CVS · CVS Health Corporation
Stock Universe · globaler AktienmarktAlgorithmic Analysis of CVS (CVS): High-Win-Rate 15-Minute Strategy Demonstrates Multi-Week Holding Structure and Low Outlier Dependence
This quantitative evaluation inspects a top-ranked algorithmic trading strategy designed for CVS stock execution, operating on a 15-minute timeframe asset setup. Over a backtested 7.03-year observation window spanning September 2019 through September 2026, the model logged 79 total completed trades, attaining an 83.54% win rate and a profit factor of 6.25. Although built on 15-minute price data, the algorithm operates as a selective, multi-week positioning system with an average holding duration of 401.98 hours (16.7 days) and a low trade frequency of 11.24 trades per year. The historical performance exhibits balanced profit distribution, with the top three winning trades contributing only 11.33% of gross profits, indicating that cumulative capital gains were generated through consistent positive trades rather than isolated momentum spikes. Peak backtested drawdown was bounded at 14.53%, perfectly matching its single worst loss, while maintaining a peak winning streak of 26 trades. Recent data from June 2024 onward reinforces historical stability, generating 160.85 percentage points across 25 completed trades with zero losing periods in 2025. This article provides a comprehensive statistical breakdown suitable for evaluating automated trading bots and algorithmic trading agents.
Analyse vom 14.10.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The evaluated algorithmic model for CVS (CVS) represents the highest-ranked quantitative framework for this asset within the DevioLab library, earning a DevioLab Score of 86.96 and holding rank 1 for the ticker. Categorized as a core strategy based on independent optimal selection logic, the algorithm operates on a 15-minute candlestick chart interval. Across a historical backtest spanning 7.03 years from September 16, 2019, to September 25, 2026, the strategy closed 79 completed trades. Total cumulative trade sum across all closed positions reached 2366.80%, translating to an annualized synthetic figure of 340.29%. Designed as decision logic capable of driving automated trading bots and quantitative execution agents, the strategy emphasizes high selectivity over high-frequency transaction volume. Out of 79 completed historical cycles, 66 trades yielded positive percentage returns, while 13 trades closed in loss, resulting in an overall historical win rate of 83.54%. The underlying architecture balances directional precision with robust loss management, achieving a historical profit factor of 6.25. This indicates that total closed gains exceeded total closed losses by more than six times across the seven-year testing sample.
Trading rhythm and position duration
While the strategy processes market data at a granular 15-minute timeframe, its execution dynamics reflect a low-frequency, multi-week swing positioning framework rather than rapid intraday turnover. The model completes an average of 11.24 trades per year, which corresponds to 1.22 trades per active calendar month. The spacing between trade exits averages 32.71 days, with a median exit interval of 28.02 days, highlighting a rhythm where market engagement is intermittent and highly filtered. When a position is initiated, the holding period is extensive. The average holding time per trade is 401.98 hours (approximately 16.75 days), while the median holding duration stands at 337.50 hours (14.06 days). The close alignment between average and median holding times indicates a consistent holding structure, where trades systematically mature over a two-to-three-week horizon rather than exiting prematurely due to intraday noise. This operational tempo allows automated trading robots to execute signals without demanding continuous microsecond infrastructure, as the logic prioritizes macro swing persistence over short-lived order-book imbalances.
Quality of historical results
The structural quality of the historical returns is characterized by high consistency and broad profit distribution across completed positions. The average trade yield across the 79-trade sample is 4.49%, closely matching the median trade return of 4.72%. This tight relationship between mean and median performance demonstrates that the strategy does not rely on heavily skewed positive outliers to maintain profitability. Further evidence of payout quality is visible in the concentration metrics: the three largest winning trades account for just 11.33% of gross strategy profits. The best individual historical trade achieved a gain of 16.29%, which sits comfortably near the central distribution rather than representing a massive runaway anomaly. On the loss side, the worst closed trade registered a decline of 14.53%. Because the strategy combines an 83.54% hit rate with an average trade yield nearly equal to its median, the backtested return profile displays structural stability, where net gain accumulates steadily through repetitive high-probability entries rather than dependence on single black-swan moves.
