CI · The Cigna Group
Stock Universe · globaler AktienmarktCI Algorithmic Strategy Analysis: High Win Rate Swing Logic and Exceptional Risk Control in CI Stock Trading
This quantitative evaluation analyzes an elite algorithmic trading strategy designed for CI stock. Operating across a seven-year historical dataset from September 2019 to September 2026, the strategy achieves a remarkable 89.29% win rate across 56 completed trades, yielding a cumulative backtested return of +2303.15% with a Profit Factor of 7.50. Despite utilizing a 15-minute price bar interval for execution timing, the logic maintains a patient swing-trading structure, averaging 491.36 hours (approximately 20.5 days) per position. Outstanding risk containment is evidenced by a maximum historical drawdown capped at just -5.17%, matching its worst individual trade loss. Furthermore, gross profits display exceptional structural health, with the top three winning trades accounting for only 18.31% of aggregate gains. With a DevioLab Score of 89.88 and ranking first overall for the CI ticker, this strategy provides a robust deCIsion logic framework for deployment within automated trading bots, trading robots, and quantitative trading agents.
Analyse vom 02.10.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The algorithmic trading strategy evaluated for CI stock represents a speCIalized quantitative model optimized for medium-term position holding while capturing intraday preCIsion. Evaluated on a 15-minute candle interval over a seven-year evaluation window spanning from September 16, 2019, to September 14, 2026, the strategy executed a total of 56 completed trades. Across this multi-year history, the model delivered a total cumulative profit of +2303.15%, corresponding to an annualized historical return benchmark of 219.71% under the backtested evaluation parameters. The strategy holds the top rank (#1) for the CI asset ticker on DevioLab with a DevioLab Score of 89.88, reflecting its exceptional balance between directional accuracy, drawdown containment, and payoff consistency. Designed as a high-conviction quantitative model, its deCIsion rules are highly suitable for implementation in automated trading bots, trading robots, and algorithmic trading agents that require strict risk management and non-frequent, high-probability execution signals.
Trading rhythm and position duration
Although the underlying market data feeds into a 15-minute chart resolution, the historical trade distribution demonstrates that the strategy operates strictly as a multi-week swing model rather than an intraday scalper. Over the entire seven-year history, the strategy registered an average trade frequency of approximately 8 completed trades per year, or 1.17 trades per active month. The temporal spaCIng between position exits reinforces this selective rhythm, exhibiting an average exit interval of 45.36 days and a median interval of 33.99 days. Position holding times reflect patient exposure: the average holding duration stands at 491.36 hours (roughly 20.5 calendar days), while the median holding time is 347.25 hours (approximately 14.5 days). The positive right-skew between median and mean holding times indicates that while many trades achieve their target objectives within two weeks, selected position runs extend over multiple weeks to capture longer structural price swings. This low trade veloCIty minimizes trading exposure, allowing automated trading agents to maintain low system activity while holding positions long enough to ride secondary equity trends.
Quality of historical results
The structural quality of the strategy's equity curve is characterized by extreme hit-rate preCIsion paired with balanced profit distribution. Out of 56 historical closed trades, 50 produced positive returns, yielding an overall win rate of 89.29%. The overall Profit Factor reaches 7.50, meaning that aggregate gross winning trades generated seven and a half times the capital lost on aggregate losing trades. The average return per completed trade sits at +6.21%, closely tracking the median trade return of +5.25%. This narrow gap between average and median performance proves that historical profitability is driven by predictable core trade gains rather than skewness from single luck-driven events. The single best historical trade yielded +29.18%, whereas the worst trade produced a loss of -5.17%. CruCIally, the top three winning trades combined generated only 18.31% of total gross profits. This exceptionally low profit concentration confirms that historical success is broadly distributed across the 50 winning trades rather than dependent on a few isolated market spikes.
