ALLE · Allegion plc
Stock Universe · global stock marketQuantitative Analysis of ALLE Algorithmic Strategy: Low-Frequency Position Holding and Exceptional Profit Factor in Stock Trading
This quantitative research report evaluates an algorithmic trading strategy developed for ALLE stock, anchored on a 15-minute price evaluation framework over a 6.87-year testing history from September 2019 to July 2026. Demonstrating a top-tier DevioLab score of 86.88 and holding the rank 1 spot for the ALLE ticker, the strategy combines a high win rate of 82.22% with an extraordinary profit factor of 8.75. Across 45 closed trades, the system generated a cumulative simulated return of +1283.26%, characterized by long position holding times averaging 1002.77 hours (approximately 41.78 days) and an exceptionally controlled maximum drawdown of 15.17%. With trade exits occurring on average once every 54.18 days, the strategy provides an intriguing archetype of patient trend-following logic suitable for deployment in automated trading bots and algorithmic trading agents seeking high signal selectivity and reduced turnover.
Analysis as of 15.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The evaluated trading strategy operates on ALLE stock within a 15-minute bar evaluation framework, capturing price action across a historical dataset spanning from September 16, 2019, to July 28, 2026. Across this nearly seven-year timeframe (6.87 years), the strategy recorded 45 fully completed trades, earning a DevioLab score of 86.88 and securing the number one rank among strategies evaluated for this asset. Despite monitoring market fluctuations on a granular 15-minute interval, the system displays a macro-oriented holding philosophy rather than a rapid intraday focus. Total cumulative profit across the backtested period reached +1283.26%, translating to a annualized benchmark figure of 133.37% under compound modeling. The core logic exhibits high trade selectivity, averaging slightly over six executed trades per year. This low-frequency structure makes the decision rule set particularly attractive as decision logic for automated trading bots, trading robots, or trading agents, where execution friction, monitoring overhead, and market noise must be minimized.
Trading rhythm and position duration
The rhythm of this quantitative strategy is defined by long, patient holding periods punctuated by sparse trade executions. Across the 45 completed trades, the average holding duration stood at 1002.77 hours, equivalent to roughly 41.78 calendar days. The median holding time of 646.75 hours (approximately 26.95 days) reinforces that positions routinely remain active across multiple weeks. On average, the strategy completes an exit once every 54.18 days, with a median exit interval of 43.50 days. In terms of trade density, the algorithm generates approximately 6.55 trades per year, or 1.15 trades per active month. These operational metrics establish a clear structural identity: while the underlying engine ingests 15-minute price bars to identify entry and exit triggers, the statistical footprint is that of a patient position trader. Positions are given substantial runway to develop, shielding the portfolio from brief intraday noise while taking advantage of medium-term directional trends in ALLE stock.
Quality of historical results
The statistical distribution of trade returns demonstrates remarkable efficiency and asymmetry. Out of 45 finished trades, 37 resulted in gains while only 8 resulted in losses, yielding a win rate of 82.22%. The strategy achieves a profit factor of 8.75, meaning gross profits exceeded gross losses by nearly ninefold. The average return per completed trade reached +6.56%, comfortably aligned with the median trade return of +5.27%. This close alignment between mean and median values indicates that positive expectancy is broadly distributed across many trades rather than being skewed by single extreme events. The best trade produced a gain of +31.68%, whereas the single worst trade was contained to a loss of -8.23%. Furthermore, the top three winning trades collectively accounted for 25.91% of gross profit. Because fewer than three-tenths of the strategy's total gain relies on its three best positions, the quality of results reflects a resilient pattern of consistent performance rather than an unrepeatable series of extreme outliers.
