COO · The Cooper Companies, Inc.
Stock Universe · global stock marketCOO Quantitative Strategy Analysis: Exceptional 80.77% Win Rate and Low Profit Concentration Mark a High-Precision Swing Trading Model
This empirical evaluation analyzes the historical backtest performance of the core algorithmic strategy for COO stock across a 7-year history spanning September 2019 to October 2026. Operating on a 15-minute execution timeframe, the strategy generates selective, multi-week position trades with an average holding period of 470.33 hours (approximately 19.6 days). Across 78 closed historical trades, the model achieves an 80.77% win rate, a Profit Factor of 5.00, and a cumulative return sum of +742.81%. Significantly, the strategy exhibits remarkable gain distribution, with the top three winning trades contributing only 13.99% of gross profits, proving that performance is driven by systematic consistency rather than isolated outliers. While the historical maximum drawdown reaches 23.79%—largely defined by a single severe loss of -23.79%—the strategy maintains tight losing streaks capped at two consecutive trades against a peak winning streak of 14 trades. Recent behavior since June 2024 demonstrates robust activity, delivering 31 closed trades and +86.50% in cumulative gains, solidifying its rank as DevioLab's top-performing strategy for COO.
Analysis as of 08.10.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The quantitative model evaluated here represents DevioLab's top-ranked core strategy for COO stock, earning a DevioLab score of 79.54 and holding the number one ranking for this specific ticker. Evaluated across a historical sample period extending from September 16, 2019, to October 8, 2026—a horizon of 7.06 years—the strategy operates on a 15-minute bar interval. Despite relying on intra-day 15-minute price data to determine entry and exit signals, the strategy's actual execution characteristics belong firmly to the multi-week position trading category rather than high-frequency or intra-day trading. Over the full 7-year backtest window, the algorithm executed 78 completed trades, yielding a total accumulated gain of +742.81% and an annualized performance metric of 103.28%. From a structural perspective, the algorithm is engineered to function as the decision logic for automated trading bots, trading robots, and programmatic trading agents operating in equity markets. By combining fine-grained 15-minute price monitoring with multi-week trade durations, the model seeks to filter out short-term market noise while pinpointing structural momentum shifts. The system operates with extreme patience, executing an average of just 11.05 trades per year. This structural design prioritizes high signal quality over trade quantity, allowing automated deployment without exposing capital to excessive portfolio churn.
2. Trading rhythm and position duration
A defining characteristic of this strategy is the stark contrast between its technical resolution and its trade duration. Although the system monitors the market on a 15-minute chart, position holding times are extended. The historical average holding duration per trade stands at 470.33 hours (approximately 19.6 days), while the median holding duration is 371.88 hours (approximately 15.5 days). This moderate right-skew in holding duration indicates that while most trades resolve within two to three weeks, a subset of winning trends is held open for longer periods to maximize move capture. The strategy's pace is further contextualized by its exit distribution. The average time elapsed between completed trade exits is 33.15 days, with a median spacing of 29 days. On an operational level, the system completes approximately 1.22 trades per active month. Consequently, an automated trading agent running this logic remains out of the market or holding a single position for weeks at a time. This low turnover rate means that position management requires patience, as the trading robot may sit inactive for extended periods waiting for high-probability setups to materialize.
3. Quality of historical results
The statistical quality of the strategy's closed historical trades is defined by remarkable consistency and broad-based profit distribution. Out of 78 completed trades, 63 were profitable and 15 resulted in losses, establishing an 80.77% win rate. The strategy achieves a Profit Factor of 5.00, meaning that gross realized profits exceeded gross realized losses by a factor of five. A granular examination of individual trade returns reveals a nearly identical alignment between the average trade return (+3.17%) and the median trade return (+3.15%). This tight symmetry demonstrates that typical trades deliver dependable, uniform returns, devoid of extreme positive skewness that often distorts algorithmic performance metrics. Furthermore, the strategy demonstrates an exceptionally healthy profit structure through its low profit concentration. The top three winning trades in the historical backtest account for only 13.99% of total gross profit, with the best single trade returning +18.27%. In quantitative strategy evaluation, a low top-three profit concentration indicates that a strategy's profitability is spread broadly across its trade population rather than depending on a handful of anomalous windfalls. The system generates high historical returns through repetitive, incremental compounding across dozens of distinct market cycles.
4. Risk, drawdown and losing behavior
Risk metrics across the 7-year sample indicate a strategy built on high win probability but exposed to occasional tail risk events. The maximum historical drawdown recorded for the strategy is 23.79%. Notably, this maximum peak-to-trough decline closely mirrors the strategy's single worst trade, which registered a loss of -23.79%. This numerical equivalence suggests that the primary source of historical equity drawdown was not a long, grinding sequence of consecutive losses, but rather a single sharp adverse event where price moved rapidly against an open position. This interpretation is reinforced by analyzing the strategy's streak behavior. The maximum consecutive losing streak across 78 trades was restricted to just two trades. In contrast, the maximum consecutive winning streak reached 14 trades, demonstrating the algorithm's ability to capitalize on sustained market regimes. Because losing sequences are brief, portfolio recovery is historically rapid, provided capital is sized appropriately to withstand occasional sharp drawdowns on individual trades. Algorithmic trading bots deploying this strategy must account for this specific tail-risk profile by managing leverage and trade sizing carefully.
