CNC · Centene Corp.
Stock Universe · mercado global de açõesCNC Quantitative Strategy Analysis: Evaluating a Multi-Day Swing Logic Ranking First on CNC Stock
This analytical report evaluates the historical performance of a core quantitative trading strategy developed for CNC stock on a 15-minute execution frame. Achieving a DevioLab score of 91.23 and ranking first among tested models for this ticker, the strategy exhibits an extraordinary historical win rate of 88.24% across 68 completed trades spanning more than seven years of backtested history from September 2019 to October 2026. Rather than relying on rapid intra-day turnover, the model executes a low-frequency, extended-holding swing approach with an average position duration of 428.42 hours (approximately 17.8 days) and a profit factor of 7.50. With a maximum historical drawdown strictly bounded at 5.77% and a top-three winner concentration of just 18.33%, the statistical profile indicates a highly balanced trade distribution rather than outlier-dependent returns. Designed as decision logic suitable for deployment via automated trading bots and algorithmic trading agents, this strategy provides a compelling case study in structural risk containment and low-frequency position trading within equity markets.
Análise em 06.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The algorithmic trading strategy examined in this report, identified in the platform catalog as CNC 0 +11218.45% 1TRAD-RJB3, is a specialized quantitative model configured for CNC stock using a 15-minute price bar interval. Within the DevioLab framework, the strategy holds the top rank (rank 1) for the CNC ticker, earning a high DevioLab score of 91.23 and earning a core independent selection designation. Evaluated across a total statistical history of 7.06 years ranging from September 16, 2019, through October 6, 2026, the strategy recorded 68 closed trades. Over this sample, it achieved a closed-trade net gain sum of +11,218.45%, corresponding to an annualized rate of 576.83%. In practical deployment contexts, such quantitative models serve as the underlying signal engine for execution infrastructure, enabling trading bots and automated trading agents to implement rule-based entries and exits without emotional intervention. The strategy operates with remarkable selectivity, maintaining an overall historical win rate of 88.24% with 60 winning positions against only 8 losing trades. This profile establishes a distinct quantitative signature characterized by infrequent market participation, high directional fidelity, and strict risk discipline.
Trading rhythm and position duration
Despite utilizing 15-minute price data for entry and exit calculations, the strategy's operational rhythm is fundamentally that of a multi-day to multi-week position trading system rather than a short-term intra-day scalper. The average position holding duration stands at 428.42 hours (roughly 17.85 days), while the median holding duration is 310.25 hours (approximately 12.93 days). This gap between mean and median indicates that while most trades resolve within roughly two weeks, select winning trades are allowed to mature over significantly longer windows to capture extended price trends. On a macro level, the system exits a position every 37.67 days on average, with a median exit interval of 28.04 days. The model generates approximately 9.64 trades per year, which translates to a modest 1.26 trades per active month. For algorithmic trading robots and execution agents, this low trade frequency eliminates high-turnover churn, allowing the automation logic to focus on high-conviction structural price swings while remaining inactive during ambiguous market phases.
Quality of historical results
The overall quality of the strategy's historical returns is underscored by a profit factor of 7.50, demonstrating that cumulative closed gains exceeded total closed losses by a factor of seven and a half. The model generated an average trade return of +7.72%, compared to a median trade return of +5.59%. This favorable alignment between average and median values confirms that positive performance is distributed broadly across the trade history rather than driven solely by a few accidental windfalls. The single best historical trade yielded +38.69%, while the worst trade was capped at -5.77%. A crucial quantitative test for any high-performing strategy is winner concentration: in this model, the three largest winning trades account for only 18.33% of gross profits. This exceptionally low concentration ratio proves that the strategy's equity curve relies on systematic, repeated statistical edges across dozens of trade cycles rather than extreme positive tail events.
