CCL · Carnival Corporation Ltd.
Stock Universe · globaler AktienmarktQuantifying Consistency and Asymmetry: An Analytical Evaluation of the 15-Minute Algorithmic Strategy for CCL · CCL
This quantitative analysis examines the historical performance of the core 15-minute algorithmic trading strategy for CCL across a 7.04-year evaluation period from September 2019 to September 2026. Generating a total cumulative return of +21,573.05% across 154 closed trades, the strategy exhibits an exceptionally high win rate of 73.38% and a robust profit factor of 2.50. Rather than relying on isolated windfall trades, its profit profile is remarkably well-distributed, with the top three winning positions accounting for only 11.72% of gross profit. However, the model presents significant tail-risk exposure, characterized by a worst single trade loss of -65.44% and a maximum drawdown of 68.00%. Designed to serve as the decision engine for automated trading bots and trading agents operating in swing-trading regimes, this strategy provides strong overall statistical edge while highlighting the absolute necessity of strict equity preservation controls.
Analyse vom 28.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The algorithm designated as CCL 0 +21573.05% 1TRAD-RYP5 operates on 15-minute price bars for CCL stock, serving as the decision logic for automated trading bots and trading agents seeking systematic exposure to medium-term price swings. Across a dataset spanning 7.04 years from September 16, 2019, to September 28, 2026, the strategy completed 154 closed trades, achieving a cumulative simulated return of +21,573.05% and an annualized equivalent metric of 340.19%. Within the DevioLab quantitative evaluation framework, the strategy earned a score of 54.36, establishing it as the rank 2 strategy overall for the CCL ticker. The model maintains a core designation, reflecting its status as an independent top performer within its asset class.
Trading rhythm and position duration
Although the strategy samples market data on a granular 15-minute timeframe, its actual execution tempo reflects a deliberate multi-day swing trading rhythm rather than high-frequency scalping. The strategy registers an average trade frequency of 21.89 trades per year, which translates to approximately 2 completed positions per active month. Position holding durations vary considerably: the average holding time is 251.54 hours (roughly 10.5 days), whereas the median holding duration is lower at 167.38 hours (approximately 7.0 days). This positive skew in duration indicates that while the majority of positions resolve within a single calendar week, selected positions extend across several weeks to capture sustained trends. Exits occur at measured intervals, averaging 16.45 days between trade closures with a median gap of 13.93 days. This paced execution structure reduces trade turnover, making the quantitative logic well-suited for trading agents designed to execute systematically without generating excessive trading noise.
Quality of historical results
The structural strength of this algorithm lies in its exceptional win rate and remarkably broad distribution of gains. Out of 154 total completed trades, 113 yielded positive returns while 41 closed at a loss, yielding a win rate of 73.38%. The overall profit factor stands at a healthy 2.50. The mathematical alignment between the average trade return of +4.54% and the median trade return of +4.35% demonstrates high empirical stability; the strategy's baseline expectation is not artificially inflated by a handful of extreme outliers. This stability is further reinforced by the concentration metric: the top three winning trades contributed just 11.72% of total gross profit. Even though the single best trade achieved a remarkable gain of +43.13%, the strategy does not depend on unpredictable tail-event windfalls to maintain profitability. Instead, performance is generated by repeatable, mid-sized gains across consecutive trade cycles.
Risk, drawdown and losing behavior
Despite its dominant win rate, the historical data reveal an asymmetric vulnerability during adverse market movements. The strategy experienced a peak-to-trough maximum drawdown of 68.00%, driven principally by deep single-trade losses rather than extended losing streaks. While the longest historical losing streak was capped at just 4 consecutive trades, the strategy's worst single trade recorded a severe decline of -65.44%. In contrast, the algorithm demonstrated exceptional positive momentum during favorable regimes, achieving a longest winning streak of 19 consecutive profitable trades. This dynamic highlights a distinct operational tension: the strategy possesses an exceptionally high probability of closing trades in profit, but when failure occurs, loss magnitude can severely impact account equity. Consequently, deployment within automated trading robots demands rigorous overlay controls to manage unhedged downside exposure.
Behavior through time and yearly stability
An analysis of the annual breakdown confirms that the strategy's historical performance is structurally persistent across changing market regimes rather than concentrated in a single fortunate year. Performance remained positive in every calendar year from 2019 through 2026. In 2020, during period-wide equity market volatility, the algorithm delivered its highest total annual return of +200.28% across 25 trades with a 72.00% win rate. Subsequent years maintained consistent profitability: 2021 generated +50.98% across 25 trades (80.00% win rate), 2022 produced +118.30% across 23 trades (65.22% win rate), and 2023 yielded +97.54% across 17 trades (58.82% win rate). The model experienced a strong resurgence in 2024 (+82.03% across 23 trades, 78.26% win rate) and 2025 (+91.70% across 23 trades, 86.96% win rate). Through the partial dataset for 2026, the strategy closed 16 trades with a 62.50% win rate, adding +31.10%. This unbroken multi-year sequence of positive sum returns illustrates high consistency.
Recent period since 2024-06-01 versus full history
Evaluating recent trade performance starting from June 1, 2024, provides a modern benchmark against the full 7-year backtest dataset. Within this recent evidence window, the strategy executed 54 completed trades, accumulating a combined return sum of +181.81%. This constitutes more than one-third of all historical trade executions occurring within the final 28 months of the test window, demonstrating that the strategy's trading frequency and exit trigger mechanisms have remained highly active in recent market conditions. Comparing the recent performance density with earlier multi-year averages reveals that the algorithm's statistical edge has remained intact, maintaining both trade generation pace and robust positive performance without showing signs of decay or alpha degradation.
Strengths and limitations
The primary strength of the strategy is its high statistical reliability, headlined by a 73.38% historical win rate, a 2.50 profit factor, and a 19-trade winning streak capacity. Furthermore, gains are widely distributed across trades, as evidenced by the low 11.72% contribution share from the top three winners and the close alignment between average (+4.54%) and median (+4.35%) trade expectations. Its continuous yearly profitability from 2019 to 2026 further underscores its structural stability. Conversely, the primary limitation is severe tail-risk vulnerability, marked by a -65.44% worst trade and a 68.00% maximum historical drawdown. Additionally, with an average frequency of 21.89 trades per year, trading agents operating this model must be built to handle long inactive periods between executions without forcing sub-optimal entries.
DevioLab analytical conclusion
The CCL 0 +21573.05% 1TRAD-RYP5 algorithmic strategy demonstrates a highly effective quantitative framework for capturing medium-term price movements in CCL stock. Achieving a DevioLab score of 54.36 and ranking second among strategies for this asset, the algorithm's strength lies in its consistency, high win rate, and non-concentrated profit generation across multi-day holding periods. However, the presence of a 68.00% historical maximum drawdown underlines the vital distinction between high-probability trade engines and comprehensive risk management platforms. For quantitative developers implementing this logic into live trading bots or automated execution software, integrating portfolio-level stop-loss mechanisms and dynamic position sizing will be essential to mitigate tail-risk losses while preserving the underlying historical edge.
Data scope and methodology
This analysis is derived entirely from historical backtested execution data for CCL on a 15-minute chart interval spanning September 16, 2019, through September 28, 2026. All reported statistics, including trade counts, win rates, holding times, and return metrics, describe closed, simulated trade outcomes without accounting for transaction costs, commissions, slippage, order routing delays, or borrowing fees. The recent evaluation window starting June 1, 2024, captures all completed trades whose sell timestamps fall on or after that date. Historical backtested performance does not represent live operational trading and provides no guarantee of future trading performance.