BDX · Becton, Dickinson and Company
Stock Universe · mercado bursátil globalHigh-Precision Swing Dynamics: Quantitative Analysis of the BDX 15-Minute Strategy
An in-depth quantitative analysis of the core BDX trading strategy on the 15-minute timeframe reveals an exceptional historical profile characterized by a 93.48 percent win rate, a Profit Factor of 7.50, and an accumulated net trade sum of +1003.08 percent across 46 completed trades over seven years. Operating with low trade frequency and multi-week position holding times, the strategy exhibits remarkable drawdown control, capping historical equity pullbacks at a tight 6.78 percent—identical to its single worst trade. With every year from 2019 to 2026 yielding net positive results and top three winners accounting for less than 27 percent of gross profits, this top-ranked algorithm demonstrates a balanced distribution of gains suitable for patient automated execution.
Análisis al 22.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The quantitative model designated BDX 0 +1003.08% 1TRAD-RFF1 represents the flagship algorithmic strategy for BDX stock asset, holding the top rank on DevioLab with an overall analytical score of 89.07. Designed for implementation within automated trading bots, trading agents, or trading robots, the strategy utilizes a 15-minute execution timeframe to evaluate price action while maintaining a macro swing profile. Across a seven-year evaluation window spanning from September 16, 2019, to September 14, 2026, the strategy generated 46 completed trades, accumulating a total closed trade return of +1003.08 percent. The structural design emphasizes extreme signal selection, trading infrequently but achieving an extraordinary hit rate of 93.48 percent, with 43 winning trades against merely 3 losses. This high-conviction architecture separates the strategy from typical intraday algorithms, using lower timeframe precision to build multi-week positions.
Trading rhythm and position duration
Despite operating on a 15-minute chart interval, the strategy exhibits a patient, low-turnover trading rhythm that completely rejects high-frequency noise. Across the full historical sample, the system executes an average of 6.57 trades per year, or approximately 1.15 trades per active month. The temporal spacing between completed trade exits averages 56.21 days, with a median interval of 46.99 days, indicating that trade completions occur roughly once every month and a half to two months. Position duration reflects a multi-week swing or position trading strategy rather than intraday scalping. The average holding time stands at 589.37 hours, or approximately 24.5 days, while the median holding duration is 360.25 hours, or roughly 15 days. This substantial difference between average and median holding times indicates that while most trades conclude within two to three weeks, select positions are held significantly longer to capture extended directional moves. For automated trading agents, this low trade frequency eliminates excessive transaction cycles while requiring prolonged patience between entry and exit triggers.
Quality of historical results
The historical quality of gains for this strategy is defined by high payoff reliability and balanced profit contribution. With a Profit Factor of 7.50, the algorithm generated seven and a half times more gross gain than gross loss across its execution history. The average completed trade yield is +5.76 percent, while the median trade yield stands at +3.99 percent. The presence of a positive variance between average and median trade performance is supported by a peak gain of +28.36 percent on its best trade. Crucially, the top three winning trades generated 26.93 percent of total gross profit. This modest concentration ratio demonstrates that historical profitability was not driven by a few isolated statistical anomalies, but was broadly distributed across the 43 winning transactions. Combined with a 21-trade winning streak, the strategy shows consistent trade-level expectancy where individual wins steadily compound overall historical returns.
Risk, drawdown and losing behavior
Risk metrics for the BDX strategy demonstrate exceptional capital containment throughout the test dataset. The maximum historical drawdown was restricted to 6.78 percent, a figure that precisely mirrors the strategy's worst individual trade loss of -6.78 percent. This statistical alignment reveals that equity pullbacks were isolated to single losing trade events rather than compounding strings of consecutive losses. Indeed, the longest losing streak observed over seven years was exactly 1 trade, meaning the algorithm never suffered back-to-back losses in closed trade history. By limiting loss frequency to just 3 instances out of 46 trades, the model avoids deep underwater equity periods. For automated trading robots operating under risk constraints, this drawdown profile reflects a system where downside risk per trade is strictly controlled and equity recovery occurs almost immediately following a rare loss.
