CRH · CRH plc
Stock Universe · mercado global de açõesQuantitative Analysis of CRH 15-Minute Algorithmic Strategy: Multi-Year Backtest Profile and Performance Dynamics
An in-depth quantitative examination of the top-ranked algorithmic trading strategy for CRH stock evaluated on a 15-minute timeframe across a 7.07-year historical testing window. Featuring an 83.5 percent win rate across 103 completed trades, a profit factor of 2.50, and a maximum historical drawdown of 13.12 percent, this strategy demonstrates remarkable consistency and exceptional profit distribution, with its top three winning trades accounting for less than ten percent of total gross profits.
Análise em 11.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The algorithmic trading model evaluated here is designed specifically for CRH stock utilizing a 15-minute price interval. Across a comprehensive 7.07-year historical testing dataset spanning from September 16, 2019, through October 11, 2026, the strategy generated 103 completed trade exits. Out of these closed positions, 86 resulted in positive return outcomes while 17 yielded negative returns, establishing an overall historical win rate of 83.5 percent. The strategy accumulated a total simulated closed return of 2047.08 percent over the evaluation period, earning a DevioLab score of 82.65 and securing the rank of number one among evaluated strategies for this ticker. Built as quantitative decision logic, this system is structured to function as the core signal engine for automated trading bots, trading robots, and quantitative trading agents seeking systematic exposure to CRH market structure without requiring discretionary manual intervention.
Trading rhythm and position duration
Despite operating on a 15-minute chart resolution, the strategy exhibits the operational characteristics of a multi-day swing trading system rather than a high-frequency trading algorithm. Over the entire 7.07-year testing window, the trade frequency averaged 14.57 completed trades per year, which translates to approximately 1.45 trades per active calendar month. Position holding times reflect a patient holding structure, with an average duration of 227.47 hours (roughly 9.48 days) and a median holding duration of 168.50 hours (approximately 7.02 days). The spacing between trade exits is similarly expanded, displaying an average of 25.07 days and a median of 21.03 days between completed positions. This distinct separation between chart granularity and holding duration indicates that while 15-minute price action is scanned continuously for trigger conditions, position management logic deliberately allows trades to mature across multi-day horizons.
Quality of historical results
The pay-off profile of the strategy demonstrates strong statistical stability and a balanced return distribution. The profit factor stands at 2.50, meaning that total gross gains exceeded total gross losses by two and a half times. The mathematical alignment between the average trade return of 3.33 percent and the median trade return of 3.67 percent suggests a remarkably symmetric return curve without heavy left-side skew. The strategy recorded a maximum individual winning trade of 14.48 percent against a worst individual losing trade of -13.12 percent. Crucially, an examination of gross profit concentration reveals that the top three largest winning trades generated only 9.73 percent of the strategy's total gross profit. This remarkably low concentration percentage confirms that performance was driven by repeated, incremental positive outcomes across many trades rather than an isolated handful of lucky outlier windfalls.
Risk, drawdown and losing behavior
Risk mitigation metrics within the dataset highlight strong downside containment over the multi-year evaluation period. The strategy experienced a maximum cumulative drawdown of 13.12 percent, an exact match in magnitude to its single worst trade loss of -13.12 percent. This alignment indicates that historical drawdown peaks were largely defined by individual stop events rather than prolonged compounding loss sequences. Furthermore, the longest losing streak observed throughout 103 trades was bounded at just 2 consecutive negative positions, whereas the longest winning streak extended to 18 consecutive successful trades. Combined with an 83.5 percent overall win rate, these streak statistics demonstrate that losing periods historically remained brief and isolated, preventing significant cumulative equity curve degradation.
Behavior through time and yearly stability
Yearly performance breakdowns reveal uninterrupted positive closed returns across all eight calendar years included in the backtest dataset. In 2019, the model initiated its history with 2 trades, both winning, producing a 6.40 percent return. During 2020, it recorded 10 trades with a 90.0 percent win rate and a cumulative gain of 50.86 percent. In 2021, 15 trades yielded an 73.33 percent win rate and 42.53 percent return. Performance in 2022 showed 12 trades, an 91.67 percent win rate, and 45.04 percent cumulative profit. The year 2023 generated 13 trades with a 76.92 percent win rate and 37.51 percent gain. In 2024, the strategy achieved a flawless 100 percent win rate across 17 trades, generating 81.26 percent return. In 2025 and 2026, activity remained steady at 17 trades per year, delivering 76.47 percent win rates in both years and returns of 48.29 percent and 31.62 percent respectively. The year-over-year consistency highlights robust statistical adaptability across changing market regimes.
Recent period since 2024-06-01 versus full history
Evaluating recent evidence starting June 1, 2024, provides vital insight into modern model stability. Within this window, the strategy completed 43 closed trades, generating a combined return sum of 129.92 percent. This represents a significant cluster of total sample activity, with over 41 percent of all historical trades occurring within this recent sub-period. The heightened trade volume during this phase coincided with strong performance continuation, confirming that the strategy's core systematic edge remained fully intact during recent market conditions rather than relying solely on older historical data points from earlier backtest years.
Strengths and limitations
The principal statistical strengths of this algorithmic model include its exceptional 83.5 percent hit rate, broad gross profit distribution with less than 10 percent concentration in top winners, highly controlled drawdown ceiling of 13.12 percent, and flawless multi-year positive return consistency. These attributes make it an appealing candidate for integration into automated trading bots designed for systematic swing strategies. Conversely, notable limitations stem from the relatively low total sample size of 103 completed trades over seven years, alongside multi-day holding requirements that expose active positions to overnight gap risk and market-wide volatility events between execution intervals.
DevioLab analytical conclusion
With a DevioLab score of 82.65 and the primary rank for CRH stock, this algorithmic trading strategy presents a robust quantitative profile characterized by steady compounding and conservative downside risk. Its long average holding period of roughly 227.47 hours allows position dynamics to bypass short-term market noise, while its high win rate and minimal profit concentration reflect a repeatable systematic edge. When deployed within disciplined trading robots or automated execution environments, the strategy offers a structured, data-backed approach to trading CRH stock.
Data scope and methodology
All metrics, figures, and statistical calculations presented in this evaluation are derived strictly from historical backtested closed trade logs spanning September 16, 2019, through October 11, 2026. The dataset evaluates completed trades on a 15-minute chart resolution for CRH stock. These results reflect simulated historical performance and do not incorporate live order book execution factors, real-time slippage, dynamic bid-ask spreads, or broker commission structures. Historical simulated results are purely analytical and provide no guarantee of future trading performance.