DVA · DaVita Inc.
Stock Universe · mercado bursátil globalDVA · DVA Algorithmic Strategy Analysis: High Win-Rate Structure and Low Profit Concentration in Stock Automation Logic
This analytical report evaluates the historical backtested performance of the top-ranked algorithmic trading strategy for DVA stock, operating on a 15-minute execution timeframe. Over a 6.97-year evaluation history encompassing 89 completed trades, the strategy achieved a 82.02% win rate, a Profit Factor of 5.0, and a cumulative simulated gain of +9,904.59%. Designed as actionable decision logic for automated trading bots, trading robots, and trading agents, the system combines extended holding periods averaging 480.38 hours with a low trade frequency of 12.77 trades per year. The statistical profile is characterized by tightly aligned mean (+5.81%) and median (+5.22%) trade returns, an extremely low top-three winner profit concentration of 13.31%, and unbroken profitability across every calendar year from 2019 to 2026. This comprehensive review examines the strategy's risk profile, structural consistency, and recent output window performance.
Análisis al 22.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The quantitative trading model evaluated in this research report represents the flagship algorithmic strategy for DVA in the stock market, earning the top rank (#1) on DevioLab with an overall analytical score of 83.21. Designated as a core independent optimal strategy, the model operates on a 15-minute price chart interval to evaluate market conditions and generate trade signals. Across a statistical dataset spanning from September 16, 2019 to September 3, 2026—a duration of approximately 6.97 years—the strategy completed 89 trades. During this multi-year evaluation, the logic achieved an absolute cumulative return of +9,904.59%, translating to an annualized return metric of 285.52%. The core objective of this quantitative model is to provide systemic, rule-based execution logic suitable for deployment via automated trading bots, trading robots, or autonomous trading agents operating on equity market feeds. By standardizing entry and exit parameters, the strategy eliminates emotional bias and converts price action data into structured portfolio decisions.
Trading rhythm and position duration
Despite utilizing a 15-minute price series for signal identification, the strategy displays a disciplined, low-frequency trading rhythm oriented toward medium-term position management rather than rapid intraday turnover. Over the entire sample, the strategy completed an average of 12.77 trades per year, which translates to roughly 1.44 trades per active trading month. The temporal spacing between trade exits reveals a median of 20.51 days and an average of 28.32 days, confirming that position cycles typically span several weeks. Position holding times further reinforce this swing-to-position trading classification: the average holding duration stands at 480.38 hours (approximately 20 calendar days), while the median holding duration is 264.50 hours (roughly 11 calendar days). The divergence between the average and median holding periods suggests that while many trades resolve within ten to twelve days, a subset of trending positions is held significantly longer to extract extended market moves. This low-frequency, multi-week holding architecture ensures that automated execution engines encounter minimal order throughput, focusing instead on capturing meaningful structural price shifts in DVA stock.
Quality of historical results
The historical return distribution of the DVA strategy demonstrates exceptional statistical stability and payoff quality. Out of 89 completed trades, 73 closed in positive territory while only 16 resulted in losses, yielding a high overall win rate of 82.02%. The strategy generated a Profit Factor of 5.0, indicating that cumulative gross profits were five times larger than cumulative gross losses. A crucial indicator of return quality is the close proximity between the average trade result (+5.81%) and the median trade result (+5.22%). This narrow gap demonstrates that the strategy's profitability is driven by repeated, structurally consistent gains rather than skewed by a handful of extreme positive outliers. Furthermore, the top three winning trades combined accounted for only 13.31% of total gross profits. In quantitative strategy evaluation, a low winner concentration ratio confirms that performance is broadly distributed across the 73 winning trades rather than dependent on lucky single-event spikes. The best individual trade achieved a gain of +31.94%, while the worst trade registered a loss of -20.80%, illustrating a controlled asymmetry between peak gain and peak loss.
