DGX · Quest Diagnostics Incorporated
Stock Universe · mercado global de açõesDGX · DGX Algorithmic Strategy Analysis: High-Win-Rate Position Holding Driven by Exceptional Payout Symmetry
This quantitative evaluation examines the top-ranked algorithmic trading strategy for DGX on DevioLab. Operating on a 15-minute chart resolution across a 7.09-year evaluation history from September 2019 to October 2026, the strategy achieved a 1,923.39% cumulative trade return across 101 completed cycles. Featuring an 80.20% win rate and a Profit Factor of 5.00, the system demonstrates structural stability, characterized by a nearly equal average (3.35%) and median (3.32%) return per trade, alongside minimal reliance on outlier profits (top three winners account for just 13.18% of gross profit). With an average holding time of 462.36 hours (19.26 days) and a modest execution frequency of 14.24 trades per year, this strategy offers an intriguing blueprint for automated trading bots and algorithmic trading agents designed for patient, multi-week position holding.
Análise em 18.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The algorithmic trading strategy for DGX · DGX, designated as a core candidate on DevioLab with a score of 85.46, holds the rank 1 position among all evaluated models for this stock ticker. Evaluated across 7.09 years of simulated market history between September 16, 2019, and October 18, 2026, the strategy generated 101 completed trade cycles. Rather than relying on high-frequency intraday noise, the strategy uses a 15-minute candle resolution as its underlying observational baseline while taking long-duration positions. Over the full backtest period, the system generated a cumulative return sum of 1,923.39% across closed trades, which corresponds to an annualized figure of 159.70%. The architecture relies on high structural precision, securing 81 winning trades against 20 losing trades, yielding an overall win rate of 80.20%. Designed as decision logic suitable for deployment in automated trading bots or trading agents, the strategy emphasizes selective participation, prioritizing signal fidelity over trade volume.
Trading rhythm and position duration
Despite operating on a 15-minute timeframe, the strategy exhibits the temporal signature of a swing or position-holding model rather than an intraday scalper. Across the 7.09-year evaluation window, the system completed 101 trades, translating to an average execution frequency of 14.24 trades per year, or approximately 1.60 trades per active month. The time spent within individual positions reflects significant patience: the average holding duration stands at 462.36 hours (roughly 19.26 calendar days), while the median holding duration is 307.50 hours (approximately 12.81 calendar days). This divergence between mean and median holding times indicates that while most positions exit within two weeks, a subset of trades remains open for multiple weeks to capture longer directional movements. Furthermore, the average interval between position exits is 25.43 days (median 20.36 days), confirming that automated trading robots driven by this logic will experience extended inactive windows where no new exit events occur. This deliberate pacing minimizes market exposure and trade turnover.
Quality of historical results
The quality of historical performance is highlighted by a Profit Factor of 5.00, indicating that gross profits exceeded gross losses by five to one. A key structural feature of this strategy is the alignment between its return distribution metrics. The average trade return of 3.35% is almost identical to the median trade return of 3.32%. In quantitative strategy design, such tight alignment between mean and median performance demonstrates remarkable distribution symmetry, confirming that overall profitability is generated by a consistent baseline of positive trades rather than skewed by a handful of extreme unexpected spikes. This is further corroborated by the concentration metric: the top three winning trades accounted for only 13.18% of total gross profits. The best individual trade generated a gain of 18.70%, which is well within a normal multiple of the average win. The consistency of these payoff dynamics suggests that trading agents running this logic benefit from repeatable statistical edge rather than rare black-swan events.
Risk, drawdown and losing behavior
Risk metrics across the 101 historical trades demonstrate tight loss containment and controlled drawdown behavior. The maximum historical drawdown experienced by the strategy was 18.35%. Notably, this maximum drawdown figure exactly matches the worst single trade loss recorded in the sample (-18.35%). This numerical identity reveals a crucial insight into the strategy's risk profile: historical peak-to-trough equity decline was driven almost entirely by a single adverse trade event rather than a compounding cluster of consecutive losses. This is reinforced by the strategy's losing streak statistics, where the longest consecutive losing streak was just 2 trades. In comparison, the strategy achieved a peak winning streak of 12 consecutive positive trades. Additionally, the worst trade (-18.35%) and best trade (+18.70%) showcase symmetrical boundary tails, illustrating that risk parameters are tightly bounded relative to the upper limits of performance.
