BXP · BXP, Inc.
Stock Universe · mercado global de açõesQuantitative Evaluation of BXP · BXP High-Precision Medium-Term Algorithmic Strategy
An in-depth statistical analysis of the core 15-minute algorithmic trading strategy for BXP, examining its 83.54% historical win rate, 6.25 profit factor, and patient medium-term position management across nearly seven years of backtested data. The study analyzes the strategy's low trade frequency of 11.5 entries per year, controlled 15.25% maximum drawdown, and well-distributed return profile where the top three winning trades account for only 17.63% of gross profit.
Análise em 25.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The BXP core quantitative strategy represents a selective, swing-oriented algorithmic model engineered for the equity market asset BXP on a 15-minute candle evaluation window. Despite utilizing a granular 15-minute price sampling interval, the execution logic operates with significant patience, seeking high-conviction structural shifts rather than frequent micro-fluctuations. Over a multi-year historical dataset spanning from September 16, 2019 to July 29, 2026—representing approximately 6.87 years of market observation—the strategy generated 79 completed trade cycles. Out of these 79 positions, 66 closed in profit while 13 closed in a loss, yielding an exceptional historical win rate of 83.54%. Achieving a DevioLab Score of 86.91 and ranking first overall among models evaluated for the BXP ticker, this algorithm demonstrates a refined profile characterized by substantial cumulative performance. The total backtested net historical output reached 3064.90% with an annualized return metric of 167.27%. The structural architecture of this core strategy makes it particularly compelling for deployment within automated trading bots, trading robots, or automated trading agents designed to execute systematic equity swing trades without requiring continuous high-frequency order placement.
Trading rhythm and position duration
A critical structural characteristic of this quantitative model is its measured pace and extended holding duration. The historical statistics reflect a highly selective filtering process: over nearly seven full years, the algorithm engaged in only 79 completed trades. This translates to an average annual trade frequency of 11.5 trades per year, or approximately 1.46 trades per active month. Investors or automated trading agents deploying this framework must anticipate extended intervals of inactivity between trading signals. The spatial separation between trade closes reinforces this selective approach. The historical average time between trade exits stands at 31.65 days, with a median inter-exit period of 20.94 days. Position holding durations further illustrate the swing-trading nature of the system. The mean holding time per trade is 336.28 hours, equivalent to roughly 14.0 days, while the median holding duration is 169.50 hours, or approximately 7.06 days. The positive skew between median and average holding times indicates that while routine profitable exits frequently occur within one to two weeks, the algorithm allows exceptional trend-following opportunities to remain open for significantly longer periods to extract maximum market movement.
Quality of historical results
Evaluating the distribution and quality of closed trade returns reveals that the performance of the BXP algorithm is built upon strong broad-based consistency rather than isolated outlier gains. The system achieved a historical profit factor of 6.25, indicating that total gross realized profits exceeded total gross realized losses by more than six times. The average closed trade result stands at +4.87%, which aligns remarkably well with the median trade outcome of +4.56%. This tight alignment between mean and median expectations demonstrates that typical operational trades closely resemble average performance, offering predictable trade-level dynamics. Furthermore, the maximum single winning trade achieved over the historical sample was +27.31%, while the worst realized trade was -15.25%. An essential metric of structural integrity is the concentration of gross profit: the three largest winning trades collectively generated only 17.63% of the strategy's total gross profit. This low concentration ratio confirms that historical profitability was distributed naturally across dozens of successful trades over the years rather than dependent on a few fortunate market shocks, providing quantitative confirmation of systematic performance durability.
Risk, drawdown and losing behavior
Downside risk control is a central feature of the strategy's historical performance. Over the entire 6.87-year testing window, the maximum peak-to-trough historical drawdown reached 15.25%. Notably, this maximum historical equity drawdown exactly matches the magnitude of the single worst trade loss (-15.25%), indicating that peak drawdown was primarily driven by a single adverse position exit rather than a compounding chain of multiple consecutive losses. The algorithm's loss management is further evidenced by its streak dynamics. The strategy recorded a maximum consecutive losing streak of only 2 trades across its entire history. In contrast, its longest consecutive winning streak reached 16 closed trades. With 66 total winning trades out of 79, the strategy demonstrates a resilient equity curve trajectory. For systematic operators and trading robots, this combination of an 83.54% win rate, brief losing sequences, and bounded drawdown profile minimizes capital stress during unfavorable market phases.
