CL · Colgate-Palmolive Co.
Stock Universe · mercado bursátil globalCL 15m Quantitative Analysis: High-Precision Swing Strategy for Automated Trading Bots
An analytical study of the 15-minute algorithmic trading strategy for CL stock across a nearly seven-year evaluation horizon from September 2019 to August 2026. Generating a cumulative gain of +550.62% across 130 completed trades with an 81.54% win rate and a profit factor of 3.75, the model demonstrates a disciplined multi-day swing profile. Its maximum historical drawdown of 5.56% exactly matches its worst single trade loss, underscoring strong structural risk containment for automated trading bots, trading agents, and algorithmic trading robots.
Análisis al 03.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The CL 15-minute algorithmic trading strategy represents a highly selective quantitative architecture engineered for equities trading. Evaluated across a dataset spanning 6.94 years from September 16, 2019, to August 24, 2026, the strategy achieved a DevioLab score of 81.49, holding the second rank for the CL symbol. Over the full historical timeline, the model generated a total cumulative CLosed-trade gain of +550.62%, translating to an annualized return metric of 74.93%. The strategy completed a total of 130 trades, delivering 106 winning outcomes against 24 losing positions, which translates into an 81.54% win rate and a robust profit factor of 3.75. Operating on 15-minute price bars, the underlying logic acts as decision intelligence suitable for implementation within automated trading bots, trading agents, and systematic execution algorithms, maintaining disciplined exposure while capturing cyCLical equity swings.
Trading rhythm and position duration
A critical structural feature of this strategy is the divergence between its execution timeframe and its position holding duration. Although the strategy processes market signals on a 15-minute price series, it is strictly a low-frequency multi-day swing system rather than a high-frequency or intraday scalping model. Positions remain open for an average holding duration of 145.58 hours (approximately 6.07 calendar days) with a median holding period of 116.13 hours (roughly 4.84 calendar days). The temporal spacing between historical trade exits reinforces this measured pace: the average time between CLosed positions is 19.64 days, with a median interval of 15.23 days. Across the 6.94-year dataset, the strategy completed an average of 18.73 trades per year, or approximately 1.88 trades per active month. For automated trading agents and trading robots, this extended duration profile means that execution overhead remains minimal, while individual trades are given sufficient time to absorb intraday noise and fulfill their directional targets.
Quality of historical results
Analyzing return distribution metrics provides deep insight into the consistency of the strategy's historical performance. The average return per completed trade stands at +1.68%, CLosely tracking the median trade return of +1.44%. This tight alignment between average and median returns indicates a well-balanced distribution devoid of extreme skewness or dependency on rare windfall trades. The strategy's best historical trade yielded a gain of +12.89%, while its worst trade registered a loss of -5.56%. Furthermore, the top three winning trades combined accounted for only 12.45% of total gross profit. This remarkably low profit concentration confirms that the cumulative historical performance of +550.62% was generated across a broad base of 106 winning trades rather than being skewed by a few isolated market events, offering high structural quality for quantitative modelers.
Risk, drawdown and losing behavior
Risk containment is one of the strategy's most prominent statistical features. Over the nearly seven-year backtest horizon, the maximum peak-to-trough drawdown recorded was exactly 5.56%. Intriguingly, this maximum drawdown figure mirrors the strategy's single worst trade loss (-5.56%), indicating that capital erosion was primarily bounded by individual trade parameters rather than compounding series of adverse trade sequences. The strategy maintained a longest winning streak of 19 consecutive profitable trades, compared to a longest losing streak of just 2 consecutive losing trades. Combined with an overall profit factor of 3.75 and an 81.54% win rate, the historical data demonstrate that losing periods were brief, isolated, and rapidly absorbed by subsequent winning cyCLes, providing operational safety for automated trading bots functioning under strict risk management constraints.
Behavior through time and yearly stability
An examination of yearly performance metrics highlights consistent profitability across diverse market environments from 2019 through 2026. The strategy generated positive aggregated returns in every single full calendar year in the dataset. In 2019, across a partial initial dataset, 6 completed trades yielded +7.41% with an 83.33% win rate. The year 2020 served as a standout period, yielding +80.03% across 24 trades with a 95.83% win rate (23 wins, 1 loss). Subsequent years maintained solid positive traction: 2021 generated +20.46% across 17 trades (82.35% win rate); 2022 produced +24.23% across 16 trades (68.75% win rate); 2023 recorded +17.72% across 17 trades (76.47% win rate); 2024 posted +10.30% across 15 trades (80.00% win rate); 2025 delivered +20.20% across 22 trades (77.27% win rate); and 2026 produced +38.39% across 13 trades (84.62% win rate). Annual trade frequency remained remarkably steady, fluctuating between 13 and 24 trades annually, establishing a highly repeatable pattern through time.
Recent period since 2024-06-01 versus full history
Evaluating recent performance since June 1, 2024, provides an important verification window for contemporary relevance. Within this recent observation period, the strategy recorded 46 completed trades, accumulating a combined profit sum of +66.92%. This recent sample represents 35.38% of all completed historical trades in the dataset, providing a robust empirical foundation. The average trade performance during this window remained strong, generating steady progress aligned with the strategy's long-term historical average of +1.68% per trade. The high density of completed trades during this period underscores that the strategy's signaling logic remained fully active and highly effective through recent market conditions, confirming that its statistical characteristics have not degraded over time.
Strengths and limitations
The strategy possesses distinct quantitative advantages alongside specific operational limitations. Its primary strengths inCLude an exceptional 81.54% win rate, a strong profit factor of 3.75, an unusually low maximum drawdown of 5.56%, and a broad-based return distribution where the top three winners account for only 12.45% of gross profit. These characteristics make it an appealing candidate for deployment in systematic trading bots and automated trading agents. Conversely, its limitations center on trade opportunity frequency. Generating only 18.73 trades per year on average, the strategy requires substantial patience, as extended flat periods between trades (averaging 19.64 days between exits) are common. Additionally, holding positions for multiple days (averaging 145.58 hours) exposes open trades to overnight gap risks inherent in traditional equities markets.
DevioLab analytical conCLusion
With a DevioLab score of 81.49 and a rank of 2 for CL, this 15-minute algorithmic strategy represents a mature, high-precision swing trading framework. Its ability to generate a +550.62% cumulative historical return while maintaining a peak drawdown of just 5.56% demonstrates a compelling balance between return generation and risk preservation. The CLose alignment between median (+1.44%) and average (+1.68%) returns, alongside consistent annual results across nearly seven years, proves that the system's edge is robustly constructed. For quantitative traders utilizing trading robots and automated execution agents, the model offers a disciplined decision engine capable of exploiting medium-term price swings with minimal trade churn.
Data scope and methodology
This analysis is based strictly on backtested statistical data for symbol CL on the 15-minute chart resolution, spanning from September 16, 2019, through August 24, 2026 (6.94 years). All metrics, inCLuding percentage gains, drawdowns, and holding durations, reflect historical simulated trade completions and do not represent live account trading or guaranteed future returns. The recent evaluation window specifically analyzes CLosed trades with exit timestamps on or after June 1, 2024. Transaction costs, execution slippage, borrowing fees, and tax implications are not inCLuded in the raw strategy outputs. This report is presented strictly for analytical and educational research purposes and does not constitute financial or investment advice.