5. Behavior through time and yearly stability
Examining yearly breakdown statistics reveals consistent profitability across changing market environments. From 2019 through 2025, every single completed calendar year delivered positive net accumulated returns. In 2019, across a partial year sample of 3 trades, the strategy achieved a 100% win rate and a +14.95% return sum. Performance remained solid through subsequent years: 2020 yielded +42.57% across 11 trades (81.82% win rate); 2021 generated +29.32% across 8 trades (75.00% win rate); 2022 recorded +36.18% across 11 trades (72.73% win rate); and 2023 produced +26.76% across 9 trades (88.89% win rate). In 2024, trade activity increased slightly to 13 trades, returning +37.33% with an 84.62% win rate. The strategy's strongest absolute annual gain occurred in 2025, producing +69.28% across 16 trades with an 87.50% win rate. In the partial year of 2026 (through October), the strategy logged 7 trades with 4 wins and 3 losses (57.14% win rate), resulting in a minor net drawdown of -8.80%. Over seven consecutive full years, the strategy proved remarkably consistent, demonstrating structural adaptability across diverse market regimes.
6. Recent period since 2024-06-01 versus full history
A focused analysis of recent history beginning June 1, 2024, provides clear evidence regarding the strategy's current effectiveness. Within this recent window, the strategy completed 31 trades, generating a total accumulated return sum of +86.50%. This recent trade sample represents nearly 40% of all historical trades recorded since September 2019, reflecting a notable acceleration in trading frequency during late 2024 and 2025 compared to earlier years. Comparing recent performance with the multi-year baseline confirms that the strategy's core edge remains intact. The substantial trade volume generated since June 2024 yielded high aggregate profitability without destabilizing the strategy's return distribution. While the full-history metrics reflect steady multi-week holding patterns, the increased trade completion rate in recent months underscores that recent market volatility in COO provided ample setup conditions that matched the strategy's entry criteria.
7. Strengths and limitations
The primary strength of this quantitative strategy lies in its extreme statistical consistency and robust profit distribution. Boasting an 80.77% win rate, a 5.00 Profit Factor, and a low top-three profit concentration of 13.99%, the strategy delivers repeatable performance that does not rely on rare market spikes. The modest average trade frequency of 11.05 trades per year minimizes execution noise, making it an efficient candidate for automated trading robots designed to execute swing trades with minimal monitoring overhead. Conversely, the strategy exhibits distinct limitations that operators must evaluate. The primary constraint is capital lockup: with an average holding time of 470.33 hours (~19.6 days) and nearly a month between trade exits, capital is tied up in individual positions for prolonged windows. Additionally, the worst historical trade (-23.79%) demonstrates that when risk controls trigger during sharp adverse moves, single-trade losses can be severe. Finally, while the 7-year backtest duration provides meaningful historical context, the total sample size of 78 completed trades represents a relatively compact statistical dataset.
8. DevioLab analytical conclusion
The COO 0 +742.81% 1TRAD-DPV4 strategy earns its place as DevioLab's top-ranked model for COO stock through exceptional reliability and well-balanced statistical mechanics. Its high DevioLab score of 79.54 reflects superior performance across win rate, profit factor, and annual consistency. The decision logic successfully translates 15-minute price input into medium-term swing edges, achieving an average trade return of +3.17% with an 80.77% probability of success. For quantitative traders deploying automated trading agents, this strategy provides a robust template for medium-horizon equity capturing. While risk management frameworks must account for occasional tail-risk single-trade drawdowns on the order of 23%, the overall historical profile—characterized by short losing streaks, 14-trade winning streaks, and seven consecutive profitable years—makes this system an outstanding subject for algorithmic strategy execution.
9. Data scope and methodology
The data analyzed in this article is derived directly from historical simulated backtest records for the COO algorithm on a 15-minute chart resolution spanning September 16, 2019, through October 8, 2026. All reported performance metrics, including total return sums (+742.81%), annualized returns (103.28%), win rates (80.77%), holding durations, and yearly breakdowns, reflect closed historical trade executions within the backtested simulation framework. These statistical results describe past quantitative simulation output and do not represent guaranteed future account returns or live execution performance. No external factors such as undisclosed execution slippage, variable brokerage commission structures, dynamic margin costs, or corporate action adjustments were introduced beyond the parameters of the strategy backtest engine. This analysis is produced strictly for quantitative strategy research and educational evaluation.