BSX · Boston Scientific Corp.
Stock Universe · mercado bursátil globalBSX Multi-Week Quantitative Strategy: Analyzing an 89.74% Win Rate, 7.20% Drawdown, and 86.88 DevioLab Score
An in-depth quantitative examination of the top-ranked algorithmic trading strategy for Boston Scientific Corporation (BSX) on the 15-minute timeframe. Spanning over 7 years of backtested history across 78 completed trades, the strategy demonstrates a remarkable 89.74% win rate, a 3.75 Profit Factor, and a tightly controlled maximum drawdown of 7.20%. Despite operating on intraday 15-minute bar data, the model behaves as a selective multi-week position system with an average holding duration of 423.20 hours. Designed for implementation via automated trading bots and trading agents, the strategy exhibits broad-based equity growth where the top three winning trades contribute only 17.57% of total gross profits. This analysis reviews the strategy's historical performance, trade duration dynamics, risk metrics, annual performance consistency, and post-June 2024 empirical evidence.
Análisis al 25.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The algorithmic trading strategy evaluated for BSX on the 15-minute chart presents a distinct quantitative profile characterized by low turnover, high decision precision, and long holding horizons. Across a 7.03-year historical dataset extending from September 16, 2019, to September 25, 2026, the strategy generated a cumulative closed trade return sum of +3568.09%, translating to an annualized performance metric of 143.05%. With a DevioLab Score of 86.88, the strategy achieves the rank 1 position among evaluated models for BSX. Over the complete evaluation window, the algorithm completed exactly 78 trades, translating to an average trading frequency of 11.10 trades per year or roughly 1.32 trades per active month. Rather than exploiting brief microsecond mispricings or fast intraday swings, the model utilizes intraday 15-minute signals to establish multi-week trend positions. This structural design makes the logic highly suited for automated trading bots and trading robots that seek to capture persistent directional momentum without incurring high transaction velocity.
Trading rhythm and position duration
Although execution triggers are derived from 15-minute price action, the strategy operates on a timeline far removed from typical intraday scalping. The average holding duration for completed trades is 423.20 hours (approximately 17.6 days), while the median holding time stands at 287.88 hours (approximately 12.0 days). This disparity between average and median duration indicates that while most positions remain open for nearly two weeks, select trades extend significantly longer to capture multi-week trend continuations. Furthermore, exits are widely spaced in time: the average interval between consecutive position exits is 33.18 days, with a median exit spacing of 25.98 days. Automated trading agents executing this logic spend substantial periods waiting in cash or holding active positions through short-term market noise, executing on average barely more than one trade closure per month.
Quality of historical results
The statistical distribution of trade returns demonstrates exceptional consistency and balanced profit distribution. Out of 78 total completed trades, 70 were profitable and 8 resulted in losses, yielding a win rate of 89.74%. The mean trade gain across all closed positions is +5.10%, which closely tracks the median trade return of +4.79%. This tight alignment between average and median returns suggests that strategy gains are not driven by extreme statistical outliers, but rather by predictable profit capture across trade cycles. The strategy's single best trade produced a return of +42.05%, while the top three winning trades combined accounted for 17.57% of total gross profit. This modest profit concentration reinforces that the overall Profit Factor of 3.75 is backed by broad-based performance across dozens of winning trades rather than a handful of lucky executions.
Risk, drawdown and losing behavior
Risk parameters throughout the 7.03-year period remained within tight boundaries relative to net cumulative returns. The strategy's historical maximum drawdown was capped at 7.20%, which exactly matches the loss incurred in its single worst trade (-7.20%). The strategy recorded an extraordinary longest winning streak of 37 consecutive positive trades, while its longest losing streak was limited to just 1 trade. Because losing trades occurred in isolation rather than in consecutive clusters, automated trading bots executing this framework historically avoided severe equity curve compounding drawdowns. The balance between a 3.75 Profit Factor, an 89.74% win rate, and a 7.20% maximum drawdown reflects a trading architecture where downside exposure per trade was strictly contained.
Behavior through time and yearly stability
A year-by-year examination reveals a strategy that maintained net positive closed trade performance in every single calendar year from 2019 through 2026, though with evolving win rates and trade frequencies. Between 2019 and 2022, the algorithm achieved a perfect 100% win rate across 33 consecutive trades: 2019 (3 trades, +13.66% sum), 2020 (12 trades, +110.11% sum), 2021 (10 trades, +69.31% sum), and 2022 (8 trades, +50.68% sum). The first historical losing trade emerged in 2023, which closed with 9 trades, 8 wins, 1 loss (+35.18% sum, 88.89% win rate). The strategy returned to a 100% win rate in 2024 across 7 trades (+43.23% sum). In 2025, trade activity expanded to 15 completed trades (13 wins, 2 losses, +46.54% sum, 86.67% win rate). In 2026, trading activity remained elevated with 14 trades (9 wins, 5 losses, +29.09% sum, 64.29% win rate). While yearly profit sums remained positive throughout, recent years reflect a transition toward higher trade frequency and an increased occurrence of losing trades.
Recent period since 2024-06-01 versus full history
Analyzing the recent trading window beginning June 1, 2024, provides valuable insights into how the strategy has functioned in recent market dynamics. Out of 78 total lifetime trades, 33 completed trades occurred on or after June 1, 2024, generating a combined return sum of +100.51%. This reveals that nearly 42% of all historical trade closures occurred within the final two years of the dataset. Comparing this recent period to the full history demonstrates that trade generation accelerated substantially relative to the earlier 2019-2023 baseline. Although this accelerated trading pace coincided with a lower overall win rate due to the 5 losses recorded in 2026, the strategy continued to deliver substantial net capital growth (+100.51% sum). Automated trading agents deployed during this recent window experienced more frequent trade rotations while continuing to capture net positive trend movements.
Strengths and limitations
The primary statistical strengths of this BSX strategy include an exceptionally high overall win rate (89.74%), a low maximum drawdown (7.20%), a robust Profit Factor (3.75%), and minimal dependency on top winning trades (top 3 winners account for only 17.57% of gross profits). Its low turnover rate reduces potential trade management friction for automated trading robots. Conversely, the strategy's primary limitation is its sample size: 78 completed trades over 7.03 years represents a modest sample count, meaning statistical metrics carry wider confidence intervals than strategies with hundreds of trades. Additionally, the drop in annual win rate to 64.29% in 2026 indicates that market conditions in later periods introduced more choppy price action, requiring realistic risk expectations during shifting volatility regimes.
DevioLab analytical conclusion
Securing the rank 1 position for BSX with a DevioLab Score of 86.88, this 15-minute quantitative strategy offers a compelling template for systematic position trading. By utilizing short-timeframe data to enter positions that are held for an average of 17.6 days, the algorithm effectively bridges the gap between intraday precision and multi-week trend capture. For quantitative developers and traders evaluating automated trading bots, the backtested profile demonstrates a strong history of drawdown containment alongside broad-based gain distribution. However, practitioners should take into account the moderate sample size of 78 trades and the shift toward higher trade frequency and lower win rates observed in 2026 when designing real-world risk management rules.
Data scope and methodology
This analysis is based entirely on historical backtested performance statistics for BSX on the 15-minute timeframe, covering the period from September 16, 2019, to September 25, 2026. All reported returns, win rates, drawdown figures, and holding times represent closed simulated trade results generated strictly within the backtest environment. Performance metrics do not reflect live order execution, broker commissions, borrow fees, or execution slippage, nor do they guarantee future returns. This document is provided strictly for educational and quantitative research purposes and does not constitute financial or investment advice.