CVX · Chevron Corp.
Stock Universe · marché actions mondialQuantitative Strategy Analysis: CVX · CVX High-Win-Rate Multi-Week Algorithmic Model
This quantitative research study evaluates the performance, risk architecture, and temporal stability of the flagship algorithmic trading strategy for CVX · CVX on the 15-minute timeframe. Spanning a 7.08-year backtested period from September 2019 to October 2026, the model executed 76 completed trades, achieving an 85.53% win rate and a Profit Factor of 5.00. Generating a cumulative historical return of +3448.90% (123.39% annualized) alongside a tightly bounded maximum drawdown of 11.99%, the strategy combines low trade frequency with multi-week holding durations. Ranked #1 for the CVX ticker on DevioLab with a composite score of 86.72, this core algorithmic model demonstrates high statistical consistency, balanced winner concentration, and resilient performance across every historical calendar year and recent out-of-sample data windows. Designed as decision logic for deployment in automated trading bots and trading agents, the strategy offers strong statistical edge for long-horizon swing execution.
Analyse au 14.10.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
Strategy profile
The subject under analysis is a core algorithmic trading strategy engineered for CVX · CVX within the stock market asset class. Operating on a 15-minute price evaluation interval, the strategy functions as an automated decision engine suitable for execution through trading bots, trading robots, and quantitative trading agents. Across a historical dataset extending from September 16, 2019, to October 14, 2026—a duration of 7.08 years—the strategy logged 76 fully completed trades. It generated a cumulative closed-trade historical return of +3448.90%, corresponding to an annualized gain of 123.39%. Achieving a DevioLab quantitative score of 86.72, the strategy secures the #1 rank among all tested algorithmic strategies for the CVX asset and holds an official core strategy designation. The core structural characteristics of this strategy center on extreme trade selection, holding positions across multi-week horizons while using granular 15-minute candle data to refine entry and exit triggers. Rather than engaging in frequent portfolio turnover, the model prioritizes high signal precision, yielding a sparse trade distribution designed to capture larger, directional price swings with minimal execution overhead.
Trading rhythm and position duration
Despite utilizing a 15-minute candle evaluation timeframe, the strategy's operational cadence is fundamentally medium-to-long-term. Over the 7.08-year history, the strategy averaged 10.74 trades per year, which translates to approximately 1.31 completed trades per active month. This low trade density confirms that the underlying logic filters out the vast majority of intraday market noise, waiting for specific structural conditions before committing capital. Position duration statistics further emphasize this swing-to-position trading approach. The average holding duration for a position is 452.19 hours (approximately 18.84 days), while the median holding time stands at 287.50 hours (approximately 11.98 days). The discrepancy between the average and median holding times indicates a right-skewed duration distribution, where certain winning positions are allowed to run for multiple weeks or months to capture extended trend progression. Temporal spacing between trade exits reinforces this measured rhythm. The strategy experienced an average of 33.82 days between trade exits, with a median spacing of 25.26 days. Consequently, automated trading agents deploying this logic must be calibrated for extended periods of market inactivity, followed by held positions lasting several weeks. The strategy is explicitly distinct from high-frequency approaches or scalping models.
Quality of historical results
The historical return profile of the strategy demonstrates notable payoff efficiency and hit-rate reliability. Out of 76 completed trades, 65 were closed in profit, yielding an overall win rate of 85.53%. The strategy produced a Profit Factor of 5.00, meaning that total gross historical gains were five times larger than total gross historical losses. This high profit factor underscores strong statistical asymmetry between positive and negative trade outcomes. Expectancy metrics show an average trade return of +5.33% across all positions, compared to a median trade return of +3.94%. The elevated average relative to the median points to positive skewness in trade gains, driven by substantial upside outliers. The strategy's single best historical trade yielded +36.21%, whereas the worst trade resulted in a loss of -11.99%. To evaluate whether performance was artificially inflated by a handful of lucky events, gross profit concentration must be examined. The top three winning trades combined accounted for 22.65% of total gross historical profit. Because less than a quarter of gross gains originated from its three best trades, the strategy demonstrates broad-based profitability across its 65 winning trades rather than reliance on extreme single-trade anomalies.
Risk, drawdown and losing behavior
Risk containment within the strategy is reflected in its historical drawdown parameters and streak characteristics. The maximum peak-to-trough historical drawdown recorded across the 7.08-year dataset was 11.99%. Remarkably, this figure matches the strategy's single worst trade loss (-11.99%), indicating that historical equity drawdowns were primarily bounded by individual position stops rather than compounding series of consecutive deep losses. The strategy's losing behavior is characterized by brief, isolated loss events. The longest historical losing streak observed was just 2 consecutive trades. In contrast, the strategy achieved a peak winning streak of 15 consecutive profitable trades. The combination of an 85.53% win rate and a maximum losing streak of 2 trades indicates that historical drawdown recovery periods were relatively short. For automated trading bots executing this logic, risk behavior appears highly controlled. The symmetry between the maximum drawdown (-11.99%) and worst trade (-11.99%) suggests that capital preservation logic successfully prevented adverse compounding failures during unfavorable historical market regimes.
