DLR · Digital Realty Trust, Inc.
Stock Universe · 全球股票市场Quantitative Strategy Analysis: DLR · DLR 15-Minute Automated Logic Achieves 81.13% Historical Win Rate with 6.71% Maximum Drawdown
This quantitative study evaluates the top-ranked algorithmic trading strategy for DLR on the 15-minute timeframe. Over a 6.97-year evaluation window encompassing 53 completed trades, the strategy recorded an 81.13% win rate, a Profit Factor of 5.00, and a cumulative trade sum of +3136.40%. Designed as a low-frequency, position-style decision engine for automated trading bots, the logic maintains an average holding time of 735.59 hours (approximately 30.6 days) while limiting its maximum historical drawdown to 6.71%. The distribution of returns shows balanced profit contribution, with the top three winning trades generating only 21.73% of total gross profit. Performance remains stable across all calendar years from 2020 through 2026, reinforced by strong recent activity since June 1, 2024, yielding +126.35% across 15 completed trades.
分析日期： 19.10.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The algorithmic trading strategy designated for DLR operates on a 15-minute price bar interval within stock market assets. Ranked first for this asset on DevioLab with a proprietary score of 87.16, the strategy serves as decision logic suitable for automated trading bots and trading agents seeking disciplined market execution. Across a total evaluated history of 6.97 years—spanning from September 16, 2019, through September 4, 2026—the strategy registered 53 fully closed trades. Of these, 43 yielded positive returns while 10 ended in losses, establishing an overall win rate of 81.13%. The aggregate sum of closed trade returns across the full dataset reached +3136.40%, translating to an annualized performance metric of 209.44%. With a profit factor of 5.00, the strategy generated five units of gross profit for every unit of gross loss incurred. The quantitative profile highlights a highly selective systematic architecture that prioritizes trade accuracy and drawdown control over trade volume.
Trading rhythm and position duration
Despite utilizing a 15-minute intraday candle frequency to anchor its entry and exit rules, the strategy displays the temporal characteristics of a intermediate-term swing or position trading model. On average, the algorithm executes 7.60 trades per year, which equates to approximately 1.10 trades per active trading month. The average duration between trade exits is 45.90 days, with a median exit interval of 37.99 days. When positions are initiated by trading robots implementing this logic, they remain open for extended periods: the mean holding time stands at 735.59 hours (roughly 30.6 days), while the median holding time is 503.75 hours (approximately 21.0 days). This spatial divergence between a fine 15-minute sampling rate and multi-week holding times indicates that the underlying algorithmic filter requires precise intraday structural confirmations before entering, but relies on broader macroeconomic or equity trends to exit positions.
Quality of historical results
The statistical distribution of trade returns indicates high payout stability rather than dependence on statistical outliers. The average return across all 53 completed trades was +7.17%, while the median trade return reached +5.80%. The best individual trade achieved a gain of +42.81%, whereas the worst trade logged a loss of -6.71%. A crucial metric regarding return quality is the gross profit concentration: the strategy's three largest winning trades accounted for only 21.73% of total gross profit. In many automated trading strategies, a high cumulative gain is driven by one or two extreme statistical anomalies; here, the modest 21.73% concentration confirms that gains are distributed broadly across the 43 winning transactions. Combined with a 5.00 profit factor and an 81.13% win rate, the historical trade log reflects structural positive expectancy across varying market regimes.
Risk, drawdown and losing behavior
Risk management metrics for the DLR strategy demonstrate rigorous downside suppression throughout the 6.97-year backtest. The maximum peak-to-trough drawdown recorded was 6.71%, a figure that aligns almost perfectly with the single worst trade loss of -6.71%. This mathematical equivalence suggests that historical capital drawdowns were primarily composed of isolated losing trade events rather than compounding multi-trade losses. This observation is further corroborated by streak analysis: the strategy's longest historical losing streak was exactly 1 trade. Across all 53 executed positions, automated trading agents following this logic never experienced consecutive losing trades. Conversely, the longest winning streak reached 8 consecutive trades. The combination of an 81.13% win rate, a maximum drawdown of 6.71%, and a single-trade maximum loss streak establishes a low-risk profile in historical simulation.
Behavior through time and yearly stability
An examination of the yearly performance breakdown reveals unbroken annual consistency from 2020 through 2026. In 2020, the strategy completed 10 trades with an 80.00% win rate, producing a cumulative return sum of +92.03%. In 2021, trade volume remained steady at 10 trades, achieving a 90.00% win rate and a +41.54% return sum. During the 2022 market period, the strategy executed 7 trades with a 71.43% win rate and a +37.35% return sum. In 2023 and 2024, the algorithm delivered identical trade counts and win rates—8 trades and 87.50% win rates in both years—generating return sums of +69.23% and +65.72%, respectively. In 2025, performance logged 5 trades with a 60.00% win rate (+23.29% sum), followed by 2026, which recorded 5 trades with an 80.00% win rate (+50.97% sum). Every completed calendar year generated positive net trade sums, demonstrating that the strategy's edge was not limited to a single favorable market phase.
Recent period since 2024-06-01 versus full history
Performance in the recent window beginning June 1, 2024, provides a robust sample to evaluate current strategy efficacy. Between June 1, 2024, and the conclusion of the dataset on September 4, 2026, the strategy closed 15 trades, accumulating a combined trade return of +126.35%. This sample represents 28.30% of all historical trades executed over the entire 6.97-year history, indicating a modest increase in trade frequency during recent market conditions compared to the long-term baseline of 7.60 trades per year. The average return per trade in this recent window remained consistent with long-term expectations, contributing over a third of the total annualized strategy output. The ongoing execution of profitable trades without elevated drawdowns indicates that the algorithmic parameters have maintained structural alignment with market behavior since mid-2024.
Strengths and limitations
The primary statistical strengths of this strategy include its high historical hit rate (81.13%), an exceptional profit factor (5.00), a strictly controlled maximum drawdown (6.71%), and an unbroken annual profitability record. Furthermore, the low profit concentration among top winners (21.73%) ensures the edge is systematic rather than accidental. Conversely, primary analytical limitations stem from the low total trade frequency: 53 completed trades over nearly seven years represents a relatively small sample size, which can widen confidence intervals around expected future outcomes. Additionally, the average holding time of 735.59 hours requires trading bots and human operators to accommodate multi-week open position exposure, which may involve overnight capital locks and exposure to broader macroeconomic events between 15-minute decision signals.
DevioLab analytical conclusion
Taking into account all quantitative dimensions, the DLR strategy represents an exemplary model of high-selectivity trend capture. Earning a DevioLab score of 87.16 and the top ranking for asset DLR, the logic successfully converts 15-minute price data into multi-week positioning strategies characterized by an 81.13% win rate and minimal historical drawdowns. The complete absence of consecutive losing trades across seven years of backtesting highlights superior trade selection logic for deployment in automated trading systems. While operators must account for extended position durations and a low annualized trade count (7.60 trades per year), the strategy's statistical architecture offers a highly disciplined framework for risk-managed exposure to DLR.
Data scope and methodology
The metrics presented in this analysis are derived exclusively from historical backtested data for asset symbol DLR on a 15-minute time interval covering the period from September 16, 2019, to September 4, 2026. All profit percentages, holding times, drawdowns, and trade counts reflect closed simulated positions generated by the strategy logic. The statistics do not account for live order execution slippage, broker commissions, exchange fees, borrowing costs, or capital liquidity constraints. Historical simulated results do not guarantee or predict future performance. This study is intended strictly for quantitative research and educational evaluation of algorithmic trading decision engines and does not constitute financial or investment advice.