COR · Cencora Inc.
Stock Universe · global stock marketCOR · COR Algorithmic Trading Strategy Analysis: High Win-Rate Multi-Day Architecture with Low Winner Concentration
This quantitative evaluation analyzes a top-ranked algorithmic trading strategy designed for COR stock on the 15-minute timeframe. Across a 7.06-year backtested history comprising 100 closed trades, the strategy achieved an 86.00% win rate, a Profit Factor of 3.75, and a cumulative gain of +3,576.74%. Utilizing multi-day position durations averaging 369.32 hours (approximately 15.39 days), the system exhibits a controlled maximum drawdown of 9.41% alongside remarkably balanced profit distribution, where the top three winning trades account for only 11.86% of gross historical returns. Suitable for deployment in automated trading bots, trading robots, and execution agents, the system combines persistent trade expectancy with low losing streak vulnerability.
Analysis as of 09.10.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The evaluated algorithmic model for COR operates on 15-minute price granularity, achieving the top rank (rank 1) among quantitative models tested on this asset with an overall DevioLab sCORe of 85.64. Over a historical dataset spanning from September 16, 2019, to October 9, 2026 (7.06 years), the strategy executed exactly 100 closed trades, generating a total cumulative gain of +3,576.74% and an annualized rate of return of 226.14%. Designed to function as the decision logic for automated trading bots, trading robots, or quantitative trading agents, the system prioritizes high-probability entries coupled with structural risk management. The dataset reveals 86 winning trades against 14 losing trades, establishing an 86.00% hit rate and an overall Profit Factor of 3.75. This fundamental statistical baseline reflects a model built around consistent capital accumulation across multi-day holding periods rather than high-frequency noise exploitation.
2. Trading rhythm and position duration
Despite using a 15-minute bar interval for data sampling, the strategy operates as a multi-day position trading system rather than an intraday scalper. The average position holding time stands at 369.32 hours (approximately 15.39 days), while the median holding duration is 264.88 hours (roughly 11.04 days). Position exits occur with significant spacing, averaging 25.75 days between historical closes (with a median interval of 20.97 days). This low exit frequency translates into a trading rhythm of 14.16 trades per year, or approximately 1.45 trades per active month. Automated execution agents deploying this strategy experience extended periods of market exposure followed by substantial inactive windows, reducing reliance on micro-second execution speed and mitigating the friction of frequent turnover.
3. Quality of historical results
The statistical distribution of trade gains demonstrates remarkable stability across the 100-trade sample. The strategy boasts an average trade return of +3.98% alongside a median trade return of +3.92%. The near-perfect alignment between mean and median figures indicates that historical profitability was driven by a repeatable payoff CORe rather than a skewed cluster of extreme outliers. Furthermore, the single best trade yielded +23.00%, while the largest three winning trades combined represent only 11.86% of total gross historical profit. This exceptionally low concentration ratio confirms that strategy returns are distributed across dozens of successful trades. The strategy's Profit Factor of 3.75 is supported by a average-winner to average-loser ratio that compensates well for occasional adverse moves.
4. Risk, drawdown and losing behavior
Risk containment represents a central strength of this quantitative model. The maximum historical drawdown reCORded across the entire 7.06-year test window was 9.41%, matching the magnitude of the single worst historical trade (-9.41%). The strategy experienced a maximum consecutive losing streak of just 2 trades, contrasted against a maximum winning streak of 18 consecutive trades. Out of 100 closed positions, only 14 resulted in negative returns. The tight coupling between the worst single trade (-9.41%) and the peak-to-trough drawdown (9.41%) suggests that equity curves were rarely burdened by compounding consecutive losses or severe market tail events during open positions, providing a stable equity progression for automated execution.
5. Behavior through time and yearly stability
The annual breakdown confirms consistent profitability across all individual calendar years within the sample period. In 2019, the strategy executed 3 trades (100.00% win rate, +16.34% sum). Performance expanded in 2020 with 19 trades, an 94.74% win rate, and +107.16% total return. Subsequent years showed steady activity: 2021 generated +49.80% across 14 trades (85.71% win rate); 2022 produced +72.63% over 18 trades (88.89% win rate); 2023 yielded +14.61% across 8 trades (75.00% win rate); and 2024 delivered +28.10% over 9 trades (77.78% win rate). Multi-year consistency continued into 2025 with 16 trades yielding +70.26% (87.50% win rate) and 2026 adding +38.95% over 13 trades (76.92% win rate). The system demonstrates persistent edge without long multi-year stagnation phases.
6. Recent period since 2024-06-01 versus full history
Evaluating recent trade performance from June 1, 2024, to October 9, 2026, provides a critical window into modern strategy efficacy. During this recent sub-period, 34 completed trades were logged, generating a cumulative return sum of +128.99%. This trade volume accounts for 34.00% of all historical trades completed by the strategy over its 7.06-year history. Comparing recent metrics against the long-term history demonstrates that the model's structural edge has remained intact in modern market regimes, maintaining high trade production and robust positive expectancy without suffering structural degradation or operational drift.
7. Strengths and limitations
Key statistical strengths include an exceptionally high 86.00% win rate, a well-controlled maximum drawdown of 9.41%, a strong Profit Factor of 3.75, and a remarkably low winner concentration ratio (top 3 winners account for 11.86% of gross profit). The alignment of mean (+3.98%) and median (+3.92%) trade returns emphasizes system reliability. Primary limitations stem from the modest sample size of 100 total completed trades over 7.06 years, reflecting a low exit frequency of 14.16 trades per year. Traders utilizing automated trading bots must accept multi-week holding periods (averaging 15.39 days per trade) and extended flat periods between execution cycles.
8. DevioLab analytical conclusion
With a DevioLab sCORe of 85.64 and a rank of 1 for COR, this algorithmic strategy presents a robust quantitative profile characterized by balanced equity growth, minimal losing streaks, and limited drawdown depth. Its reliance on multi-day position holding allows it to capture substantial trend swings on COR while bypassing the market friction inherent in high-frequency intraday approaches. The model offers strong potential as decision logic for automated trading systems and execution agents designed for low-frequency, high-expectancy stock trading.
9. Data scope and methodology
This analysis is based entirely on historical backtested closed trade statistics for the algorithmic strategy applying to COR on the 15m timeframe between September 16, 2019, and October 9, 2026. All statistics, including win rates, trade sums, drawdowns, and holding durations, represent simulated closed historical trade outcomes. Performance calculations do not account for real-world execution slippage, broker commissions, borrow costs, or unexpected exchange outages. Historical backtested results are provided strictly for research and analytical purposes and do not guarantee future profitability or live execution outcomes.