Behavior through time and yearly stability
An examination of yearly historical performance demonstrates structural consistency across diverse market environments between 2019 and 2026. The strategy delivered positive cumulative returns in every single calendar year recorded in the dataset. In 2019 (starting September 16), the strategy logged 1 trade, resulting in a +7.34% return (100% win rate). The model experienced its highest return year in 2020, executing 14 trades with a 92.86% win rate (13 wins, 1 loss) and accumulating +159.71% in net trade gains. In 2021, trade frequency contracted to 5 trades, producing a 60.00% win rate (3 wins, 2 losses) and a net gain of +23.45%, marking the strategy's lowest annual win rate. Performance rebounded in 2022 with 14 trades, a 92.86% win rate (13 wins, 1 loss), and +82.89% cumulative return. In 2023, 12 trades yielded an 83.33% win rate (10 wins, 2 losses) and +20.69% net return. In 2024, 10 trades produced an 80.00% win rate (8 wins, 2 losses) and +33.35%. In 2025, 12 trades achieved a 91.67% win rate (11 wins, 1 loss) and +28.90%. In 2026 (through October 14), 8 trades generated a 75.00% win rate (6 wins, 2 losses) and +49.12%. Annual win rates remained at or above 75.00% in seven out of eight calendar periods. This year-over-year stability demonstrates that the strategy's statistical edge was not isolated to a single favorable regime but persisted across varying market cycles.
Recent period since 2024-06-01 versus full history
Evaluating recent performance since June 1, 2024, provides an out-of-sample perspective on whether the strategy's quantitative edge has persisted in recent market structures. Within this recent window, the strategy completed 24 trades, generating a cumulative return sum of +92.26%. Comparing this recent sample to full-history averages reveals consistent operational behavior. Across the 2.36-year period from June 2024 through October 2026, 24 trades reflect an annualized cadence of approximately 10.15 trades per year, which aligns closely with the full-history rate of 10.74 trades per year. Furthermore, the average gain per trade during this recent period remained robust at approximately +3.84%. The strong trade activity (24 completed trades) and positive cumulative return (+92.26%) since June 2024 confirm that the strategy's decision rules remained effective in late-stage data. The recent evidence shows no signs of decay, alpha erosion, or structural breakdown.
Strengths and limitations
The primary analytical strengths of this algorithmic model include: - Exceptional historical win rate of 85.53% combined with a high Profit Factor of 5.00. - Controlled drawdown risk, with a historic maximum drawdown of 11.99% matching the worst single trade loss. - Multi-year stability, producing positive closed-trade performance in every calendar year from 2019 through 2026. - Low gross profit concentration, with the top 3 winning trades comprising only 22.65% of gross profits. - Strong recent performance since June 1, 2024 (+92.26% across 24 trades). Key limitations and trade-offs that quantitative traders must consider include: - Low trade sample size (76 completed trades across 7.08 years), which increases statistical confidence intervals relative to higher-frequency models. - Long position holding periods (averaging 18.84 days), requiring trading agents to lock up position margin for extended durations. - Extended inactive intervals between trade exits (averaging 33.82 days), which requires operational patience during prolonged market filtration periods.
DevioLab analytical conclusion
With a DevioLab quantitative score of 86.72 and the #1 rank for CVX, this strategy represents a highly refined, low-frequency swing trading model. Its core strength lies in signal filtering, successfully converting high-granularity 15-minute price input into well-timed multi-week positions that minimize over-trading. The statistical alignment between its maximum historical drawdown (-11.99%) and single worst trade loss (-11.99%), alongside a 15-trade winning streak and a 5.00 Profit Factor, highlights disciplined risk containment. The distribution of returns across all 76 trades confirms that cumulative profitability (+3448.90%) was built upon repeatable win rates rather than high-leverage reliance on single outliers. For traders seeking automated trading bots or algorithmic execution models for CVX · CVX, this strategy offers a compelling balance of high win probability, low turnover, and contained drawdown. Capital allocation should account for the multi-week holding times and long inter-trade intervals inherent to its selective logic.
Data scope and methodology
This quantitative research report is based exclusively on backtested closed-trade performance data provided by DevioLab for the algorithmic strategy 'CVX 0 +3448.90% 1TRAD-IYR8' on the CVX · CVX stock asset over a 15-minute chart interval. The dataset covers historical execution logic from September 16, 2019, through October 14, 2026 (7.08 years). All reported percentage returns, win rates, drawdown figures, holding durations, and annual breakdowns reflect closed historical simulated trades within this backtest environment. Performance statistics do not account for variable brokerage commissions, execution slippage, borrowing fees, or real-time order routing latency. All conclusions presented are analytical deductions grounded strictly in the supplied statistical dataset. Historical backtested performance is not a guarantee of future live execution results.