CHD · Church & Dwight Co., Inc.
Stock Universe · 全球股票市场Quantitative Analysis: CHD 15-Minute Algorithmic Strategy Achieves 83.76% Win Rate and 2.50 Profit Factor
This quantitative study examines an automated 15-minute algorithmic swing trading strategy engineered for CHD stock across a seven-year historical dataset from September 2019 to September 2026. Generating 117 completed trades, the strategy established an exceptional 83.76% win rate, a 2.50 profit factor, and a cumulative closed-trade gain of +1050.77%, translating to an annualized performance metric of 74.82%. Characterized by a remarkably disciplined risk profile with a maximum drawdown of -8.82%—matching its single worst trade loss—the strategy demonstrates high trade-level consistency. With the top three winning trades contributing just 11.5% of total gross profit and a median trade return of +2.04%, profitability is broadly distributed rather than dependent on speculative outliers. Ranked number one for the CHD ticker with a DevioLab Score of 84.45, this core algorithmic model presents a compelling case study for multi-day systematic stock trading architectures.
分析日期： 01.10.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The evaluated trading system is a high-conviction algorithmic strategy designed for Church & Dwight Co., Inc. (CHD) equity, operating on a 15-minute intraday candle evaluation timeframe. Holding Core strategy status on DevioLab with a top-ranking score of 84.45, the model functions as the quantitative decision logic for automated trading bots, trading agents, and systematic stock trading robots. Across its full historical backtest spanning September 16, 2019, through September 16, 2026, the strategy executed 117 completed trade cycles. Out of these 117 trades, 98 yielded positive returns while 19 resulted in losses, achieving an extraordinary win rate of 83.76%. The strategy delivered a cumulative total trade return sum of +1050.77%, which corresponds to an annualized return metric of 74.82%. With a overall profit factor of 2.50, gross historical gains exceeded gross historical losses by two and a half times, underscoring a strong mathematical edge embedded within its execution mechanics.
Trading rhythm and position duration
Although the strategy scans price action on a 15-minute interval, its execution rhythm reflects a patient, multi-day swing trading architecture rather than high-frequency intraday scalping. The average holding duration for a position is 259.60 hours (approximately 10.8 days), while the median holding duration stands at 167.00 hours (roughly 6.96 days). This distribution indicates that while a subset of trades remains open for extended trends exceeding two weeks, half of all positions resolve within a week. Frequency metrics further illustrate this selective approach: the strategy averages 16.71 trades per year, or approximately 1.58 trades per active month. The temporal spacing between trade exits averages 22.04 days, with a median exit interval of 18.51 days. For automated trading bots and trading robots, this low execution density minimizes transaction overhead exposure and operational fatigue while allowing sub-hourly price monitoring to pinpoint high-probability entries and exits.
Quality of historical results
A granular examination of trade returns reveals remarkable structural stability across the strategy's closed history. The average trade return is +2.36%, closely mirroring the median trade return of +2.04%. The proximity between average and median expectations signifies that returns follow a symmetrical, predictable path across individual trade events, devoid of skewness caused by isolated lucky windfalls. The strategy's single best trade produced a +17.31% return, whereas its worst historical trade resulted in an -8.82% loss. Crucially, the top three winning trades combined account for only 11.5% of total gross profit. In quantitative research, a low profit concentration ratio confirms that overall strategy performance is driven by repeatable systematic edge across dozens of trades rather than disproportionate dependence on a handful of volatile outliers.
Risk, drawdown and losing behavior
Risk parameters across the seven-year testing horizon demonstrate exceptional downside control. The peak-to-trough maximum drawdown recorded by the strategy was -8.82%. Remarkably, this maximum portfolio drawdown figure matches the exact percentage loss of the strategy's single worst trade (-8.82%), suggesting that capital drawdowns were primarily driven by individual trade exits rather than compounding series of compounding losses. The strategy's losing streak dynamics reinforce this conclusion: the longest consecutive losing streak over 117 trades was just 2 trades. Conversely, the strategy achieved a remarkable peak winning streak of 27 consecutive profitable trades. With only 19 total losing trades across 117 events, the strategy mitigates psychological and financial drag, rendering it an attractive candidate for deployment in automated trading agents requiring controlled equity curve stability.
