BLDR · Builders FirstSource Inc.
Stock Universe · mercado global de açõesQuantitative Analysis of BLDR Algorithmic Strategy: High Hit-Rate Position Dynamics on 15-Minute Charts
This analytical report evaluates the historical performance of the top-ranked core algorithmic strategy for BLDR on a 15-minute execution interval. Spanning nearly seven years of historical backtest data from September 2019 to September 2026, the strategy achieved a DevioLab score of 82.94, ranking first for the ticker. Defined by an 81.31 percent win rate across 107 completed trades and a profit factor of 7.50, the systematic model combines high statistical accuracy with balanced profit distribution. The top three winning trades account for only 11.01 percent of total gross profit, confirming that gains are driven by systematic broad-based efficiency rather than isolated extreme outliers. Despite operating on a 15-minute timeframe, the strategy exhibits swing-trading characteristics with an average holding time of 296.44 hours (12.35 days) and an average interval between trade exits of 23.58 days. Risk analysis reveals a maximum drawdown of 30.53 percent and a worst trade of -30.40 percent, contrasted against a maximum winning streak of 19 trades and a maximum losing streak of just 2 trades. Recent performance from June 1, 2024, through September 2026 accounts for 37 trades generating a cumulative return sum of +153.33 percent, demonstrating robust modern sample validity.
Análise em 24.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The examined quantitative model represents the primary core strategy for BLDR on a 15-minute timeframe, achieving the number one rank for the symbol on DevioLab with an overall analytical score of 82.94. Across a history length of approximately 6.97 years running from September 16, 2019, to September 04, 2026, the strategy generated 107 completed trades. Out of these historical trades, 87 yielded positive returns while 20 resulted in losses, translating to a high historical win rate of 81.31 percent. Over the full history, the sum of trade percentage returns reached 26,854.48 percent, producing an annualized compound metric benchmark of 294.26 percent. Designed as quantitative decision logic, this model provides the rule-based structure required for automated trading bots, trading robots, and trading agents operating in stock markets where precise entry and exit parameters must run without manual intervention. The strategy's profile is defined by disciplined trade selection, trading roughly 15.35 times per year or 1.57 times per active month, prioritizing high-confluence technical setups over high-frequency turnover.
Trading rhythm and position duration
Although the decision logic evaluates pricing metrics on a 15-minute candlestick interval, the position holding dynamics align closely with medium-term swing trading rather than short-term scalp tactics. The average holding duration per position is 296.44 hours, equivalent to approximately 12.35 calendar days, while the median holding duration stands at 192.00 hours, or exactly 8.0 days. This divergence between the 15-minute execution interval and multi-day position duration indicates that the system utilizes intraday bar resolution to fine-tune entry execution and exit precision, while allowing profitable macro trends to develop over several trading sessions. The pace of trade completion reflects selective filter criteria: the strategy records an average of 23.58 days between completed exits, with a median spacing of 20.01 days. With an average of 1.57 completed trades per active month, automated trading agents executing this logic experience extended periods of market exposure punctuated by deliberate, widely spaced trade closures.
Quality of historical results
An evaluation of return distributions reveals high structural quality in the strategy's payoff profile, marked by a profit factor of 7.50. The average return per completed trade is +5.95 percent, which closely matches the median trade return of +6.29 percent. The proximity between the mean and median trade metrics suggests a symmetric trade distribution without heavy skewness caused by anomalous spikes. The best trade achieved a gain of +32.58 percent, whereas the worst trade incurred a loss of -30.40 percent. Crucially, profit attribution is highly decentralized across the sample set: the three largest winning trades account for just 11.01 percent of total gross profit. This low concentration metric confirms that the cumulative profitability is built upon a consistent, repeatable edge spread across 87 winning trades, rather than reliance on a handful of uncharacteristic windfall trades.
