ARE · Alexandria Real Estate Equities, Inc.
Stock Universe · marché actions mondialQuantitative Evaluation of ARE Trading Strategy: High Precision and Multi-Day Holding on 15-Minute Chart Structure
This empirical study analyzes an algorithmic trading strategy evaluated on the stock ticker ARE over a 6.93-year backtest horizon from September 2019 to August 2026. Designed as decision logic suitable for automated trading bots and trading agents operating on a 15-minute execution timeframe, the model demonstrates a selective, low-frequency position-taking approach. Across 70 completed trades, the strategy achieved an 84.29% win rate, a profit factor of 8.75, and a cumulative closed-trade return of +1076.02%, while restricting its historical maximum drawdown to 9.66%. The empirical footprint reveals extended multi-day position durations averaging 271.10 hours (~11.3 days) combined with long intervals between trade exits, producing an exceptionally low dependence on outlier winners.
Analyse au 18.09.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
1. Strategy profile
The evaluated algorithmic model for ARE operates on a 15-minute candle structure within the stock market asset class, serving as automated decision logic for trading robots and quantitative execution engines. Over a historical backtesting window spanning 6.93 years between September 16, 2019, and August 19, 2026, the strategy generated a total of 70 completed trade cycles. This modest trade count highlights a highly selective filtration process that bypasses short-term noise in favor of specific price configurations. The model achieved a total cumulative return of +1076.02%, translating to an annualized return metric of 153.77%. It currently holds the top DevioLab rank (Rank 1) for the ARE ticker with a quantitative score of 87.07, designated as a core independent strategy. By prioritizing selective entry conditions on lower timeframe data, the strategy combines structural precision with low transaction frequency.
2. Trading rhythm and position duration
Despite operating on a 15-minute timeframe, the strategy exhibits an extended holding profile rather than high-frequency intraday behavior. The average holding duration per position is 271.10 hours (approximately 11.3 days), with a median holding time of 195.88 hours (roughly 8.2 days). This demonstrates that while entries ARE refined using 15-minute granularity, positions ARE allowed to develop across multiple calendar weeks. Exit frequency is similarly deliberate: the model records an average of 34.20 days between trade exits and a median of 25.04 days between exits, yielding an annual turnover rate of approximately 10.11 trades per year (or 1.35 trades per active month). For automated trading bots deployed in live environments, this structural rhythm implies low operational overhead and minimal exposure to microstructural execution noise, as trade events ARE separated by substantial calm periods.
3. Quality of historical results
The structural quality of the historical performance is characterized by high win consistency and balanced payoff distribution. Out of 70 historical trades, 59 were profitable and 11 resulted in losses, establishing a win rate of 84.29%. The strategy generated an average trade return of +3.93% against a median trade return of +3.20%, indicating that historical profitability is broadly distributed across standard trade setups rather than skewed by extreme tail outcomes. The profit factor stands at 8.75, supported by a maximum winning trade of +25.52% and a worst trade of -9.66%. Furthermore, the top three winning trades collectively account for only 19.29% of gross historical profit. This relatively low winner concentration confirms that overall equity curve growth was built upon a broad sequence of moderate gains rather than dependence on a few isolated market surges.
4. Risk, drawdown and losing behavior
Risk metrics across the 6.93-year dataset indicate strict loss containment relative to cumulative gain. The strategy recorded a maximum peak-to-trough drawdown of 9.66%, which exactly matches the magnitude of its single worst closed trade (-9.66%). This alignment suggests that major historical drawdowns were predominantly driven by individual adverse trade exits rather than compounding sequences of severe losses. The strategy's longest losing streak was capped at just 2 consecutive trades, compAREd to a longest winning streak of 17 consecutive closed trades. This asymmetrical streak profile reflects rapid risk mitigation during adverse market conditions. However, because position durations regularly stretch past 10 days, open positions remain subject to multi-day market gaps and broader stock market regime shifts while trades remain active.
5. Behavior through time and yearly stability
Annual distribution of trade results demonstrates multi-year consistency across varying equity market environments. In 2020, the strategy produced 8 winning trades out of 8 (+33.23% sum). In 2021, it recorded 9 trades with an 88.89% win rate (+28.68% sum), followed by 13 trades in 2022 with a 76.92% win rate (+37.86% sum). Profitability expanded in 2023 with 9 trades and an 88.89% win rate (+41.37% sum) and continued into 2024 with 12 trades and a 91.67% win rate (+38.14% sum). The strategy experienced its sole negative calendar year in 2025, recording 5 wins and 5 losses (50.00% win rate) with a modest return sum of -5.89%. It rebounded sharply in 2026, delivering 9 wins out of 9 trades (100.00% win rate) and a cumulative sum of +101.59%. This historical progression illustrates sustained performance, with a single muted contraction year followed by strong structural recovery.
6. Recent period since 2024-06-01 versus full history
Examining performance metrics from June 1, 2024, onward offers insight into recent strategy behavior under contemporary market conditions. During this recent window, the strategy completed 24 trades, generating a sum of closed trade returns of +106.83%. Accounting for over a third of the strategy's total historical trade volume (24 out of 70 trades), this period demonstrates sustained entry frequency and strong return capture. Comparing this recent trajectory to the complete history reveals that the core statistical traits—specifically high win probability and effective risk containment—remained active through recent market cycles. The sample size of 24 completed trades since mid-2024 provides a robust statistical baseline confirming that the model's short-term execution behavior aligns closely with its multi-year profile.
7. Strengths and limitations
The primary analytical strengths of this ARE strategy include an exceptional win rate (84.29%), a high profit factor (8.75), a modest maximum drawdown (9.66%), and low profit concentration (top 3 trades contributing under 20% of gross gains). The prolonged average holding duration (~11.3 days) minimizes trade frequency, making it suitable for automated trading agents designed to execute without high-frequency churn. The primary statistical limitation lies in the small total trade sample size (70 completed trades across nearly 7 years). An average of ~10 trades per year means that statistical confidence accumulates slowly over time. Additionally, holding positions across multi-week horizons exposes capital to overnight equity gap risk and earnings announcement volatility that cannot be managed on intraday execution alone.
8. DevioLab analytical conclusion
The quantitative evaluation assigns this ARE strategy a DevioLab score of 87.07, ranking it first among available strategies for this asset. The combination of strong profit factor metrics, low maximum drawdown, and minimal winner concentration highlights a robust quantitative profile. Automated trading bots implementing this decision logic benefit from a selective filtering framework that favors trade quality over trade volume. While the historical 84.29% hit rate and low drawdown profile ARE compelling, potential operators must recognize that extended holding periods require patience during low-turnover phases, and past backtested performance does not guarantee future results across evolving stock market regimes.
9. Data scope and methodology
This analysis is based strictly on historical backtested performance data compiled for the ARE ticker on a 15-minute timeframe from September 16, 2019, through August 19, 2026. All reported metrics, including percentage returns, profit factors, holding durations, and drawdown values, reflect simulated closed trade outputs generated by the algorithmic model logic. Slippage, exchange fees, borrowing costs, and corporate action adjustments ARE not factored into these closed-trade calculations unless explicitly stated. Historical statistical outcomes serve exclusively for analytical evaluation of strategy design and do not constitute financial advice, investment recommendations, or guarantees of future trading performance.