Behavior through time and yearly stability
A year-by-year examination of backtested trade results demonstrates remarkable temporal consistency across varying equity market cycles. In 2019, during the initial partial testing window starting in September, the strategy completed 1 trade with a 100% win rate (+5.75%). In 2020, amidst heightened market volatility, the strategy executed 14 trades, winning 12 and losing 2 (85.71% win rate), accumulating a combined annual trade gain of +103.81%. Performance remained robust in 2021, generating +61.73% across 15 trades with an 86.67% win rate (13 wins, 2 losses). During the challenging macroeconomic environment of 2022, the model sustained excellent precision, taking 12 trades with 11 wins and 1 loss (91.67% win rate) to produce a net combined trade total of +33.70%. In 2023, the system delivered +44.86% across 11 trades (8 wins, 3 losses, 72.73% win rate). Continuing into 2024, it closed 11 trades with 9 wins and 2 losses (+48.94%, 81.82% win rate), followed by 2025 where 12 trades delivered 10 wins and 2 losses (+60.31%, 83.33% win rate). In the partial year of 2026, the strategy completed 3 trades with 2 wins and 1 loss (+25.70%, 66.67% win rate). The multi-year distribution highlights consistent capital growth across multiple annual cycles without any negative full-year performance.
Recent period since 2024-06-01 versus full history
Analyzing performance during the recent sample period starting June 1, 2024 provides vital insight into the current applicability of the quantitative logic. Since June 1, 2024, the strategy completed 21 trades, yielding an aggregate combined return sum of +120.61%. This recent window accounts for over 26.5% of the total 79 completed trades in the entire multi-year history. Comparing the recent operational pace with full-history baseline norms confirms that signal generation has remained fully active. The recent completion of 21 trades within roughly two calendar years reflects an execution cadence aligned with its overall historical average of 11.5 trades per year. The sustained high output of +120.61% during this recent timeframe indicates that the underlying market inefficiencies exploited by the algorithm on BXP remain active and statistically effective in contemporary market conditions.
Strengths and limitations
The strategy exhibits clear structural strengths alongside notable operational limitations. Among its primary advantages is an exceptional hit rate (83.54%) paired with a high profit factor (6.25), proving that win rate has not been artificially inflated by sacrificing risk-reward proportions. Additional strengths include a broad distribution of gains across trades (top 3 winners account for only 17.63% of gross profit), a modest historical maximum drawdown of 15.25%, and consistent yearly performance across multiple market regimes. Conversely, the primary operational limitation is the low total trade frequency. With only 79 completed positions over nearly seven years, statistical sample size considerations apply, requiring trading agents and quantitative analysts to recognize that individual trade outcomes carry significant weight. Furthermore, average holding durations exceeding 14 days require patient capital allocation, unsuited for market participants seeking daily action or ultra-short-term turnover.
DevioLab analytical conclusion
The DevioLab core quantitative model for BXP presents a compelling, highly disciplined swing-trading framework. Achieving the number-one rank for the asset with a DevioLab Score of 86.91, the algorithm succeeds by prioritizing signal selectivity over trade volume. Its ability to maintain an 83.54% win rate while keeping maximum historical drawdown to 15.25% underscores an effective balance between downside risk mitigation and upside capture. For institutional strategy designers, individual quantitative traders, and automated trading bots operating on BXP, this model offers a robust foundational logic. Its structural characteristics favor deployment within trading robots configured for patient, multi-day holding patterns where trade execution quality and low exposure frequency take priority over turnover speed.
Data scope and methodology
This analysis is derived entirely from historical backtested execution data for the BXP 15-minute quantitative strategy spanning September 16, 2019 through July 29, 2026. All reported statistics, including trade counts, win rates, holding durations, drawdowns, and annualized metrics, represent historical simulated closed trades under standard modeling parameters. The results reflect historical mathematical model output and do not represent live brokerage account execution, actual account return, or guaranteed future profitability. Slippage, commission rates, and exchange fees vary by execution platform and are not explicitly modeled within raw performance metrics.