Risk, drawdown and losing behavior
The historical risk profile of the BLDR strategy demonstrates a distinct asymmetry between losing frequency and single-trade drawdown severity. The maximum peak-to-trough drawdown recorded across the nearly seven-year backtest is 30.53 percent. The largest individual trade loss was -30.40 percent, indicating that major strategy drawdowns are predominantly driven by sharp single-trade adverse movements rather than cumulative chains of consecutive losses. Indeed, the strategy's longest losing streak throughout 107 trades is only 2 trades. Conversely, the longest winning streak reached 19 consecutive profitable trades. The combination of an 81.31 percent hit rate, a maximum losing streak of 2 trades, and a 7.50 profit factor provides strong psychological and statistical resiliency, though risk controls must accommodate the potential for occasional significant adverse price moves during multi-day holding periods.
Behavior through time and yearly stability
Annual performance breakdown metrics confirm remarkable stability across varying market regimes from 2019 through 2026. The strategy delivered positive aggregate return sums in every single calendar year of its backtested history. In 2019, during its initial period, 1 trade yielded +18.73 percent. In 2020, 15 trades yielded a total sum of +123.39 percent with an 86.67 percent win rate. Performance remained potent in 2021, generating +116.98 percent across 13 trades (84.62 percent win rate). The year 2022 saw the highest annual activity with 25 trades producing +112.83 percent and an 84.00 percent win rate. In 2023, 10 trades produced +77.76 percent with an 80.00 percent win rate, followed by 2024 with 14 trades, a 92.86 percent win rate, and +75.14 percent. In 2025, trade efficiency experienced a temporary dip to a 60.00 percent win rate across 15 trades (+47.21 percent sum), before rebounding in 2026 with 14 trades, a 78.57 percent win rate, and +64.71 percent. This steady multi-year output demonstrates structural robustness.
Recent period since 2024-06-01 versus full history
Analyzing recent strategy behavior since June 1, 2024, provides critical validation regarding modern signal efficacy. Within this post-June 2024 window, the strategy executed 37 completed trades, accounting for more than one-third of the total 107 historical trade events. These 37 recent trades generated a cumulative return sum of +153.33 percent. Comparing this recent window to the full history demonstrates accelerated trade selection while maintaining solid equity expansion. The presence of 37 closed trades over a 27-month span indicates that recent market structure in BLDR provided ample opportunity for the 15-minute quantitative logic to deploy capital effectively, proving that the model's predictive edge has not decayed in recent trading environments.
Strengths and limitations
The primary strength of this BLDR quantitative model lies in its high statistical reliability, highlighted by an 81.31 percent win rate, a 7.50 profit factor, and an exceptionally long 19-trade winning streak. The low gross profit concentration of 11.01 percent in the top three trades demonstrates broad distribution of gains. Furthermore, the maximum losing streak of just 2 trades minimizes severe drawdown compounding. The principal limitation stems from single-trade loss risk, as evidenced by the worst trade of -30.40 percent, which closely matches the maximum historical drawdown of 30.53 percent. Additionally, with an average trade frequency of 15.35 trades per year and a median time between exits of 20.01 days, the strategy demands substantial patient capital and is unsuitable for traders or automated systems requiring frequent intraday turnover.
DevioLab analytical conclusion
Holding a DevioLab score of 82.94 and the top rank for BLDR, this 15-minute algorithmic strategy represents a high-conviction quantitative architecture. The model successfully bridges the gap between intraday execution resolution and multi-day trend capture, producing a balanced profile characterized by a mean trade of +5.95 percent and a median trade of +6.29 percent. For developers configuring automated trading bots, trading robots, or algorithmic trading agents, the strategy offers a tested framework that combines high hit rates with exceptional overall profit factor metrics. Managing open risk during extended multi-day holding periods remains the primary operational focus required to navigate occasional sharp individual trade drawdowns.
Data scope and methodology
The statistics analyzed in this document are derived strictly from backtested historical trade simulations executed on BLDR 15-minute pricing data between September 16, 2019, and September 04, 2026. All reported percentage gains, win rates, drawdowns, and trade counts reflect closed position results recorded in the strategy log. Recent metrics from June 1, 2024, onward comprise all positions closed on or after that date. Historical backtested performance does not guarantee future live performance and does not account for variable external factors such as live execution slippage, broker fees, order queue placement, or margin financing costs.