CLX · The Clorox Company
Stock Universe · mercado bursátil globalCLX Algorithmic Trading Strategy Analysis: High-Conviction Low-Frequency Execution on CLX
An in-depth quantitative examination of the CLX 0 +197.83% 1TRAD-JPF1 algorithmic trading strategy evaluated on a 15-minute chart frame over a 6.89-year test period. Achieving an absolute return of +197.83% across 8 completed trades with zero historical losses, this long-horizon model demonstrates an extreme filter design suitable for automated trading bots and trading agents seeking multi-month position holding on CLX equity.
Análisis al 04.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The strategy designated as CLX 0 +197.83% 1TRAD-JPF1 represents an ultra-selective quantitative trading model configured for the CLX equity asset on a 15-minute timeframe. Over an evaluation span of approximately 6.89 years between September 2019 and August 2026, the model completed a total of 8 trades, yielding a net cumulative return of +197.83% and an annualized growth rate of 24.43%. Designed to operate as automated decision logic for algorithmic trading bots, trading robots, and quantitative trading agents, the system prioritizes extreme signal selectivity over transactional volume. Within the DevioLab framework, the strategy holds a rank of 3 for the CLX ticker with a DevioLab score of 62.62. The baseline architecture demonstrates an unconventional paradigm: despite processing 15-minute intraday bar data, the strategy operates as a long-term position holding engine, completely filtering out market noise to capture major structural swings in CLX.
Trading rhythm and position duration
The operational cadence of this strategy is characterized by extreme patience and extended exposure windows. Across the nearly seven-year backtested history, the system generated an average of only 1.16 completed trades per year, or 1 trade per active month in which exits occurred. Position holding times reflect a macro-oriented posture rather than typical intraday behavior. The average holding duration stands at 3373.38 hours (approximately 140.5 days), while the median holding duration is 2270.25 hours (roughly 94.6 days). The cadence of trade realizations is similarly spaced, with an average interval of 314.75 days between consecutive position exits and a median exit interval of 250 days. This statistical profile confirms that although signal calculations occur on a 15-minute chart interval, the execution logic acts as a patient macro allocator, holding positions through multiple earnings cycles and broad market trends before satisfying exit conditions.
Quality of historical results
The trade performance metrics exhibit a perfect hit rate alongside substantial gain magnitude. Across all 8 closed transactions, the strategy achieved a 100% win rate with zero losing trades recorded. The average trade yield reached +15.58%, while the median trade yield was +10.99%. Profit distribution shows a moderate right-skewed tail, bounded by a best single trade return of +50.29% and a worst trade return of +3.63%. The profit factor is recorded at 6.88. An examination of gross profit concentration reveals that the top 3 winning trades account for 66.95% of total realized cumulative gains. While a high concentration in top performers is standard in trend-following systems, the absence of negative baseline trades demonstrates that every historical exit satisfied profitable criteria, though the total sample size remains tightly bounded at 8 occurrences.
Risk, drawdown and losing behavior
From a closed-trade analytical perspective, the maximum equity drawdown is recorded at 0.00%, direct result of all completed trades closing with net positive returns. The longest winning streak matches the entire sample length at 8 trades, while the longest losing streak is 0. Because no closed losses were logged, standard risk ratios based on trade-loss frequency cannot be calculated in typical fashion. However, investors and quantitative analysts evaluating this framework for automated trading agent deployments must distinguish between closed-trade drawdowns and intra-trade floating equity drawdowns. Given average holding periods exceeding 140 days, positions were exposed to broader equity market volatility during open cycles. The strategy's risk control relies on exit selectivity that deferred profit taking until favorable price conditions emerged.
Behavior through time and yearly stability
Analyzing the annual breakdown illustrates how profits accumulated unevenly across the test timeline. In 2020, a single closed trade contributed +50.29%, marking the largest single trade expansion in the strategy history. In 2021, activity increased to 3 completed trades, generating a combined +23.35%. Subsequent years saw steady, single-trade completions: 2022 generated +9.22% across 1 trade, 2023 logged +9.55% across 1 trade, 2024 produced +12.43% across 1 trade, and the partial horizon leading into 2026 registered 1 trade yielding +19.81%. Certain years, such as 2019 and 2025, recorded no closed trade events, highlighting extended periods where open positions were maintained without satisfying exit triggers. This temporal distribution confirms that historical returns were driven by occasional macro trends rather than consistent monthly compounding.
Recent period since 2024-06-01 versus full history
In the observation window from June 1, 2024 onward, the strategy logged 2 completed trade exits, producing a cumulative return sum of +32.24%. Comparing this recent performance to the multi-year history indicates that recent market conditions remained highly conducive to the strategy's signal filters. The average return per completed trade in this recent sub-period reached approximately +16.12%, which closely mirrors the full-history average trade return of +15.58%. The recent window contributed a significant portion of the total historical gain (+32.24% out of +197.83%), confirming that the algorithmic model retained active execution capability and profitability in modern market structures without experiencing strategy decay or elevated exit friction.
Strengths and limitations
The primary strength of this algorithmic model lies in its flawless historical accuracy, robust profit factor of 6.88, and impressive annualized return of 24.43% on a defensive stock ticker like CLX. Its ultra-low trade frequency minimizes exposure to market friction, transaction volume, and erratic noise. Conversely, the main statistical limitation stems from the sample size of only 8 completed trades over nearly seven years. A sample of 8 events introduces statistical uncertainty when attempting to project future win probabilities, as a 100% win rate is unlikely to persist infinitely over broader statistical regimes. Furthermore, holding trades for hundreds of days requires high psychological discipline or fully automated execution through trading bots, as open floating drawdowns during holding phases are not reflected in closed-trade metrics.
DevioLab analytical conclusion
The CLX 0 +197.83% 1TRAD-JPF1 strategy demonstrates a specialized macro-swing framework operating on intraday 15-minute price data. With a DevioLab score of 62.62 and a third-place ranking for the CLX equity symbol, the algorithm provides a compelling case study in extreme signal filtering. The system sacrifices trade frequency to achieve high payoff quality and zero historical closed losses across its 8 execution cycles. For traders seeking logic suitable for automated trading robots or trading agents, this strategy provides a ultra-selective option, provided the user acknowledges the statistical constraints of a small sample size and prepares for extended holding periods spanning multiple quarters.
Data scope and methodology
The analytical statistics presented in this report were compiled from simulated historical backtest results for the CLX equity symbol between September 16, 2019, and August 4, 2026. All trade metrics, holding durations, yearly breakdowns, and recent performance calculations are derived strictly from closed position data. Performance percentages reflect trade-level growth rather than account-level equity curves with dynamic compounding or fee modeling. Historical results do not guarantee future performance, and this quantitative analysis serves strictly educational and research purposes for automated trading strategy evaluation.