BKR · Baker Hughes Company
Stock Universe · globaler AktienmarktQuantitative Strategy Analysis: BKR Algorithmic Trading Strategy Achieves 89.61% Win Rate and 5.0 Profit Factor Over Multi-Year Backtest
This empirical quantitative research report evaluates the primary algorithmic trading strategy for BKR on the 15-minute timeframe. Across a 6.63-year evaluation history from February 2020 to September 2026, the strategy recorded 77 completed trades, achieving an 89.61% win rate, a Profit Factor of 5.0, and a maximum drawdown capped at 15.28%. With an overall simulated cumulative return of 29,723.33% and an annualized equivalent of 254.10%, the strategy demonstrates extreme trade selectivity, keeping position holding times long with an average duration of 413.27 hours. The distribution of gross profit shows healthy broad-based contribution, with the top three winning trades representing only 14.83% of total gross gains. Recent performance since June 1, 2024, accounts for 27 closed trades generating a cumulative sum of 150.31%, reinforcing the model's structural stability across distinct market regimes.
Analyse vom 23.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The evaluated trading model for BKR is a core algorithmic strategy engineered for equity market dynamics using 15-minute candle intervals. Operating under the DevioLab framework, it holds the top rank (Rank 1) for the ticker with an overall strategy score of 85.80 out of 100. Rather than executing frequent intraday transactions, the system utilizes the 15-minute data granularity primarily to refine entry and exit timing while maintaining a low overall transaction frequency. Over the 6.63-year evaluation window running from February 7, 2020, to September 23, 2026, the strategy generated only 77 completed trade cycles, which translates to an average of 11.62 trades per year or roughly 1.45 trades per active month. Designed as automated decision logic suitable for execution via trading bots, trading robots, or automated trading agents, the strategy emphasizes patience and high-conviction signal filters over volume-based exposure.
Trading rhythm and position duration
Although the decision engine scans market structure on a 15-minute chart, its operational rhythm resembles medium-term swing positioning rather than short-term trading. The average holding duration for a completed trade is 413.27 hours (approximately 17.2 calendar days), while the median holding duration stands at 306.00 hours (12.75 calendar days). This divergence between mean and median indicates that while most trades conclude within roughly two weeks, select positions are held significantly longer to ride extended trends. Furthermore, the average interval between consecutive trade exits is 31.53 days, with a median spacing of 26.53 days. These cadence metrics highlight that the strategy spends substantial time out of the market or holding single positions patiently, avoiding churn and reducing signal exposure during indecisive market phases.
Quality of historical results
The performance metrics reveal exceptional trade efficiency and gain distribution. Out of 77 completed trades, 69 were closed at a profit, resulting in an 89.61% win rate and a Profit Factor of 5.0. The average return per trade across the entire dataset reached +8.23%, closely mirrored by a median trade return of +6.80%. The best individual trade achieved a gain of +37.81%, while the worst single position closed with a loss of -15.28%. Notably, the top three winning trades generated just 14.83% of total gross profit, confirming that historical outperformance was not driven by a few lucky outliers. Instead, profitability was distributed broadly across dozens of successful trades, demonstrating consistent edge execution across multi-year asset cycles.
Risk, drawdown and losing behavior
Risk containment is a central characteristic of this algorithmic model. The maximum drawdown recorded over the full 6.63-year backtest was 15.28%, perfectly matching the magnitude of its single worst trade loss (-15.28%). Throughout 77 closed trades, the strategy experienced only 8 total losses against 69 wins, yielding a maximum consecutive losing streak of exactly 1 trade. In contrast, its longest consecutive winning streak reached 26 trades. This structural asymmetry—where losing trades occur in isolation and are promptly followed by recovery trades—keeps equity curves stable and mitigates the risk of compounding drawdowns during adverse volatility spikes.
Behavior through time and yearly stability
Annual performance metrics illustrate high strategic consistency across varying broader market environments. In 2020, the strategy generated a cumulative return sum of 198.39% across 13 trades with a 92.31% win rate. In 2021, it recorded a perfect 100.00% win rate over 9 trades, yielding 89.50%. The highest activity level occurred in 2022 with 16 trades, delivering 129.72% at an 87.50% hit rate. Performance remained positive through subsequent years: 2023 produced 58.11% across 11 trades (81.82% win rate), 2024 logged 39.97% over 6 trades (83.33% win rate), 2025 delivered 49.84% across 8 trades with a 100.00% win rate, and 2026 generated 68.07% over 14 trades (85.71% win rate). The absence of a single losing calendar year highlights the strategy's adaptive strength.
Recent period since 2024-06-01 versus full history
An examination of the recent market regime from June 1, 2024, to September 23, 2026, confirms that the strategy has maintained its statistical edge. During this 2.3-year window, 27 trades were closed, producing a cumulative return sum of 150.31%. This represents a higher trade frequency compared to the earlier baseline, accounting for over one-third of all historical trades within approximately 35% of the total backtest duration. The sustained accumulation of positive closed-trade gains during recent market conditions indicates that the model's underlying rules remain aligned with ongoing price behavior rather than relying on obsolete historical patterns.
Strengths and limitations
The primary strengths of the strategy include an exceptional win rate (89.61%), a high Profit Factor (5.0), modest drawdown (15.28%), and low gross profit concentration in top trades (14.83%). The strategy's long holding times and low transaction frequency help minimize trade clutter. Conversely, the principal limitation stems from the modest sample size of 77 trades over 6.63 years. Because positions remain open for an average of 413.27 hours, deployed trading bots and automated agents are exposed to multi-week market gap risk and overnight macroeconomic events, which cannot be adjusted dynamically during market closures.
DevioLab analytical conclusion
With a DevioLab score of 85.80, this quantitative model stands out as the premier strategy for BKR within the evaluated database. Its combination of high win rate, low drawdown, and uniform profit distribution makes it a compelling candidate for deployment in algorithmic trading systems, trading robots, and automated portfolio agents requiring controlled risk parameters. However, operators must account for long holding periods and low trade frequency, which require strict patience and capital allocation discipline during inactive periods.
Data scope and methodology
This analysis is based strictly on historical backtested strategy data for BKR on the 15-minute timeframe from February 7, 2020, to September 23, 2026. All statistics refer to completed, closed trade cycles executed under simulated historical conditions. The reported metrics do not include live execution costs, slippage, or commission structures, nor do they guarantee future performance. This study is provided solely for quantitative research and software logic evaluation purposes and does not constitute financial advice.