CCEP · Coca-Cola Europacific Partners PLC
Stock Universe · mercado bursátil globalCCEP 15-Minute Algorithmic Strategy Analysis: High Win Rate and Controlled Drawdown in Equity Trading
An in-depth quantitative analysis of the top-ranked CCEP 15-minute algorithmic strategy on DevioLab. Over a 7.03-year backtested window comprising 122 completed trades, the strategy achieved a cumulative gain of 2148.41% with an 83.61% win rate and a peak drawdown restricted to 7.34%. Featuring an average holding duration of 198.08 hours and a profit factor of 2.50, this research evaluates how low execution frequency combined with multi-day position holding generates persistent risk-adjusted performance across varying market cycles.
Análisis al 27.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
1. Strategy profile
The quantitative trading model designated as CCEP 0 +2148.41% 1TRAD-SSG7 represents the primary algorithmic framework for Coca-Cola Europacific Partners (CCEP) on the 15-minute timeframe within the DevioLab database. Earning a DevioLab score of 85.01 and holding the number one rank for its specific ticker, the strategy demonstrates how granular intraday data can be utilized to drive a disciplined, low-frequency swing trading methodology. Across a continuous backtested evaluation window spanning 7.03 years—from September 16, 2019, through September 27, 2026—the strategy generated a closed-trade cumulative return of 2148.41%, translating to an annualized performance metric of 106.61% under constant exposure calculations. Designed as decision logic suitable for deployment in automated trading bots and algorithmic execution agents, the framework minimizes market presence while seeking statistical edges in equity price structures. Rather than engaging in frequent intraday trading, the strategy uses 15-minute candlestick evaluations to refine entry and exit timing for multi-day position holdings. Over the entire sample period, the strategy closed exactly 122 trades, establishing a profile characterized by extreme selectivity, robust win metrics, and strict capital preservation.
2. Trading rhythm and position duration
Despite operating on a 15-minute chart interval, the strategy exhibits the temporal characteristics of a selective swing trading engine rather than an intraday scalper. The model averages 17.35 completed trades per year, which corresponds to approximately 1.74 trades per active month. This sparse execution cadence highlights a high statistical threshold for trade initiation, filtering out market noise and executing orders only when multi-factor quantitative criteria are satisfied. The temporal footprint of individual positions reveals a clear multi-day horizon. The average holding duration per trade stands at 198.08 hours (approximately 8.25 calendar days), while the median holding duration is 138.13 hours (roughly 5.75 calendar days). The divergence between the mean and median holding times indicates that while most trades resolve within roughly six days, a subset of favorable trend-following positions remains open for longer durations to capture extended directional moves. Furthermore, the average interval between consecutive trade exits is 20.91 days (with a median exit spacing of 16.75 days). For quantitative operators using automated trading robots, this structural cadence requires operational patience, as the system remains idle in cash for multi-week periods between active position cycles.
3. Quality of historical results
The strategy's payout dynamics reflect high historical precision coupled with asymmetric upside expansion. Out of 122 completed trades, 102 closed in positive territory, yielding a win rate of 83.61%, against 20 losing positions. This high hit rate is complemented by a profit factor of 2.50, demonstrating that total gross profits significantly outpaced total gross losses over the 7.03-year evaluation historical dataset. A fundamental quantitative insight lies in the statistical symmetry between average and median returns per trade. The strategy produced an average trade expectation of +2.88%, which aligns closely with its median trade expectation of +2.63%. This structural proximity confirms that the total net gain of +2148.41% is built upon a consistent, repeatable distribution of single-digit wins rather than a small cluster of extreme statistical anomalies. Supporting this finding, the concentration ratio shows that the top three winning trades accounted for only 15.72% of total gross profits, even though the single best trade yielded +32.43%. On the downside, the single worst trade was capped at -7.24%, confirming effective risk containment relative to favorable trade outcomes.
4. Risk, drawdown and losing behavior
Risk metrics across the historical sample showcase remarkable capital protection relative to overall profitability. The strategy recorded a maximum peak-to-trough drawdown of 7.34%. In the context of a 2148.41% cumulative return, this creates an exceptionally favorable return-to-drawdown ratio, illustrating that compounding was achieved without exposing capital to deep equity retracements. An examination of losing sequences explains how drawdown severity was kept minimal. The strategy experienced a maximum consecutive losing streak of only 2 trades across the entire 122-trade dataset, contrasted against a maximum winning streak of 18 consecutive positive trades. Because losing clusters were brief and single-trade downside was historically limited to -7.24%, equity curves experienced rapid recovery phases following adverse execution events. For algorithmic trading agents operating under automated risk constraints, this controlled drawdown profile represents a stabilizing operational factor during challenging equity market regimes.
