CLX · The Clorox Company
Stock Universe · marché actions mondialCLX Quantitative Strategy Analysis: Evaluating a High-Win-Rate 15-Minute Algorithmic Model for Stock Trading
An in-depth quantitative examination of the top-ranked algorithmic trading strategy for CLX equity over a 7.05-year evaluation history. Operating on a 15-minute execution interval while maintaining multi-week position holding periods, the strategy generated a cumulative trade sum return of +584.34% across 50 completed trades with an 82.00% win rate and a profit factor of 2.50. This analysis explores the system's structural characteristics, trade return distributions, risk parameters, and temporal stability to evaluate its viability for automated trading bots and trading agents.
Analyse au 04.10.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
Strategy profile
The quantitative strategy designated as CLX 0 +584.34% 1TRAD-BIE1 represents the top-ranked algorithmic trading model for CLX stock within the DevioLab framework, achieving a DevioLab Score of 76.24. Positioned at rank 1 for this ticker, the system is engineered around a 15-minute timeframe (15m interval) to capture price inefficiencies in the equity market. Over a total historical dataset spanning 7.05 years from September 16, 2019, to October 4, 2026, the strategy recorded 50 completed trades, yielding a total accumulated trade gain of +584.34% and an annualized equivalent return of 38.99%. The foundational architecture exhibits a high hit rate, securing 41 winning trades against 9 losing trades, which translates to an 82.00% win rate. With a profit factor of 2.50, the cumulative gross gains generated by winning positions exceed total gross losses by two and a half times. The decision logic is designed to function as an automated trading agent or trading robot, leveraging intraday bar data to execute high-conviction entries while maintaining controlled exposure. Rather than seeking high transaction frequency, the system operates as a selective, trend-aligned framework tailored for automated execution environments.
Trading rhythm and position duration
Although the model evaluates market data on a granular 15-minute bar frequency, its actual execution rhythm is patient and low-frequency. Across the 7.05-year history, the strategy averaged 7.09 trades per year, which translates to approximately 1.19 trades per active month. This sparse trade frequency indicates that the underlying logic filters out the vast majority of intraday market noise, waiting for specific structural conditions before entering a position. Once entered, trades are held for substantial durations. The average holding duration stands at 673.02 hours (approximately 28.0 days), while the median holding duration is 408.25 hours (roughly 17.0 days). The divergence between the mean and median holding times suggests that while the typical position resolves within two to three weeks, a subset of strong trending trades is held open for several months to maximize capital extraction. Furthermore, the spacing between trade exits is extended, with an average of 52.10 days and a median of 45.79 days between completed exit events. For trading bots and automated execution systems, this temporal structure means trading activity is episodic rather than continuous, eliminating constant turnover and positioning the algorithm as a multi-week swing or position management engine.
Quality of historical results
Evaluating the distribution of trade outcomes reveals a remarkably well-balanced performance profile. The average return per completed trade is +4.41%, closely matching the median trade return of +3.90%. The proximity of the mean to the median indicates that overall historical profitability is driven by a consistent stream of positive outcomes rather than being skewed by extreme positive outliers. The single best trade achieved a gain of +22.90%, while the worst trade resulted in a loss of -17.47%. A crucial quantitative insight lies in the concentration of gross profit: the top three winning trades combined account for only 23.27% of total gross profits. In many automated strategies, a handful of exceptional trades generate the overwhelming majority of gains, creating statistical fragility. Here, the low concentration metric proves that historical profitability is distributed broadly across the 41 winning trades. Combined with an 82.00% win rate and a profit factor of 2.50, the quality of results reflects a resilient pay-off structure where positive returns are repeatedly earned across varied market phases.
Risk, drawdown and losing behavior
Risk parameters within the strategy show effective drawdown containment relative to total accumulated returns. The maximum drawdown experienced across the entire 7.05-year history was capped at 17.47%. Analyzing this metric alongside individual trade metrics reveals a notable relationship: the maximum drawdown of 17.47% is virtually identical to the strategy's worst single trade (-17.47%). This indicates that historical portfolio equity dips were primarily driven by isolated losing trades rather than severe compounding sequences of multiple consecutive failures. The losing behavior statistics reinforce this finding, as the longest losing streak recorded over the entire dataset was just 2 trades. Conversely, the longest winning streak extended to 14 consecutive positive trades. While the presence of an individual -17.47% trade underscores the need for sound position sizing and risk control, the system's ability to quickly truncate losing runs prevents deep compounding equity drawdowns. For automated trading agents, this risk profile offers structural stability during adverse equity phases.
