CTAS · Cintas Corporation
Stock Universe · mercado global de açõesCTAS Quantitative Strategy Analysis: Evaluating a High-Precision Macro Trend Logic for CTAS Automated Trading Bots
This quantitative study examines the performance profile of the top-ranked algorithmic trading strategy for CTAS on the 15-minute timeframe. Over a 7.08-year historical evaluation window from September 2019 to October 2026, the strategy achieved a cumulative sum of closed trade gains reaching +2131.65%, underpinned by an extraordinary 95.45% win rate across 22 completed trades. Operating with an average holding duration of 2177.19 hours (approximately 90.7 days), the decision logic functions as a highly selective macro swing framework executed on intraday candle data. With a profit factor of 6.25, a maximum drawdown limited to 9.28%, and a DevioLab performance score of 86.18, the strategy offers a compelling baseline for automated trading robots and systematic trading agents, balanced by the statistical considerations inherent in a compact historical sample size.
Análise em 13.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The strategy designated as CTAS 0 +2131.65% 1TRAD-MDG4 represents the primary core quantitative model for CTAS within the DevioLab framework, earning a DevioLab score of 86.18 and holding the number one rank for this specific ticker. Evaluated across a dataset spanning from September 16, 2019, through October 13, 2026—a duration of approximately 7.08 years—the strategy generated a aggregate closed-trade return sum of +2131.65%, which translates to a calculated annualized return rate of 158.46%. Operating on a 15-minute candle interval, the strategy uses granular price action data to identify structural entry and exit signals, making it suitable as the decision logic for automated trading bots, systematic trading robots, and algorithmic trading agents looking for controlled equity curve expansion in equity markets. What sets this strategy apart is its extreme selectivity. Despite evaluating price data on a 15-minute resolution, it executed only 22 completed trades over the entire 7.08-year history. Out of these 22 closed positions, 21 trades resulted in positive returns, producing a win rate of 95.45% alongside a single losing trade. This combination of an exceptionally high hit rate and a robust profit factor of 6.25 establishes a strategy profile defined by minimal transactional noise and focused capital deployment. Rather than seeking frequent micro-gains, the algorithm acts as a macro trend harvester, utilizing intraday price structures to capture long-duration market movements while ignoring minor short-term fluctuations.
Trading rhythm and position duration
A critical aspect of this quantitative strategy is the pronounced dichotomy between its execution timeframe and its position holding duration. While entry and exit triggers are computed on a 15-minute chart, position holding times reflect a multi-month investment horizon. The average holding duration across all completed trades is 2177.19 hours (approximately 90.7 days), with a median holding period of 1655.75 hours (approximately 69.0 days). This indicates that once an automated trading agent enters a position based on 15-minute candle criteria, it remains exposed to the asset for weeks or months at a time, allowing broad structural trends to fully develop before initiating an exit sequence. The cadence of trading activity is correspondingly sparse. The strategy registers an average of 3.11 trades per year and 1.10 trades per active month. Furthermore, the average duration between closed positions stands at 118.60 days, with a median interval of 93.21 days between trade exits. This low turnover rate demonstrates that the strategy does not engage in rapid intraday flipping or high-frequency scalp operations. Instead, the 15-minute chart functions strictly as a high-resolution timing engine for entering and exiting multi-week trend cycles. For quantitative traders deploying trading robots, this extended rhythm reduces exposure to market noise while drastically minimizing exposure to execution frequency risk.
Quality of historical results
The statistical quality of the historical results is characterized by high win consistency and favorable trade distribution metrics. The strategy's overall win rate of 95.45% is derived from 21 winning trades against just 1 losing trade. The mean gain per completed trade stands at +15.93%, while the median trade return is +13.13%. The proximity of the mean and median trade metrics suggests a well-balanced distribution of profits, where historical gains were generated through consistent trade outcomes rather than being heavily skewed by a single extreme outlier. Evaluating the magnitude of individual outcomes reveals a best trade of +43.91% and a worst trade of -9.28%. The strategy's profit factor of 6.25 indicates that gross historical gains exceeded gross historical losses by more than six to one. Additionally, the top three winning trades collectively generated 30.47% of the total gross profits. In many trend-following strategies, the top three trades frequently account for 50% to 70% or more of total returns, creating dependency on rare windfall gains. Here, a 30.47% concentration share demonstrates that profit generation was distributed across multiple trades throughout the 7.08-year period, underscoring the repeatable payoff structure embedded in the decision rules.