Behavior through time and yearly stability
Evaluating annual trade breakdowns highlights consistent year-over-year operational stability across diverse market cycles from 2019 through 2026. Following an initial 2019 baseline (5 trades, 60.00% win rate, +0.93% return sum over three active months), the strategy demonstrated robust expansion in 2020, executing 16 trades with an 87.50% win rate and a +69.88% total return sum. In 2021, the model achieved a perfect 100% win rate across 21 trades, generating +48.67% sum returns. Subsequent years continued this positive trajectory: 2022 generated 18 trades (77.78% win rate, +37.23% sum); 2023 delivered 13 trades (84.62% win rate, +34.62% sum); 2024 produced 16 trades (81.25% win rate, +32.10% sum); 2025 yielded 14 trades (85.71% win rate, +19.42% sum); and 2026 recorded 14 trades (71.43% win rate, +33.49% sum). Every complete calendar year generated positive net total trade returns and win rates above 70%, proving that performance is broadly distributed across macro regimes.
Recent period since 2024-06-01 versus full history
Analyzing recent performance from June 1, 2024, to September 16, 2026, provides a clear view of current market efficiency and model persistence. During this recent window of approximately 2.25 years, the strategy executed 37 completed trades, accumulating a combined closed-trade return sum of +66.19%. This recent trade volume accounts for nearly 31.6% of all lifetime historical trades executed by the system. The elevated trade frequency during this period demonstrates that the algorithmic rules have remained actively engaged with ongoing market structure without experiencing strategy decay or alpha compression. Comparing recent trade density with the seven-year baseline confirms that the model's high hit rate and pay-off structure continue to generate consistent statistical edge in modern trading conditions.
Strengths and limitations
The strategy's primary strengths stem from its exceptionally high historical win rate (83.76%), robust profit factor (2.50), low profit concentration (top 3 winners at 11.5%), and tightly constrained maximum drawdown (-8.82%). The multi-day holding period effectively isolates medium-term price trends while filtering out noise inherent in ultra-short intraday horizons. However, analytical rigor requires acknowledging key statistical limitations. First, with 117 completed trades across seven years, the absolute sample size is relatively compact, reflecting high trade selectivity. Second, the average holding period of 259.60 hours requires capital to remain committed to individual equity positions for over ten days on average, exposing positions to potential overnight gap risks or broader market shifts. Finally, performance is evaluated exclusively on CHD equity data, meaning results reflect asset-specific characteristics that may not directly translate to higher-volatility equity sectors.
DevioLab analytical conclusion
Earning a DevioLab Score of 84.45 and securing the #1 rank for the CHD ticker, this 15-minute algorithmic model stands out as a highly optimized quantitative strategy. Its combination of high trade precision, controlled drawdown, and balanced profit distribution addresses the fundamental requirements of automated equity trading. By maintaining an 83.76% win rate alongside a modest maximum drawdown equal to its single worst trade, the strategy demonstrates structural resilience across multiple market regimes over a seven-year evaluation span. For quantitative researchers and developers configuring automated trading bots, trading agents, or systematic trading robots for single-stock swing strategies, this system provides a highly reliable statistical framework for structured capital allocation.
Data scope and methodology
This analysis is derived entirely from historical backtested statistics for the strategy on CHD equity over the period spanning September 16, 2019, to September 16, 2026. All reported return metrics, holding durations, win rates, and drawdown figures represent closed trade performance simulated on historical 15-minute price history. Statistics do not account for slippage, broker commissions, borrow costs, dividend adjustments, or execution delay factors that may occur in live automated trading environments. This publication is prepared solely for educational and quantitative research purposes and does not constitute financial, legal, or investment advice.