Behavior through time and yearly stability
An examination of yearly statistical breakdowns reveals remarkable operational consistency across varying market environments from 2019 to 2026. The strategy achieved a 100 percent win rate in five separate calendar years: 2019 (2 trades, +11.16 percent), 2021 (4 trades, +18.32 percent), 2023 (4 trades, +21.60 percent), 2025 (7 trades, +38.39 percent), and 2026 (7 trades, +57.82 percent). In the remaining years, win rates remained high at 87.5 percent in 2020 (8 trades, 1 loss, +66.03 percent) and 2024 (8 trades, 1 loss, +22.85 percent), and 83.33 percent in 2022 (6 trades, 1 loss, +28.57 percent). The algorithm did not register a single net-negative calendar year across the seven-year timeline. Trade frequency remained remarkably stable, fluctuating between 2 and 8 trades per year, showing that the strategy's edge does not depend on specific high-volatility regimes, but consistently extracts yield across multi-year cycles.
Recent period since 2024-06-01 versus full history
Evaluating the evidence window starting June 1, 2024, shows a clear acceleration in both completed trade activity and return accumulation. Out of the 46 total historical trades, 19 completed trades occurred with sell timestamps on or after June 1, 2024. These recent transactions delivered a cumulative sum of +118.31 percent, representing over 11 percent of the full historical performance generated in a period representing roughly one-third of the total history length. This cluster of 19 trades confirms that the strategy's filtering logic has remained highly active and relevant in recent market conditions. Comparing the recent sample to the broader 2019-2026 dataset demonstrates that the underlying signal generation has not decayed or stagnated, maintaining strong productivity without sacrificing historical win rate patterns.
Strengths and limitations
The primary analytical strengths of this BDX strategy lie in its high accuracy rate of 93.48 percent, an outstanding Profit Factor of 7.50, and a tightly contained maximum drawdown of 6.78 percent. The low profit concentration in the top three trades (26.93 percent) confirms broad-based trade quality, while unbroken annual profitability underscores historical structural resilience. However, material limitations must be noted. The primary constraint is the small overall sample size of 46 completed trades across seven years. Because the strategy trades infrequently—averaging only 6.57 trades per year—statistical estimation carries wider confidence intervals than strategies with hundreds of trades. Furthermore, the average holding time of nearly 25 days requires long holding horizons, which may expose positions to overnight market gaps or macro events that cannot be mitigated by intraday execution rules.
DevioLab analytical conclusion
The BDX 0 +1003.08% 1TRAD-RFF1 strategy achieves its top rank on DevioLab through a rare combination of extreme signal selectivity, minimal drawdown, and high gain consistency. By utilizing 15-minute chart resolution to execute multi-week positions, the model successfully strips away intraday market churn while retaining precise entry execution. Its structural alignment—where maximum historical drawdown matches its worst single loss—makes it a compelling logic template for deployment in automated trading bots and trading agents seeking disciplined swing exposure. While prospective users must respect the low sample size inherent in a 46-trade historical dataset, the strategy's unbroken multi-year track record offers strong quantitative backing for systematic asset allocation.
Data scope and methodology
This analysis is derived entirely from historical backtested trade logs for asset symbol BDX over the timeframe beginning September 16, 2019, and ending September 14, 2026. All reported statistics, including the 46 completed closed trades, 93.48 percent win rate, 7.50 profit factor, and recent 19-trade sample since June 1, 2024, represent simulated closed-position performance calculated without leverage. Historical trade yields do not incorporate undisclosed real-world trading costs, execution slippage, broker fees, or corporate actions. Past simulated results are analyzed strictly for quantitative research purposes and do not guarantee future live performance or constitute direct financial advice.