Risk, drawdown and losing behavior
Risk metrics across the 7.08-year backtest show a remarkable degree of downside containment. The maximum peak-to-trough equity drawdown observed across the entire history was limited to 9.28%. Notably, this drawdown figure matches the exact percentage magnitude of the strategy's single historical losing trade (-9.28%). This exact alignment suggests that the cumulative backtest equity curve experienced virtually no compounded drawdown series across winning trades, with peak equity impairment being directly dictated by the sole losing position recorded in the dataset. The strategy's streak metrics further highlight its historical stability. The longest consecutive winning streak reached 17 trades, while the longest losing streak was capped at 1 trade. Because the strategy experiences extended holding periods averaging over 2100 hours per trade, open position volatility inevitably occurs while positions are active; however, at the closed-trade level, equity curve retracements have historically been brief and bounded. For automated trading bots managing capital allocations, a single historical loss of -9.28% within a 22-trade sample reflects a well-controlled risk envelope, though future performance may encounter different drawdown profiles as market dynamics evolve.
Behavior through time and yearly stability
An examination of the yearly performance breakdown reveals consistent capital growth across multiple annual cycles, albeit with varying activity levels. In 2019, during the initial part of the evaluation window starting in September, the strategy completed 1 trade with a +9.80% return (100% win rate). In 2020, activity expanded to 6 completed trades, all 6 winning (100% win rate), producing a combined annual return sum of +122.26%. The calendar year 2021 recorded 0 closed trades, indicating that active positions were either initiated prior and held through, or that market conditions did not trigger exit events during that twelve-month span. Trading activity resumed closed-trade reporting in 2022 with 3 winning trades totaling +48.43% (100% win rate). In 2023, the strategy recorded 1 completed winning trade delivering +22.86%. In 2024, 3 trades were finalized, all winning, yielding a combined +76.11% return sum. In 2025, another 3 winning trades were completed, adding +42.32% (100% win rate). In 2026, through October 13, the strategy executed 5 trades, achieving 4 wins and 1 loss (80.00% win rate) for a net annual sum of +28.60%. Every active calendar year in the sample achieved positive aggregated closed-trade returns, demonstrating multi-year structural stability across varying equity market regimes.
Recent period since 2024-06-01 versus full history
Analyzing recent performance from June 1, 2024, through October 13, 2026, provides valuable insight into how the quantitative model has operated in modern market conditions. During this recent window, the strategy completed 10 trades, generating a cumulative return sum of +108.97%. Considering that the entire 7.08-year dataset contains 22 completed trades, almost half (45.45%) of all historical trade exits occurred within this recent timeframe. This increased frequency of completed exits in the recent period reflects a higher density of trade resolutions compared to the earlier years of the sample. Despite the higher concentration of closed trades, performance quality remained high, with recent trades contributing +108.97% toward the total cumulative gain of +2131.65%. The steady accumulation of returns during this recent period confirms that the algorithm's exit criteria and trend-capture mechanisms have remained responsive to contemporary market behavior without suffering from degradation or structural drift.
Strengths and limitations
The primary historical strength of this algorithmic strategy lies in its outstanding win rate (95.45%), strong profit factor (6.25), and low maximum drawdown (9.28%). By pairing a high-resolution 15-minute execution chart with macro-length holding periods, the system effectively avoids overtrading while capturing major price movements in CTAS. Furthermore, gross profit distribution is well-balanced, with the top three winners accounting for only 30.47% of total gains, and the median trade return of +13.13% remaining close to the mean trade return of +15.93%. Conversely, the chief limitation of the strategy is its small total sample size. With only 22 completed trades over 7.08 years, individual trade outcomes carry significant statistical weight. While a 95.45% win rate across 22 trades is statistically impressive, sample size constraints mean that future live performance could experience a higher loss frequency than observed in backtested data. Additionally, holding positions for an average of ~90.7 days requires substantial patience and capital commitment, as long inactive periods—such as 2021 with zero closed trades—may test the discipline of operators managing automated trading agents.
DevioLab analytical conclusion
With a DevioLab performance score of 86.18 and the top rank for CTAS, this algorithmic model stands out as a highly specialized strategy designed for long-term trend extraction. The combination of an annualized return rate of 158.46%, a cumulative sum gain of +2131.65%, and a worst historical trade of -9.28% reflects a disciplined approach to systematic risk management and capital growth. For quantitative portfolio managers and developers deploying trading robots or automated agents, the strategy offers a low-turnover, high-conviction decision framework. The long holding periods and average exit interval of nearly 119 days mean that execution friction remains low. While operators must account for the small historical trade sample when designing risk control protocols, the strategy's consistency across active years establishes it as a premier benchmark for CTAS systematic model architecture.
Data scope and methodology
The quantitative metrics presented in this article are derived exclusively from historical backtested closed-trade data for CTAS on the 15-minute timeframe covering the period from September 16, 2019, to October 13, 2026. All statistics, including cumulative return sums, win rates, drawdown percentages, holding durations, and yearly breakdowns, represent mathematically modeled execution logs under fixed decision rules. This research is intended solely for educational and analytical purposes and does not constitute financial or investment advice. Historical simulated performance is not a guarantee of future live execution results. Factors such as exchange order routing, live bid-ask spreads, execution slippage, interest rates, and evolving market liquidity conditions can cause live trading results to diverge from backtested models. Automated trading bots and agents should always be monitored with appropriate capital protection mechanisms.