Risk, drawdown and losing behavior
Risk management metrics reveal a compact and resilient loss profile over the 6.97-year backtest history. The strategy experienced a historical maximum drawdown of 20.80%, a figure that exactly mirrors the worst individual trade outcome of -20.80%. This alignment suggests that peak equity distress was primarily driven by a single adverse position event rather than a compounding series of consecutive losses. The strategy's streak dynamics strongly support this conclusion: the longest consecutive losing streak across all 89 completed trades was restricted to just 2 losing positions. Conversely, the longest winning streak reached 14 consecutive positive trades. With an 82.02% win rate and short losing sequences, the strategy limits drawdowns from clustering. However, the presence of a -20.80% maximum trade loss highlights that individual trades can experience noticeable price adverse selection before an exit is triggered. Automated trading agents executing this logic must account for single-trade drawdown volatility while benefiting from quick historical recovery cycles driven by high win frequency.
Behavior through time and yearly stability
An examination of the yearly breakdown from 2019 through 2026 reveals exceptional temporal consistency across varying market regimes. The strategy recorded positive aggregate returns in every single calendar year of the evaluation period. In 2019 (partial year from September), the model completed 1 trade with a 100% win rate, yielding +22.22%. Performance accelerated in 2020 with 13 trades, a 92.31% win rate, and a summed trade return of +101.94%. In 2021, trade count moderated to 9 trades with a 66.67% win rate producing +33.39%. The year 2022 generated 16 trades at a 93.75% win rate (+69.92%), followed by 2023 with 12 trades, a 66.67% win rate, and +108.06% in accumulated returns. In 2024, the strategy executed 10 trades with an 80.00% win rate (+41.25%). In 2025, 14 trades yielded a 71.43% win rate (+57.28%), and in 2026, 14 trades delivered a 92.86% win rate (+82.67%). Annual trade frequency remained remarkably steady within a band of 9 to 16 trades per full year, demonstrating that the strategy's underlying signal generation criteria maintain stable output rates regardless of broader financial market shifts.
Recent period since 2024-06-01 versus full history
Performance during the recent observation window beginning June 1, 2024, highlights robust ongoing viability. Between June 1, 2024, and September 3, 2026, the strategy completed 35 trades, generating a cumulative summed return of +158.01%. This recent window accounts for approximately 39.3% of the strategy's total 89 lifetime trades, reflecting an increased density of trade completions in recent years compared to the earlier historical sample. The average contribution per trade during this recent period remained highly productive, consistent with the long-term historical mean of +5.81% per trade. The strong performance across these 35 recent trades confirms that the strategy's decision logic has maintained its edge in current stock market conditions, showing no evidence of performance decay or signal degradation.
Strengths and limitations
The strategy possesses several distinct quantitative strengths: first, an impressive 82.02% win rate coupled with a Profit Factor of 5.0, demonstrating structural efficiency in trade selection; second, an exceptionally low top-three profit concentration of 13.31%, proving that returns are systemic and broad-based; third, complete annual reliability, having delivered positive performance in all eight recorded calendar years; and fourth, strong recent validation with +158.01% gained across 35 trades since mid-2024. Conversely, certain statistical limitations must be noted. The total trade count of 89 completed trades over nearly seven years represents a relatively compact sample size, meaning that individual trade outcomes carry significant statistical weight. Additionally, the maximum trade drawdown of -20.80% indicates that automated trading bots using this strategy must tolerate multi-week position pullbacks during adverse trade setups.
DevioLab analytical conclusion
With a DevioLab Score of 83.21 and a #1 ranking for DVA stock, this algorithmic trading strategy stands out as a highly disciplined model for equity market automation. Its core strength lies in combining high win accuracy (82.02%) with broad profit distribution and minimal dependence on tail-risk winning trades. The strategy's medium-term holding pattern—averaging ~20 days per trade—allows automated trading robots and execution agents to capture substantial price swings without incurring the noise and market impact associated with higher-frequency strategies. While the low annual trade volume (12.77 trades per year) demands patience from operators, the historical data demonstrates consistent, high-expectation performance across multi-year market cycles.
Data scope and methodology
This analysis is based strictly on historical backtested performance data compiled by DevioLab for the ticker symbol DVA in the stock market over the period from September 16, 2019, to September 3, 2026 (6.97 years). The strategy evaluates market data on a 15-minute timeframe. All reported statistics—including profit factors, trade totals, win rates, drawdown levels, and holding durations—reflect simulated outcomes derived from closed positions. The results do not account for unstated live execution factors such as order routing delays, slippage, or trading commissions. Historical backtested figures provide a quantitative baseline for evaluating strategy behavior but do not guarantee future returns.