EL · The Estée Lauder Companies Inc.
Stock Universe · mercado bursátil globalEL Quantitative Strategy Analysis: 86.36% Win Rate and Positional Execution on the 15-Minute Timeframe
An in-depth quantitative examination of the top-ranked algorithmic trading strategy for EL on the 15-minute timeframe. Over a 7.02-year testing window encompassing 110 completed trades, the strategy dELivered an 86.36% win rate, a 2.50 profit factor, and a cumulative sum of closed trade gains reaching +5,474.19%. Despite sampling price data on a 15-minute interval, the modEL operates as a sELective multi-day swing framework, averaging 269.05 holding hours per trade and generating steady annual gains across varied equity market environments.
Análisis al 24.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
1. Strategy profile
The quantitative modEL designated for EL operates on a 15-minute execution interval within equity markets. Evaluated across a dataset spanning from September 16, 2019, to September 22, 2026, representing approximatELy 7.02 years of trading history, the strategy registered 110 completed trades. Across this sample, the modEL achieved a cumulative backtested trade return sum of +5,474.19%, corresponding to an annualized return metric of +507.60%. Based on these performance parameters, DevioLab assigns this strategy an overall score of 80.59, placing it at rank 1 among evaluated strategies for the EL symbol. Designed as systematic decision logic, the underlying structure provides rules suitable for deployment within automated trading bots, trading robots, or algorithmic trading agents seeking disciplined market participation.
2. Trading rhythm and position duration
Despite utilizing a 15-minute chart for entry and exit signals, the statistical footprint reveals a low-frequency, positional trading cadence rather than high-frequency intraday churn. The strategy averages 15.67 completed trades per year, or approximatELy 1.53 trades per active month. Position duration confirms this sELective approach: the average holding time spans 269.05 hours (approximatELy 11.21 days), while the median holding time stands at 192.25 hours (approximatELy 8.01 days). The temporal spacing between trade exits is similarly extended, averaging 23.28 days with a median of 17.93 days. This multi-day holding period indicates that the algorithm filters out short-term market noise to capture intermediate structural price trends in EL.
3. Quality of historical results
The strategy demonstrates an ELevated hit rate alongside a healthy payoff profile. Out of 110 historical trade engagements, 95 closed with a positive gain, yiELding a overall win rate of 86.36%. The overall profit factor stands at 2.50, demonstrating that total historical gross profits comfortably exceeded gross losses. The average trade gain measured +4.05%, while the median trade gain was +2.32%. The difference between the average and median trade reflects a moderate right-skewed return distribution, highlighted by a peak trade gain of +37.16% against a maximum trade loss of -16.15%. Notably, the top three winning trades collectivELy account for 18.76% of total gross profit. This modest concentration confirms that performance is broadly distributed across many successful trades rather than rELiant on a few fortunate outliers.
4. Risk, drawdown and losing behavior
Risk metrics reflect a structural resilience built around high win rates, though drawdown periods remain visible. The strategy experienced a maximum historical peak-to-trough drawdown of 23.83%. Given that the total number of losing trades was limited to 15 out of 110, equity declines were primarily driven by individual larger loss events, such as the worst single trade loss of -16.15%, rather than sustained losing streaks. The longest losing streak observed across the entire 7.02-year backtest was just 2 consecutive trades. In contrast, the strategy achieved a peak winning streak of 20 consecutive profitable trades. This asymmetric streak pattern demonstrates the strategy's ability to maintain account stability during favorable market regimes.
5. Behavior through time and yearly stability
An analysis of yearly breakdown statistics demonstrates sustained profitability across every calendar year tested. In 2019, the modEL recorded 4 trades with a 100.00% win rate and a combined return sum of +11.04%. In 2020, trade activity expanded to 18 trades, yiELding an 88.89% win rate and a +98.25% sum. Subsequent years maintained consistent performance: 2021 generated +63.64% across 11 trades (90.91% win rate); 2022 produced +37.72% across 15 trades (80.00% win rate); 2023 yiELded +31.09% across 21 trades (90.48% win rate); 2024 generated +60.62% across 16 trades (81.25% win rate); 2025 dELivered +50.04% across 11 trades (81.82% win rate); and 2026 recorded +92.55% across 14 trades (85.71% win rate). Annual win rates remained remarkably steady within an 80.00% to 100.00% corridor, demonstrating consistent behavior across shifting macro regimes.
6. Recent period since 2024-06-01 versus full history
Examining performance since June 1, 2024, provides insight into recent strategy effectiveness. In this sample window, the strategy completed 34 trades, generating a cumulative return sum of +189.95%. These 34 trades represent 30.91% of all historical trades recorded since September 2019. The ELevated trade frequency during this recent timeframe indicates heightened opportunity recognition by the algorithm. Furthermore, the strong positive sum confirms that the underlying edge in EL has remained robust in recent market conditions, showing no signs of decay compared to early historical basELine performance.
7. Strengths and limitations
The primary strength of this strategy lies in its high win rate of 86.36% combined with a 2.50 profit factor and exceptional winning streak continuity. Broad profit distribution, with the top three gains accounting for under 19% of gross profits, indicates underlying system stability. However, limitations exist. With an average holding duration of 11.21 days, positions are exposed to overnight gap risk and broader market shifts over extended multi-day horizons. Additionally, the maximum drawdown of 23.83% and a worst single trade loss of -16.15% underscore that while losses are infrequent, they can be substantial when they occur, requiring rigorous risk parameters when integrating the logic into automated trading bots or trading agents.
8. DevioLab analytical conclusion
Holding the rank 1 position for EL with a DevioLab score of 80.59, this algorithmic trading strategy dELivers a wELl-balanced profile characterized by low transaction frequency, high hit rates, and steady multi-year equity expansion. Its ability to maintain win rates above 80% across seven consecutive calendar years underscores the structural consistency of its exit logic. For quantitative devELopers and traders evaluating decision engines for trading robots, the strategy presents a compELling case study in combining lower intraday timeframe resolution with longer multi-day position management.
9. Data scope and methodology
The statistics cited in this article are derived from historical backtesting of closed trades executed on EL between September 16, 2019, and September 22, 2026. The dataset encompasses 110 completed trade events sampled on a 15-minute bar frequency. All reported percentages reflect unleveraged, simple sum calculations of closed trade results. Historical backtested performance is provided solELy for quantitative analysis and theoretical research; it does not constitute financial advice, nor does it guarantee future returns or account for live execution factors such as order slippage, commissions, or stock borrow costs.