Risk, drawdown and losing behavior
Risk metrics within the backtested window reveal controlled exposure and limited sequential loss clustering. The strategy experienced a maximum drawdown of 14.53% over the entire 7.03-year history. Notably, this maximum draw ceiling is identical in magnitude to the strategy's worst single historical trade (-14.53%), indicating that equity retracements were primarily driven by single trade occurrences rather than compounding chains of successive losses. The historical losing behavior is further detailed by a maximum losing streak of only 2 consecutive trades, contrasted against a maximum winning streak of 26 consecutive positive trades. Out of 79 completed positions, only 13 total trades resulted in losses. With a profit factor of 6.25 and bounded drawdown depth, the historical risk profile reflects an environment where capital exposure is kept constrained. For quantitative managers evaluating automated trading agents, this loss distribution suggests that equity curve retrenchments historically recovered swiftly due to the high baseline win rate and low losing streak persistence.
Behavior through time and yearly stability
An examination of the yearly breakdown demonstrates persistent statistical edge across changing equity market environments from 2019 through 2026. In 2019, across a partial initial window, the strategy executed 3 trades with a 100% win rate, yielding 11.10%. Full-year performance showed notable stability: 2020 generated 35.94% across 12 trades (75.00% win rate); 2021 produced 63.75% across 12 trades (91.67% win rate); 2022 produced 43.73% across 10 trades (70.00% win rate); 2023 yielded 17.54% across 10 trades (70.00% win rate); 2024 generated 56.51% across 12 trades (91.67% win rate); 2025 delivered 72.38% across 10 trades (100.00% win rate); and 2026 recorded 53.55% across 10 trades (80.00% win rate). Annual trade counts remained remarkably stable between 10 and 12 trades per full year, underscoring fixed algorithmic discipline. Furthermore, positive total returns were logged in every single calendar year, with annual win rates never falling below 70.00%. This year-over-year uniformity confirms that performance was broadly distributed across multi-year cycle changes rather than concentrated in a single fortunate market regime.
Recent period since 2024-06-01 versus full history
Evaluating the recent performance sample beginning June 1, 2024, provides insight into the strategy's contemporary behavior. In this recent window, covering completed trades with sell timestamps on or after June 1, 2024, the strategy executed 25 trades, generating a cumulative return sum of 160.85%. This represents approximately 31.6% of total historical completed trades occurring in the most recent 2.3-year sub-period. The pace of trade completion during this window (roughly 10.8 trades per year) aligns closely with the overall full-history baseline of 11.24 trades per year, showing no signs of frequency decay or execution paralysis. Performance quality in the recent window remained exceptionally strong, highlighted by a flawless 10-trade winning streak across 2025 (+72.38%) and 8 wins out of 10 trades in 2026 (+53.55%). Comparing recent metrics to full-history averages confirms that the strategy's statistical edge has remained intact into recent market structures rather than deteriorating over time.
Strengths and limitations
The main analytical strengths of this algorithm include its elevated historical win rate (83.54%), strong profit factor (6.25), and controlled drawdown profile (14.53%). Its profit generation is exceptionally balanced, evidenced by the low concentration of gross profits in top winners (11.33%) and the close proximity of average trade gain (4.49%) to median trade gain (4.72%). Additionally, consistent annual profitability across all tested years highlights temporal stability. The primary limitation stems from the modest sample size of completed trades. Executing only 79 closed positions over 7.03 years (11.24 trades per year) means statistical conclusions are drawn from a low-density sample. While holding positions for an average of 16.7 days reduces transaction noise, it also exposes open equity to overnight stock market gaps, earnings announcements, and macro news events that cannot be managed intraday once entered. Traders deploying automated trading bots must account for long exposure durations in individual equities.
DevioLab analytical conclusion
The quantitative profile of this CVS strategy establishes it as a highly efficient, swing-oriented algorithmic model. Ranked first for the asset with a DevioLab Score of 86.96, the strategy achieves a rare combination of high hit rate (83.54%) and substantial payoff factor (6.25) without relying on extreme tail winners. The convergence of mean (4.49%) and median (4.72%) trade returns confirms that its backtested growth trajectory is built on repeatable statistical edges rather than fragile outliers. Its 14.53% maximum drawdown and short losing streaks (maximum 2 trades) demonstrate effective downside boundary control over a seven-year backtest. While the low annual trade frequency demands patience from operators of automated trading agents, the empirical historical data demonstrates steady equity accumulation and robust stability across both historical and recent observation windows.
Data scope and methodology
This analysis is derived entirely from historical backtested trading data for CVS under a 15-minute chart resolution spanning from September 16, 2019, to September 25, 2026. All reported statistics, including win rates, trade counts, holding durations, yearly breakdowns, and drawdown figures, reflect closed simulated trades within this backtest environment. The data scope assumes fixed logic execution without live order slip, variable brokerage commissions, or interest rate borrowing costs. Historical performance figures do not guarantee future returns, and backtested results should be evaluated as quantitative research rather than direct investment advice or financial guarantees.