Risk, drawdown and losing behavior
Risk metrics across the 7.06-year evaluation window showcase exceptional downside containment. The strategy's maximum drawdown recorded over the entire dataset is -5.77%, matching exactly the magnitue of its single worst trade (-5.77%). This mathematical congruence suggests that historical equity declines were primarily driven by single trade resolutions rather than cascading multi-trade drawdowns. The strategy achieved a maximum winning streak of 18 consecutive profitable trades, whereas its longest losing streak was capped at just 1 single loss throughout the 68-trade dataset. With only 8 total losing trades occurring across seven years, losing trades were isolated occurrences separated by extended sequences of profitable closed positions. For quantitative risk managers utilizing trading bots or automated agents, this structural asymmetry between drawdown (-5.77%) and historical cumulative upside provides a strong statistical buffer against strategy breakdown.
Behavior through time and yearly stability
Examining performance on a year-by-year basis demonstrates remarkable operational consistency across varying market regimes. Following an initial 2019 dataset consisting of 1 trade (+24.46%), the strategy delivered its highest volume year in 2020 with 16 trades, 15 wins (93.75% win rate), and a cumulative return sum of +141.18%. In subsequent years, trade frequency stabilized into a tight range of 6 to 10 trades per year: 2021 produced 6 trades (83.33% win rate, +48.25%), 2022 generated 9 trades (77.78% win rate, +39.74%), 2023 recorded 7 trades (100.00% win rate, +36.68%), and 2024 saw 10 trades (90.00% win rate, +36.09%). Performance accelerated sharply in 2025 with 9 trades (88.89% win rate, +85.46%) and continued strongly into 2026 with 10 trades (80.00% win rate, +112.86%). The strategy generated positive net aggregate returns in every single year of recorded history, demonstrating that its core logic remains stable across diverse market conditions.
Recent period since 2024-06-01 versus full history
A focused examination of the recent window starting June 1, 2024, provides strong empirical evidence regarding the strategy's ongoing relevance. In this recent window, which encompasses completed trades closed on or after June 1, 2024, the strategy executed 25 trades with a cumulative closed return sum of +216.82%. Representing nearly 37% of all historical trades completed over the 7-year backtest, this elevated activity level in recent periods confirms that the strategy's signal triggers have remained active and effective in modern market environments. Comparing the recent sub-sample with the long-term baseline reveals consistent performance density: average returns per trade in the recent window closely match historical long-term expectations, dispelling concerns regarding alpha decay or strategy degradation.
Strengths and limitations
The primary structural strengths of this quantitative model include an extraordinary historical win rate (88.24%), an exceptional profit factor of 7.50, an extremely low maximum drawdown (-5.77%), and a balanced return structure where the top three winners contribute less than 19% of gross profits. Furthermore, a maximum losing streak of just one trade highlights remarkable signal stability. Conversely, fundamental limitations must be acknowledged. With only 68 total trades over 7.06 years, the statistical sample size is relatively small, producing long operational periods of inactivity between trade exits (averaging 37.67 days). Market participants deploying automated trading agents must exercise patience, as the system does not offer frequent trading opportunities. Additionally, because positions are held for an average of nearly 18 days, trades remain exposed to overnight gaps and macro market shifts that cannot be managed intra-day.
DevioLab analytical conclusion
The CNC quantitative strategy stands out as a top-tier algorithmic model, earning a 91.23 DevioLab score and ranking first overall for CNC stock. By combining high directional accuracy with rigorous risk limits, the strategy achieves a rare balance between growth and capital preservation. Its reliance on long holding durations (median 310.25 hours) and selective trade execution (9.64 trades per year) allows it to bypass noise and capture substantial trend moves. For systematic investors seeking automated trading bot logic or decision models for trading agents, this strategy offers a robust historical blueprint. However, operators must maintain realistic expectations, recognizing that past backtested performance, high win rates, and limited drawdowns do not guarantee future profitability in changing market environments.
Data scope and methodology
This analysis is based strictly on historical simulated backtest data generated for CNC stock on a 15-minute timeframe from September 16, 2019, to October 6, 2026. All statistics, including completed trade counts, holding durations, win rates, drawdowns, and yearly performance breakdowns, reflect closed position results calculated within the DevioLab quantitative engine. The metric for recent performance isolates completed trades with exit timestamps on or after June 1, 2024 UTC. Performance percentages represent closed trade output and do not account for external execution slippage, broker fees, or variable spread costs unless explicitly stated. These historical research results are presented exclusively for analytical and educational purposes and do not constitute financial advice or trade recommendations.