Behavior through time and yearly stability
An examination of the yearly performance breakdown illustrates sustained effectiveness across multiple annual cycles from 2019 through 2026. Following an initial partial year in 2019 (1 trade, +2.45% return), the strategy delivered strong performance in 2020, executing 9 trades with an 88.89% win rate and summing +84.03% in trade returns. In 2021, performance remained exceptional with 9 trades, a 100.00% win rate, and +33.07% cumulative gain. The stability continued through 2022 and 2023, both recording 7 trades, an 85.71% win rate, and summed returns of +29.43% and +30.24% respectively. In 2024, performance moderated slightly to 6 trades, a 66.67% win rate, and +31.64% return. The calendar year 2025 represented the strategy's sole negative annual period, completing 5 trades with a 60.00% win rate and a net return of -7.92%, caused by the impact of larger individual losses relative to smaller wins. However, performance rebounded in 2026, delivering 6 trades with a 66.67% win rate and +17.47% return. Over eight calendar years, seven produced positive net gains, demonstrating high temporal consistency.
Recent period since 2024-06-01 versus full history
Evaluating the recent evidence window from June 1, 2024, to October 4, 2026, provides insight into the strategy's current operational health. During this recent period, the system completed 14 trades, generating a combined return sum of +43.60%. These 14 trades represent 28.00% of the strategy's total 50 completed trades across its 7.05-year history, indicating a moderate increase in trade realization frequency during recent market conditions. The average trade gain during this recent window stands at approximately +3.11%, which remains aligned with the full historical average of +4.41%. Comparing recent behavior with full history confirms that the decision logic has maintained functional continuity without experiencing parameter degradation or drift. Despite encountering a minor annual contraction in 2025, the substantial positive return accumulated since mid-2024 demonstrates that the model continues to successfully capture multi-week price swings in CLX stock.
Strengths and limitations
The primary structural strength of this algorithmic model lies in its exceptional win rate (82.00%), solid profit factor (2.50), and broad distribution of returns, where the top three winning trades account for less than a quarter of gross profits (23.27%). Its low execution frequency (7.09 trades per year) prevents overtrading, while a maximum drawdown of 17.47% keeps historical risk within controlled boundaries. However, key limitations must be acknowledged. With only 50 completed trades over 7.05 years, the sample size is relatively small, meaning statistical metrics carry higher standard errors than high-frequency models. Additionally, the worst single trade loss of -17.47% demonstrates that when a trade fails, the downside can be significant relative to the average positive trade (+4.41%). Furthermore, annual win rates showed a slight shift from the 85%-100% range seen between 2020 and 2023 down to 60%-66.67% in 2024-2026, highlighting the necessity of ongoing monitoring when deploying automated trading robots.
DevioLab analytical conclusion
With a rank of #1 for CLX stock and a DevioLab Score of 76.24, the CLX 0 +584.34% 1TRAD-BIE1 strategy stands out as an effective model for capturing multi-week equity trends using intraday 15-minute price data. The strategy successfully bridges the gap between granular entry timing and long-duration position management, averaging 673.02 hours per trade while limiting total trade count to 50 over 7.05 years. Its high win rate of 82.00%, moderate drawdown of 17.47%, and balanced profit distribution demonstrate significant statistical quality. For quantitative researchers and operators of automated trading bots, this system offers a compelling baseline for low-frequency stock trading strategies, provided risk management protocols account for occasional wide single-trade loss exposures.
Data scope and methodology
This analysis is derived exclusively from historical backtest statistics for the algorithmic strategy applied to CLX equity over the dataset window from September 16, 2019, to October 4, 2026 (7.05 years). All performance metrics, including the total cumulative trade gain (+584.34%), win rate (82.00%), profit factor (2.50), and drawdown metrics, reflect closed backtested trade returns generated on 15-minute price data. Statistics calculated for the recent period (since June 1, 2024) comprise completed trades with sell timestamps on or after June 1, 2024, UTC. These findings represent simulated historical strategy research for educational and analytical purposes and do not represent live account trading performance, execution costs, slippage, or financial advice.