BMY · Bristol-Myers Squibb Company
Stock Universe · globaler AktienmarktHigh-Win-Rate Positional Execution on BMY: A Quantitative Strategy Analysis of the 15-Minute Automated Trading Model
An in-depth quantitative study of the BMY 15-minute algorithmic strategy, which generated a cumulative backtested performance of +621.39% across 7.02 years of simulated history. Ranking first for BMY on DevioLab with a score of 79.62, the strategy relies on low-frequency, long-duration position holding, achieving an 80.82% win rate across 73 completed trades. This research dissects the mathematical interplay between exceptional hit consistency, a modest Profit Factor of 1.25, a controlled maximum drawdown of 12.62%, and accelerated trade activity in the recent period following June 2024.
Analyse vom 24.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
1. Strategy profile
The strategy designated BMY 0 +621.39% 1TRAD-ISN4 represents the primary core quantitative model evaluated for BMY stock on DevioLab. Designed to operate on a 15-minute chart interval, this algorithmic strategy converts high-frequency price data into macro-level positional signals suitable for execution by automated trading bots, trading robots, or quantitative trading agents. Over an evaluation span of 7.02 years—stretching from September 16, 2019, through September 24, 2026—the model registered a cumulative trade sum of +621.39%, achieving a top rank of 1 for the ticker with a DevioLab Score of 79.62. Rather than engaging in hyper-active intraday churn, the strategy utilizes the 15-minute timeframe to pinpoint entry and exit timing while maintaining a remarkably patient approach to market exposure.
2. Trading rhythm and position duration
A defining characteristic of this strategy is the structural contrast between its underlying 15-minute execution timeframe and its prolonged holding periods. Across 7.02 years of backtested history, the strategy closed only 73 trades, translating to an average frequency of 10.39 trades per year, or approximately 1.20 trades per active month. Position durations are exceptionally long for a lower-timeframe model: the average holding time stands at 562.27 hours (approximately 23.4 days), while the median holding time is 432.00 hours (18.0 days). Furthermore, the time between completed trades is substantial, averaging 35.11 days with a median interval of 31.48 days. This low trade frequency demonstrates that the strategy functions as a multi-week position management engine rather than a rapid intraday system. For deployment in automated trading environments, this cadence minimizes transaction frequency and avoids short-term noise, though it requires trading agents to hold open positions through multi-day market fluctuations.
3. Quality of historical results
The strategy's performance profile is anchored by an extraordinarily high hit rate. Out of 73 completed trades, 59 ended in profit and 14 resulted in losses, establishing a overall win rate of 80.82%. Equity growth is characterized by consistent incremental gains: the average trade yielded +3.08%, closely aligning with the median trade return of +2.96%. The single best trade reached +15.12%. Notably, gross profitability is not reliant on a few extreme statistical outliers; the top three winning trades account for just 14.27% of total gross profit, indicating that historical returns were broadly distributed across the winning trade population. However, an important analytical tension exists between the high win rate and the strategy's overall Profit Factor, which stands at a modest 1.25. This divergence indicates an asymmetrical payoff ratio: while the algorithm wins more than four out of every five trades, its losing trades carry a higher average magnitude relative to its standard winning gains.
4. Risk, drawdown and losing behavior
Risk management metrics reveal an orderly structural profile with localized exposure bounds. The strategy's historical maximum drawdown was held to 12.62%, a figure that directly matches its single worst trade loss of -12.62%. This identity between maximum drawdown and worst trade indicates that equity declines were primarily driven by individual trade exits rather than compounding cascades of consecutive losses. The algorithm exhibited strong sequential reliability, recording a peak winning streak of 13 trades against a maximum losing streak of only 2 trades. Because losing streaks remain strictly bounded, historical drawdowns were brief in duration rather than long structural slumps. Nevertheless, the presence of an individual -12.62% loss alongside a median trade return of +2.96% underscores that position risk is heavily weighted toward tail-end stop exits, requiring disciplined capital allocation when configuring automated trading bots.
5. Behavior through time and yearly stability
An examination of the yearly breakdown demonstrates consistent positive trajectory across differing market environments, with profitability recorded in seven out of eight calendar years. The strategy opened with flawless win rates during its initial cycles: 2019 generated +18.50% across 3 trades (100% win rate), 2020 yielded +28.20% across 10 trades (100% win rate), and 2022 produced +51.76% across 9 trades (100% win rate). The single unprofitable calendar period occurred in 2023, when the strategy logged a modest net loss of -3.18% across 9 trades, accompanied by a reduced win rate of 55.56%. Performance recovered swiftly in subsequent years: 2024 produced +43.61% across 12 trades (75.00% win rate), 2025 delivered +44.70% across 13 trades (76.92% win rate), and the partial year 2026 contributed +28.11% across 9 trades (77.78% win rate). This longitudinal stability highlights a durable operational framework capable of navigating multi-year stock cycles without structural breakdown.
6. Recent period since 2024-06-01 versus full history
Evaluating the evidence window from June 1, 2024, to September 24, 2026, reveals a noticeable shift in execution pacing and density. During this recent 2.3-year window, the strategy completed 29 trades—representing roughly 39.7% of its total lifetime trades—and accumulated a combined profit sum of +104.59%. This translates to an increased average activity rate of more than 12 trades per year, compared to the overall historical baseline of 10.39 trades per year. The recent performance confirms that the strategy's signal generation mechanism remained highly active and productive in modern market conditions. Rather than suffering from alpha degradation or reduced signal fidelity, the strategy delivered over a hundred percentage points of net trade gains in the recent sample, validating its continued historical relevance.
7. Strengths and limitations
The primary analytical strength of this strategy lies in its exceptional consistency, highlighted by an 80.82% win rate, a maximum losing streak of just 2 trades, and a low reliance on profit concentration (top 3 winners at 14.27%). Its low trade frequency (10.39 trades per year) makes it highly manageable for execution engines and reduces operational overhead. Conversely, the principal limitation is the asymmetry between average gains and worst-case losses, reflected in the Profit Factor of 1.25. Because the worst historical trade (-12.62%) is roughly four times larger than the median trade gain (+2.96%), a small cluster of losses can temporarily neutralize several months of accumulated profits. Additionally, the small sample size of 73 completed trades over 7.02 years means that individual trade outcomes exert a strong statistical weight on overall metrics.
8. DevioLab analytical conclusion
The BMY 15-minute core strategy establishes a compelling quantitative model centered on patience, high probability execution, and controlled downside risk. By combining a 15-minute signal window with multi-week position holding times averaging 562.27 hours, the algorithm filters out short-term market noise while capturing larger trend segments in BMY stock. Ranking first on DevioLab with a score of 79.62, the strategy is well suited as an algorithmic logic for automated trading bots and position-focused trading agents. Traders considering this quantitative model should remain conscious of the tension between its 80.82% win rate and its 1.25 Profit Factor, ensuring that risk controls are configured to absorb occasional deeper trade exits without disrupting long-term equity growth.
9. Data scope and methodology
This analysis is based entirely on historical backtested trade results for BMY stock generated between September 16, 2019, and September 24, 2026. All statistics, including win rates, holding durations, drawdowns, and profit sums, derived from simulated closed trade logs evaluated on a 15-minute chart interval. Performance metrics do not reflect live brokerage account trading, actual order execution fees, slippage, or borrowing costs. Historical backtest results provide statistical context regarding algorithmic behavior but do not guarantee future performance. This publication is provided strictly for educational and quantitative research purposes and does not constitute investment advice or financial recommendations.