DOV · Dover Corporation
Stock Universe · marché actions mondialDOV · DOV Quantitative Strategy Analysis: A High-Precision 15-Minute Algorithmic Model Combining Multi-Week Holding Times with Low Drawdown
This quantitative research article provides an in-depth evaluation of the core balanced algorithmic trading strategy for DOV stock on the 15-minute timeframe. Spanning 6.87 years of simulated historical history from September 2019 through July 2026, the strategy generated a cumulative closed-trade return of +1924.87% across 49 completed trades. Defined by an exceptionally high win rate of 89.8% (44 winning trades against 5 losses) and a profit factor of 8.75, the model exhibits unusual structural stability. Risk containment is a defining feature, with historical maximum drawdown limited to 5.35%, matching the single worst trade loss of -5.35%. Despite evaluating price action on a fine 15-minute bar, the strategy operates as a long-horizon position trading model, averaging 732.78 hours (approximately 30.5 days) per position and generating roughly 7.13 trades per year. Profit distribution across winners remains well balanced, with the top three winning trades accounting for 24.62% of gross profit. Evaluated across eight separate calendar years, every single year yielded positive net returns, including perfect 100% win rates in five distinct years. Earning a DevioLab Score of 84.51, this strategy ranks second among quantitative models evaluated for DOV, offering an empirical blueprint for execution via automated trading bots and trading agents.
Analyse au 20.10.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
Strategy profile
The quantitative trading model for DOV operates on a 15-minute execution interval while exhibiting macro position-holding characteristics. Designated under DevioLab's selection criteria as a core balanced optimal strategy (selection reason core1_balanced_best), it achieves a DevioLab Score of 84.51, placing it second among all evaluated quantitative strategies for DOV stock. Over an extended backtest sample of 6.87 years—spanning September 16, 2019 to July 31, 2026—the model executed 49 completed trades, securing 44 winning outcomes against just 5 losses. This translates to a high overall win rate of 89.8%. Cumulative closed-trade performance reached +1924.87%, representing an annualized baseline return metric of 126.69%. The framework is designed to function as the systematic execution engine for automated trading bots, trading robots, and quantitative trading agents seeking disciplined equity exposure without requiring frequent market intervention.
Trading rhythm and position duration
Although the strategy samples price data on a 15-minute interval, its execution rhythm is deliberate and low-frequency. The model completed 49 trades across nearly seven years, averaging 7.13 trades per year or approximately 1.14 trades per active month. Position exits are separated by substantial temporal gaps, with an average of 51.33 days and a median of 40.85 days between trade conclusions. Once a position is opened, the average holding duration extends to 732.78 hours, which equates to roughly 30.5 calendar days. The median holding time stands at 359.25 hours (approximately 15.0 days), demonstrating that while some positions resolve within two weeks, others remain open for significantly longer periods to capture multi-week trend continuations. This structural gap between 15-minute chart granularity and multi-week position duration suggests the strategy uses intra-day data primarily to refine entry timing while ignoring minor noise during the trade lifecycle.
Quality of historical results
The quality of performance metrics reflects a combination of high accuracy and solid payoff characteristics. The strategy achieved an exceptional profit factor of 8.75, indicating that gross profits exceeded gross losses by nearly ninefold. The average trade performance stands at +6.87%, while the median trade yield reached +5.10%. The single best trade produced a gain of +29.63%, whereas the worst trade registered a loss of -5.35%. A critical metric in evaluating profit quality is gross profit concentration: the top three winning trades combined generated 24.62% of the strategy's total gross profits. This relatively modest concentration ratio demonstrates that historical net profitability was not dependent on a few random windfall trades, but rather built upon a consistent distribution of moderate winning outcomes across the 44 successful trade events.
Risk, drawdown and losing behavior
Risk metrics across the sample period reveal tight downside containment. The maximum historical drawdown experienced by the portfolio was capped at 5.35%. Notably, this maximum drawdown figure matches the strategy's single worst trade loss of -5.35%. The alignment between maximum drawdown and worst trade magnitude demonstrates that severe equity curve compounding of losses was historically absent. Across 49 completed trades, the strategy experienced a maximum losing streak of just 2 consecutive trades, compared to a maximum winning streak of 17 consecutive trades. With only 5 losing trades recorded over the entire 6.87-year period, equity retracements were brief and shallow. This controlled drawdown profile reduces capital strain for automated trading bots operating under strict equity protection thresholds.
Behavior through time and yearly stability
A year-by-year examination demonstrates continuous stability across differing market environments from 2019 through 2026. Every calendar year in the sample achieved positive aggregate returns. In 2019, the model logged 1 trade with a 100% win rate and a sum of +13.88%. Activity peaked in 2020 with 7 trades (6 wins, 1 loss, 85.71% win rate) producing +81.15%. In 2021, 5 trades yielded +49.28% with a 100% win rate. The strategy faced higher activity in 2022, completing 12 trades (10 wins, 2 losses, 83.33% win rate) to accumulate +54.93%. In 2023, 7 trades (5 wins, 2 losses, 71.43% win rate) generated +21.12%. Performance maintained its consistency into the subsequent years: 2024 delivered 5 trades (100% win rate) for +42.82%, 2025 logged 6 trades (100% win rate) for +19.62%, and the partial year 2026 contributed 6 trades (100% win rate) totaling +53.77%. In five out of eight calendar years, the strategy achieved a 100% win rate, showing robust temporal resilience.
Recent period since 2024-06-01 versus full history
Analyzing the recent trading performance beginning June 1, 2024 provides insight into modern structural behavior. In this recent window, the strategy completed 15 trades, accumulating a sum of +92.50% in closed-trade returns. This equates to roughly 30.6% of the strategy's total historical trade count occurring over approximately 2.16 years, reflecting a trade frequency consistent with its full-history average of 7.13 trades per year. Comparing recent yield density against full history indicates that the strategy's edge has remained active, contributing meaningfully to overall returns without degradation in trade frequency or hit rate during recent market regimes.
Strengths and limitations
The primary strength of this quantitative model lies in its combination of a high win rate (89.8%), robust profit factor (8.75), and minimal drawdown (5.35%). Broad profit distribution—where the top three winners account for less than a quarter of gross profits—validates the statistical breadth of its edge across multiple years. However, clear limitations exist within the data profile. The strategy generated only 49 completed trades over nearly seven years, presenting a relatively small statistical sample size. Furthermore, position holding times averaging over 30 days mean that capital is committed for extended durations, requiring patience from operators of automated trading agents. Traders utilizing this logic must accept prolonged periods between trade exits and low overall deal flow.
DevioLab analytical conclusion
With a DevioLab Score of 84.51 and a rank of #2 for DOV stock, this core balanced strategy represents a stable quantitative framework. Its historical ability to maintain positive net returns across all eight calendar years, combined with a maximum drawdown kept to 5.35%, underscores disciplined risk management. The model is well-suited for inclusion in automated trading systems, trading robots, or algorithmic portfolios that prioritize high win rates and shallow drawdown over high-frequency turnover. Nevertheless, market participants should remember that past backtested performance across simulated trades provides useful statistical insight but cannot guarantee identical results in future live execution.
Data scope and methodology
The results presented in this analysis are derived from backtested, closed-trade historical simulations for DOV stock spanning from September 16, 2019 to July 31, 2026 on a 15-minute price bar interval. All metric calculations—including cumulative profits (+1924.87%), trade counts (49 completed), holding times, and drawdown figures—are evaluated on completed positions without leverage, compounding equity adjustments, or deductions for broker commissions and execution slippage. This research serves purely as an analytical review of historical quantitative strategy design and does not constitute financial or investment advice.