its worst individual trade (-8.83%). This equivalence indicates that drawdowns were driven almost entirely by single isolated trade exits rather than compounding series of open or closed losses. The strategy's risk profile is further illuminated by its streak metrics: the longest consecutive winning streak reached 16 trades, whereas the longest consecutive losing streak was exactly 1 trade. Across all 70 completed transactions, the strategy never experienced back-to-back losing trades. This absence of loss clustering suggests that the entry criteria effectively insulate the portfolio from prolonged adverse market regimes. For automated trading robots operating in live environments, such controlled drawdown characteristics reduce capital stress and margin demands, providing a stable equity curve during market pullbacks.
Behavior through time and yearly stability
An examination of the yearly breakdown shows consistent profitability across distinct market regimes from 2019 through 2026. In 2019, the strategy executed 2 trades, winning both for a sum of +23.89%. In 2020, trade activity expanded to 13 trades with a 100% win rate, yielding a net sum of +107.78%. The year 2021 delivered 10 trades with a 90.00% win rate (+32.88% sum), followed by 2022 with 13 trades, an 84.62% win rate, and a net sum of +47.08%. Activity moderated in 2023 to 5 trades (80.00% win rate, +17.74% sum), before rebounding in 2024 with 6 trades, a 100% win rate, and +44.77% cumulative gain. In 2025, 13 trades were logged with an 84.62% win rate (+51.20% sum), and the partial year 2026 registered 8 trades with a 75.00% win rate (+21.73% sum). Every single calendar year closed with positive net gains and win rates between 75.00% and 100.00%, illustrating remarkable temporal stability across both bull and bear market cycles in DPZ stock.
Recent period since 2024-06-01 versus full history
Evaluating the recent performance window from June 1, 2024, to September 29, 2026, provides valuable insight into the strategy's contemporary edge. During this 2.3-year sub-period, the strategy executed 26 completed trades, accumulating a combined return sum of +106.84%. This represents 37.14% of all historical trades completed within roughly 33% of the total backtest timeframe, indicating a higher density of signal generation in recent years compared to earlier periods like 2019 and 2023. The recent window achieved an annualized performance pace that closely mirrors the long-term baseline. Importantly, the strategy demonstrated no signs of alpha decay or performance degradation during recent market shifts. The ongoing generation of positive closed trades during this window confirms that the automated trading agent's underlying logic remains well-adapted to recent volatility and liquidity patterns in DPZ stock.
Strengths and limitations
The primary strengths of this strategy lie in its exceptional historical win rate (88.57%), high profit factor (8.75), and tightly bounded risk exposure with a maximum drawdown of 8.83%. The low profit concentration (18.87% in top 3 trades) and single-trade maximum losing streak demonstrate exceptional distribution balance and capital protection. However, meaningful quantitative limitations exist. With only 70 completed trades across more than seven years, the statistical sample size is relatively small, meaning confidence intervals around specific metrics remain broader than in high-frequency models. Additionally, the average position holding time of 10.8 days requires trading bots and operators to endure multi-week market exposure, exposing capital to overnight gap risk and corporate earnings events inherent in individual stock equities like DPZ.
DevioLab analytical conclusion
Ranked number one for DPZ with a DevioLab score of 90.18, this algorithmic strategy represents an exceptionally high-quality quantitative model for trading DPZ stock. Its combination of a 2,342.82% overall cumulative gain, an 88.57% win rate, and an 8.83% maximum drawdown reflects a disciplined, low-turnover approach that effectively captures larger directional swings while minimizing whipsaw losses. The logic's structural resistance to consecutive losses and balanced profit distribution make it a strong candidate for deployment within automated trading agents and algorithmic execution frameworks. While low trade frequency demands patience and discipline, the historical statistics confirm that the strategy's selective filter successfully extracts high-probability trading opportunities from 15-minute price data.
Data scope and methodology
This analysis is based on historical simulated backtest data for DPZ stock over the period from September 16, 2019, to September 29, 2026. The strategy executes on a 15-minute price interval. All reported percentage gains, trade counts, drawdown figures, and duration metrics are derived from closed trade evaluations within the simulated environment. Results do not reflect real-time live trading, actual brokerage account fills, slippage, borrowing fees, or transaction costs. Historical simulation results are presented solely for quantitative research purposes and do not guarantee future performance or live trading profitability.