Risk, drawdown and losing behavior
Risk containment within this strategy is notably rigid. The maximum historical portfolio drawdown recorded across the entire seven-year dataset is -5.17%. Strikingly, this maximum drawdown figure exactly equals the magnitude of the single worst historical trade loss (-5.17%). This numerical identity reveals that historical drawdowns were entirely dictated by single trade occurrences rather than cumulative compounding loss streaks. Over 56 trades, the strategy never experienced more than a single losing trade in sequence, yielding a maximum losing streak of exactly 1 trade. Conversely, the strategy achieved a maximum consecutive winning streak of 15 trades. Because losses never clustered back-to-back, account equity experienced near-immediate recovery following unprofitable exits. For risk-averse quantitative allocators and automated trading bots, this structural loss isolation presents a highly favorable profile, preventing compounding drawdowns during extended market stress.
Behavior through time and yearly stability
A year-by-year examination of backtested trades illustrates continuous profitability across diverse market environments, with zero calendar years ending in a net loss. Performance began in late 2019 with 1 trade delivering a +29.18% gain. In 2020, activity expanded to 12 trades with an 91.67% win rate (11 wins, 1 loss) yielding +68.52% total gain. Subsequent years maintained high win rates and steady cumulative returns: 2021 delivered +30.92% across 5 trades (80% win rate); 2022 generated +42.72% across 5 trades with a perfect 100% win rate; 2023 yielded +38.37% over 8 trades (75% win rate); 2024 produced +29.58% across 7 trades (85.71% win rate); 2025 delivered +71.54% over 11 trades with another 100% win rate; and 2026 recorded +37.09% over 7 trades (85.71% win rate). Trade distribution remained remarkably stable throughout, osCIllating between 5 and 12 trades per full year, proving that the algorithm consistently identifies high-probability entry criteria across different market cycles.
Recent period since 2024-06-01 versus full history
Evaluating recent dataset activity from June 1, 2024, onward reveals an acceleration in trade signal completion without any deterioration in quality. Within this recent window, the strategy executed 23 completed trades, achieving a cumulative return sum of +125.81%. This recent segment accounts for approximately 41% of all historical trades executed over the entire seven-year timeline. The increase in trade frequency during this period demonstrates active ongoing signal generation in contemporary CI stock price dynamics. Comparing recent returns against the broader history indicates that the underlying core edge has remained functional and robust, generating significant multi-trade gains without triggering structural drawdowns or altering the long-term trade holding characteristics.
Strengths and limitations
The primary strength of this algorithmic strategy lies in its outstanding reliability metrics: an 89.29% win rate, a 7.50 Profit Factor, a modest -5.17% maximum drawdown, and an absolute absence of consecutive losing streaks. The even dispersion of profits—evidenced by the 18.31% top-three winner concentration and close alignment between average (+6.21%) and median (+5.25%) returns—underlines a repeatable quantitative process. Conversely, the main limitation of the strategy is its low absolute sample size of 56 total trades over seven years, averaging just 8 trades per year. Extended waiting intervals averaging 45 days between trade exits demand substantial operational patience from traders. automated trading robots operating this logic must be programmed to withstand lengthy inactive periods without force-executing sub-optimal trade setups.
DevioLab analytical conclusion
Earning a DevioLab Score of 89.88 and holding the top ranking for CI equity strategies, this algorithmic model represents an exemplary swing-trading deCIsion framework. The empirical data highlights a system that prioritizes trade selection quality over frequency, successfully suppressing equity volatility while compounding returns over multi-week holding frames. The symmetry between the maximum historical drawdown (-5.17%) and the worst individual trade (-5.17%) demonstrates exceptional downside mitigation. For quantitative developers and deployment frameworks seeking an institutional-grade logic for automated trading agents, this strategy provides a compelling historical track record built on broad-based edge distribution and rigorous capital protection.
Data scope and methodology
The statistics CIted in this research article are derived exclusively from historical closed trade data for CI stock evaluated on a 15-minute timeframe over the period spanning September 16, 2019, through September 14, 2026. All metric computations—including win rates, holding durations, yearly breakdowns, drawdowns, and profit factor—reflect exact historical backtested executions. These findings represent simulated performance metrics intended strictly for quantitative evaluation and research. Historical trading results do not guarantee future returns, and live operational factors such as order execution latency, market slippage, or fee structures are not accounted for in backtested performance metrics.