Behavior through time and yearly stability
Analyzing the yearly breakdown from 2019 through 2026 demonstrates consistent equity growth across varied market regimes. After an initial setup period in late 2019 (2 trades, 50.00% win rate, +1.61% sum), the strategy experienced its highest volume year in 2020, executing 20 trades with an 85.00% win rate and generating a cumulative return sum of 104.28%. Performance remained strong in 2021 (17 trades, 70.59% win rate, +70.43%) and 2022 (15 trades, 73.33% win rate, +24.30%). In 2023, trading activity compressed to 7 trades, yet maintained profitability (+9.39% sum, 71.43% win rate). Activity re-accelerated in 2024 with 15 trades achieving a 93.33% win rate (+41.55% sum). This positive trajectory continued into 2025 (13 trades, 84.62% win rate, +48.29% sum) and 2026 (12 trades, 83.33% win rate, +38.44% sum). Crucially, the strategy achieved positive total net returns in every single full calendar year evaluated, proving that its edge is stable across different multi-year structural environments.
Recent period since 2024-06-01 versus full history
The dataset includes a dedicated evaluation window tracking completed trades with sell timestamps on or after June 1, 2024. During this recent window, the strategy executed 31 completed trades, accumulating a combined sum of trade returns of +105.85%. This indicates an increase in execution pacing compared to the full-history average of 14.24 trades per year, as 31 trades were finalized in approximately two years. Despite the higher trade volume in recent market conditions, quality metrics remained robust, aligning with the elevated win rates recorded in the annual breakdowns for 2024 (93.33%), 2025 (84.62%), and 2026 (83.33%). The recent period confirms that the strategy's multi-week holding logic has remained effective in current market environments, showing no sign of decay or structural breakdown.
Strengths and limitations
The strategy's primary statistical strengths include an exceptionally high Profit Factor (5.00), an 80.20% overall win rate, exceptional symmetry between average (3.35%) and median (3.32%) trade returns, low profit concentration (top 3 winners at 13.18%), and brief losing streaks (maximum of 2 trades). These traits make it an attractive candidate for automated execution logic. However, clear limitations exist within the statistical sample. The strategy's overall sample size is relatively low at 101 total closed trades over 7.09 years, reflecting an average of only 1.60 trades per active month. Furthermore, the average holding time of 462.36 hours requires trading agents and operators to endure long periods of illiquidity per position. Finally, the worst historical loss of -18.35% illustrates that while losing trades are rare, an individual loss can be sizable relative to the average winning trade of ~3.35%.
DevioLab analytical conclusion
Ranked #1 for DGX on DevioLab with an overall score of 85.46, this algorithmic strategy stands out for its structural stability and smooth payout profile. Its 80.20% win rate and 5.00 Profit Factor are backed by broad-based performance rather than high-variance statistical luck, as evidenced by the uniform spread of gains across its 101 trades. The strategy's low trade frequency and multi-week holding times make it especially suitable for automated trading bots and trading robots designed for swing-to-position execution, where minimizing trade turnover and avoiding false signals are paramount. While operators must remain mindful of the low trade count and the potential for occasional sharp drawdowns matching the -18.35% tail event, the broad consistency across all calendar years underscores its status as a robust core strategy.
Data scope and methodology
This analysis is based entirely on historical backtested closed trade statistics for the DGX asset on a 15-minute chart resolution, covering the period from September 16, 2019, through October 18, 2026. All reported performance figures—including cumulative returns, annualized metrics, drawdown percentages, holding times, and profit factor—reflect simulated historical output derived strictly from closed trades. These results do not include live execution data, real-time order book friction, commission costs, or slippage estimates. Historical backtested performance provides insight into algorithmic logic under specific quantitative parameters, but past statistical performance is no guarantee of future trading results.