Risk, drawdown and losing behavior
Risk management metrics reveal a tightly bounded downside exposure relative to the strategy's total historical yield. The maximum peak-to-trough drawdown observed across the entire 6.87-year duration was 15.17%. This drawndown profile is notably shallow when contrasted against the cumulative backtested return of +1283.26%. The strategy's worst single trade loss was limited to -8.23%, demonstrating that capital preservation rules or structural exit parameters effectively prevented catastrophic drawdowns on individual positions. Losing behavior is similarly controlled in sequence: the strategy's longest losing streak consisted of just 2 consecutive trades, whereas its longest winning streak spanned 13 consecutive trades. Combining an 82.22% win rate with short, shallow losing sequences limits extended equity curve stagnation. The primary risk exposure for this strategy stems not from rapid sequence losses, but rather from the market risk inherent in holding individual equity positions across multi-week horizons.
Behavior through time and yearly stability
Evaluating the strategy on a calendar-year basis reveals strong stability and consistent positive output across divergent market environments. In 2020, the system logged 8 trades, achieving a 100% win rate and a combined return sum of +78.75%. In 2021, it recorded 6 trades with an 83.33% win rate and a sum of +40.32%. During the broader equity market headwinds of 2022, the strategy maintained positive performance, closing 7 trades with a 57.14% win rate and a net return sum of +28.49%. Performance bounced back strongly in 2023 with 6 trades, an 83.33% win rate, and a return sum of +48.70%. In 2024, 5 completed trades produced an 80% win rate and a +16.62% sum. The calendar year 2025 yielded 9 trades with an 88.89% win rate and +47.10% sum, while the partial year 2026 recorded 4 trades with a 75% win rate and a +35.40% sum. Across all seven individual years, the strategy achieved positive total closed returns, illustrating robust adaptability over changing market volatility cycles.
Recent period since 2024-06-01 versus full history
Examining performance since June 1, 2024, provides a contemporary sample to test against the full historical record. From June 2024 through July 2026, the strategy executed 15 completed trades, representing one-third of all historical trades within roughly two years. These 15 recent trades generated a cumulative return sum of +93.92%. This reflects a notable increase in trade realization compared to the multi-year average of 6.55 trades per year, indicating that recent volatility and price structure in ALLE provided an increased number of actionable entry signals. The high sum of returns (+93.92%) over this recent period demonstrates that the strategy's statistical edge remained active and potent in recent market conditions, maintaining its characteristic profitability without suffering from performance degradation.
Strengths and limitations
The fundamental strength of this algorithmic model lies in its exceptional payoff efficiency and minimal turnover requirement. A profit factor of 8.75 coupled with an 82.22% win rate and a max drawdown of 15.17% represents a high-performing statistical footprint. Because the strategy averages only 6.55 trades per year, execution complexity and market slippage have minimal impact on theoretical returns, making the model highly suited for execution via automated trading bots or trading agents. Conversely, the strategy's chief limitation lies in its limited sample size: 45 total closed trades across 6.87 years is a small statistical baseline. Additionally, the average holding time of over 1002 hours means capital is committed to individual positions for long durations, exposing the strategy to overnight gaps, quarterly earnings releases, and broader macroeconomic shifts affecting ALLE stock.
DevioLab analytical conclusion
Securing a DevioLab score of 86.88 and ranking first among strategies analyzed for ALLE, this quantitative framework represents a compelling approach to trading stock assets. By using 15-minute price data to filter entry points while executing positions that span weeks or months, the algorithm achieves a rare balance of high win rate (82.22%) and deep payoff capability. The low concentration risk—where the top three trades account for only 25.91% of gross profits—confirms that equity curve growth is driven by repeatable statistical edge rather than localized luck. For quantitative traders and developers constructing automated trading robots or software agents, this strategy offers an instructive template: prioritizing signal quality over trade quantity can produce high profit factors while keeping drawdown bounded within manageable limits.
Data scope and methodology
The analytical conclusions presented in this article are derived exclusively from historical backtested trade logs for ALLE stock between September 16, 2019, and July 28, 2026. The dataset evaluates closed trade performance originating from 15-minute price bar analyses. All statistical metrics—including win rates, average trade percentages, holding periods, profit factors, and drawdown measurements—represent simulated historical outcomes under standardized testing conditions without assuming variable commission structures, order book slippage, or unstated leverage. Historical backtest performance provides valuable analytical insights into strategy design but does not guarantee future results in live trading environments.