ELV · Elevance Health, Inc.
Stock Universe · mercado global de açõesELV · ELV Algorithmic Strategy Analysis: High Win-Rate Swing Model with a 5.0 Profit Factor
This detailed quantitative evaluation examines the top-ranked 15-minute algorithmic trading strategy for ELV stock over a 4.09-year testing period from August 2022 to September 2026. Generating a cumulative closed-trade return sum of 855.14% across 84 completed trades, the strategy demonstrates an 83.33% historical win rate, a 5.0 Profit Factor, and a maximum drawdown of 6.91%. Ranking first for the asset with a DevioLab score of 88.13, the quantitative model exhibits broad profit distribution, with the top three winning trades contributing just 18.11% of total gross gains. Designed to provide systematic decision logic for deployment in automated trading bots, trading robots, or algorithmic trading agents, this report analyzes holding duration dynamics, risk metrics, annual performance consistency, and recent trade sample behavior since June 2024.
Análise em 24.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The subject of this quantitative study is a core algorithmic trading strategy developed for ELV stock, evaluated on a 15-minute execution timeframe. Spanning 4.09 years of market history from August 12, 2022, to September 14, 2026, the backtest captures 84 completed round-trip trades. Over this historical window, the strategy compiled a cumulative closed-trade return sum of 855.14% and an annualized rate of 348.76%. Out of 84 completed trades, 70 closed in positive territory while 14 resulted in losses, yielding an overall win rate of 83.33%. With a Profit Factor of 5.0 and a maximum drawdown limited to 6.91%, the strategy achieves the number 1 rank for ELV with an overall DevioLab score of 88.13. The underlying quantitative structure serves as systematic rule-based decision logic suitable for integration into automated trading bots, trading robots, or algorithmic trading agents seeking disciplined swing-trading exposure in stock equity markets.
Trading rhythm and position duration
Although the decision architecture evaluates price data on a granular 15-minute bar interval, the actual trade execution rhythm reflects a patient, selective swing trading approach. On average, the strategy completes 20.53 trades per year, which translates to approximately 1.95 trades per active month. Individual positions feature substantial holding periods: the average holding time across all completed trades is 229.24 hours (roughly 9.55 days), whereas the median holding duration is 165.63 hours (approximately 6.90 days). The spacing between trade exits is similarly paced, showing an average interval of 17.93 days and a median interval of 14.11 days between closed positions. The contrast between fine-grained intraday bar evaluations and multi-day position duration demonstrates that the strategy utilizes intraday resolution to refine entry and exit timing without succumbing to over-trading or noise-driven scalping.
Quality of historical results
An examination of trade magnitude and yield metrics reveals a highly favorable statistical distribution. The strategy achieves an average return per trade of +2.97% and a median return of +2.38%, demonstrating that typical winning outcomes closely align with expected mean expectations. The single best trade achieved a gain of +18.38%, whereas the worst individual losing trade was contained at -6.84%. Crucially, the top three winning trades generated only 18.11% of total gross profits. This relatively low winner concentration confirms that total strategy return is not reliant on a handful of extraordinary tail events, but rather stems from repeated, consistent capture of positive trade legs. Furthermore, the Profit Factor of 5.0 indicates that total gross profits were five times greater than total gross losses, reflecting strong structural payoff quality across the entire trade dataset.
Risk, drawdown and losing behavior
The strategy demonstrates exceptional drawdown containment and risk mitigation properties across its historical track record. The peak-to-trough maximum drawdown recorded was 6.91%, a remarkably modest contraction given the cumulative return profile. This low drawdown severity is supported by short losing streaks and tightly constrained loss magnitudes. The longest consecutive losing streak observed over the 4.09-year period was just 2 trades, in stark contrast to a peak winning streak of 25 consecutive profitable trades. Furthermore, with the worst historical trade capped at -6.84% and an overall win rate of 83.33%, losing clusters were statistically brief and shallow. For automated execution engines, such controlled equity drawdowns reduce capital preservation risks and limit the psychological stress associated with extended underwater periods.
Behavior through time and yearly stability
Evaluating annual trade breakdowns illustrates consistent historical profitability across multiple market regimes. In 2022 (starting in August), the strategy completed 8 trades with 6 wins (75.00% win rate), accumulating a return sum of +9.40%. Activity picked up in 2023 with 24 trades, 22 wins (91.67% win rate), and a total gain sum of +45.14%. In 2024, the system registered 14 trades, 11 wins (78.57% win rate), and +40.90% sum return. Performance continued strongly into 2025, recording 20 trades, 13 wins (65.00% win rate), and a gain sum of +55.67%. The 2026 period through September delivered 18 trades, all 18 of which were profitable (100.00% win rate), generating +98.47%. While win rates varied year-to-year between 65.00% and 100.00%, every single calendar year closed with positive overall return sums, highlighting multi-year structural stability.
Recent period since 2024-06-01 versus full history
Focusing on the recent testing window from June 1, 2024, through September 14, 2026, provides valuable insight into contemporary strategy behavior. During this recent segment, 47 completed trades closed, representing more than 55% of the strategy total sample of 84 trades. The cumulative sum of trade returns during this recent period reached +166.53%. This demonstrates that strategy trade generation has remained active and highly productive in modern market conditions, rather than relying on stale early-history performance. The high density of successful recent trade exits confirms that the underlying quantitative decision logic has maintained statistical efficacy up to the present end-date of the dataset.
Strengths and limitations
The main historical strengths of this ELV algorithmic strategy include an extraordinarily high win rate of 83.33%, a high Profit Factor of 5.0, a exceptionally low maximum drawdown of 6.91%, and low profit concentration in outlier trades (18.11% top-3 share). Short losing streaks (maximum 2 trades) and consistent annual gains further reinforce statistical robustness. Conversely, limitations center on sample frequency and position exposure. Completing 84 trades over 4.09 years yields a modest frequency of ~20.53 trades per year, requiring patience from automated deployment systems. Additionally, holding positions for an average of 9.55 days exposes trades to multi-day overnight market gap risk in equity markets. As with all backtested quantitative models, real-world execution costs, slippage, and evolving market regimes represent ongoing operational factors.
DevioLab analytical conclusion
Achieving an overall score of 88.13 and ranking number 1 for ELV stock, this algorithmic trading strategy stands out as a highly effective swing-trading framework. Its statistical profile features an exceptional balance of high win probability, broad gross profit distribution, minimal drawdown depth, and sustained performance across all recorded annual periods. The strategy's design is particularly well suited as core decision logic for automated trading bots, trading robots, and quantitative agents seeking systematic stock equity exposure without incurring excessive trade churn or deep historical drawdown periods.
Data scope and methodology
The findings presented in this analysis are based exclusively on backtested historical performance metrics for ELV stock over the explicit dataset period from August 12, 2022, to September 14, 2026. Evaluation parameters utilize a 15-minute price bar resolution covering 84 completed simulated trades. Summed percentage return figures refer to simple additive closed-trade gains and do not represent exact compounding or live broker account balances. Theoretical modeling does not directly deduct live broker execution fees, transaction commissions, or market slippage. Historical simulated performance metrics are purely analytical and do not constitute investment advice or guarantee future trading performance.