5. Behavior through time and yearly stability
An analysis of yearly performance reveals persistent positive return generation across every single calendar year represented in the dataset. Following an initial single trade in late 2019 (+0.09%), full-year active operations commenced in 2020, delivering a cumulative yearly gain of +94.55% across 21 trades with an 80.95% win rate. Subsequent years maintained consistent profitability: 2021 recorded +52.57% across 17 trades (88.24% win rate), while 2022 generated +79.41% across 23 trades (86.96% win rate). Trade frequency and return distribution shifted slightly in later periods while remaining firmly profitable. In 2023, the model closed 10 trades with a 90.00% win rate for a combined +40.57% return. In 2024, performance totaled +31.46% across 15 trades with a 93.33% win rate. In 2025 and 2026, win rates moderated to 72.22% (18 trades, +25.86%) and 76.47% (17 trades, +27.23%), respectively. The historical record indicates that while total annual percentages varied based on market volatility and trend persistence, the strategy demonstrated uninterrupted yearly profitability across multiple market environments.
6. Recent period since 2024-06-01 versus full history
Evaluating the recent operational window beginning June 1, 2024, offers insight into modern performance persistence. During this recent testing window, the strategy executed and closed 46 trades, representing over 37% of its total lifetime historical sample. Across these recent positions, the model accumulated a sum of trade returns equal to +81.36%. Comparing this recent period to the multi-year history demonstrates robust operational continuation. The strategy maintained active position turnover, averaging approximately 1.6 trades per month during this phase, which matches its long-term average of 1.74 trades per active month. The generation of +81.36% across 46 closed trades confirms that the model's edge was not restricted to earlier historical regimes, but continued to capture structural price movements in CCEP throughout recent market conditions.
7. Strengths and limitations
The primary analytical strength of this algorithmic strategy resides in its outstanding historical win rate of 83.61% combined with a tightly constrained maximum drawdown of 7.34%. The close alignment between the average trade (+2.88%) and median trade (+2.63%), alongside a modest top-three winner concentration of 15.72%, indicates a stable return distribution that does not rely on rare outlier events. Additionally, the low execution frequency (17.35 trades per year) naturally limits trade exposure periods. Conversely, important structural limitations must be noted. Operating with an average holding time of 198.08 hours requires market participants to aCCEPt multi-day overnight and weekend exposure in equity markets, introducing susceptibility to unexpected gap risk. Furthermore, because the model averages fewer than two completed trades per month, trading bots deploying this strategy will experience long periods of inactivity, requiring strict adherence to automated execution rules without manual interference during quiet market cycles.
8. DevioLab analytical conclusion
With a DevioLab score of 85.01 and a rank of 1 for the CCEP ticker, strategy CCEP 0 +2148.41% 1TRAD-SSG7 stands out as an exemplary low-frequency quantitative model. By synthesizing 15-minute intraday execution precision with multi-day position holding, the strategy achieves an attractive balance between high hit rate (83.61%), solid profit factor (2.50), and strict drawdown mitigation (7.34%). For quantitative researchers and developers configuring automated trading robots or trading agents for equity assets, this model offers a compelling case study in structural selectivity. The empirical evidence demonstrates that high cumulative returns (+2148.41% over 7.03 years) can be generated without high-frequency trading churn, provided the underlying decision engine effectively filters low-conviction market noise.
9. Data scope and methodology
This analysis is based strictly on historical backtested data generated for the strategy CCEP 0 +2148.41% 1TRAD-SSG7 on the 15-minute price interval covering the period from September 16, 2019, to September 27, 2026. All metrics, including closed trade returns, holding durations, drawdown figures, and yearly breakdowns, reflect simulated performance calculated across 122 completed trade events. These statistical findings are presented solely for quantitative research and analytical evaluation purposes and do not constitute financial advice, investment recommendations, or live account performance guarantees. Real-world implementation through automated trading bots or manual execution may differ due to factors such as execution slippage, broker fees, corporate actions, and evolving market liquidity conditions.