CARR · Carrier Global Corp.
Stock Universe · mercado bursátil globalQuantitative Analysis of the CARR Core Algorithmic Strategy: High Hit-Rate Dynamics and Structural Stability in Stock Trading
An extensive analytical evaluation of the CARR 15-minute algorithmic trading strategy reveals a highly structured quantitative framework operating within stock markets. Spanning a 6.40-year backtested history from May 2020 to September 2026, the strategy completed 109 trades with a 77.06% win rate, a 3.75 profit factor, and a maximum drawdown capped at 10.95%. Designed as a disciplined, multi-day decision logic suitable for deployment in automated trading bots, trading robots, and algorithmic agents, the strategy exhibits remarkable payoff stability. Its core strength lies in broad-based profitability rather than dependence on statistical outliers, demonstrated by a modest 14.73% profit concentration among its top three winning trades and an average trade return of 3.64% that closely tracks its median trade return of 3.48%.
Análisis al 26.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
1. Strategy profile
The strategy under evaluation, designated as the rank 1 core model for CARR within the DevioLab framework, achieves an overall DevioLab score of 84.59. Developed specifically for stock market assets using a 15-minute chart evaluation interval, the model provides systematic decision logic intended for automated trading bots, trading robots, and quantitative trading agents. The historical test period covers 6.40 years, extending from May 4, 2020, through September 26, 2026. Across this multi-year evaluation dataset, the strategy executed 109 completed trades. Its primary structural characteristics include selective trade entry, extended position holding durations, and strict risk containment, yielding a cumulative backtested return of 3288.19% across the entire sample period. By operating as a rule-based algorithm on intraday 15-minute price updates while maintaining multi-day exposure, the model filters short-term market noise while capturing broader structural price movements in CARR.
2. Trading rhythm and position duration
A critical dimension of any quantitative trading model is the relationship between execution frequency and trade holding duration. Despite sampling price data on a 15-minute bar schedule, this strategy operates at a disciplined, lower-frequency pacing. Over the 6.40-year history, it completed 109 trades, translating to an average of 17.04 trades per year and 1.63 trades per active month. Position durations reflect a multi-day holding methodology rather than high-frequency intraday turnover. The average holding duration per trade is 307.21 hours (approximately 12.8 days), while the median holding duration stands at 216.25 hours (approximately 9.0 days). Furthermore, position exits are spaced well apart, with an average of 21.60 days and a median of 16.99 days between closed trades. This statistical profile demonstrates that automated trading agents utilizing this logic remain patient during open positions, allowing underlying market trends to materialize over several weeks rather than incurring unnecessary turn-over on short-term oscillations.
3. Quality of historical results
The historical performance quality of the CARR strategy is characterized by a high hit rate and balanced payoff distribution. Out of 109 completed trades, 84 were profitable while 25 resulted in losses, establishing a historical win rate of 77.06%. The overall profit factor reaches 3.75, signifying that gross gains exceeded gross losses by nearly four to one. Across all closed trades, the average return per trade was 3.64%, which aligns closely with the median trade return of 3.48%. This tight alignment between average and median expectations indicates a symmetric return distribution free from severe skewness. Furthermore, the top three winning trades accounted for only 14.73% of the total gross profit. In quantitative research, a low top-three concentration confirms that historical profitability was driven by repeatable statistical edge rather than a handful of uncharacteristic windfall trades. Individual trade outcomes ranged from a maximum best trade of 26.89% to a worst trade loss of -8.37%.
4. Risk, drawdown and losing behavior
Risk management metrics demonstrate that the strategy maintained strict drawdown control relative to its overall capital accumulation. The maximum peak-to-trough drawdown experienced throughout the 6.40-year history was limited to 10.95%. This controlled drawdown profile coexists with a maximum losing streak of only 3 consecutive trades, compared to a maximum winning streak of 11 consecutive trades. The strategy's worst single trade recorded a loss of -8.37%, demonstrating that losing trades were contained within reasonable structural boundaries relative to the average trade gain of 3.64%. For quantitative systems operating in stock markets, the ability to restrict drawdowns to approximately 10.95% over a multi-year period while achieving a profit factor of 3.75 suggests that the strategy's exit rules effectively curb tail risk during adverse market environments.
5. Behavior through time and yearly stability
An examination of yearly statistical breakdowns from 2020 through 2026 reveals consistent positive returns across every calendar year tested. In 2020, across 14 trades, the strategy recorded a 92.86% win rate and a combined return sum of 85.84%. In 2021, 17 trades generated a 82.35% win rate and a return sum of 71.12%. Performance in 2022 remained positive despite challenging macro conditions in broader stock markets, yielding 16 trades with a 68.75% win rate and a return sum of 37.16%. The model accelerated in 2023, delivering 20 trades with a 75.00% win rate and a return sum of 56.67%. In 2024, 11 trades produced an 81.82% win rate and a 36.87% return sum. In 2025, 16 trades yielded a 68.75% win rate and a return sum of 53.14%, followed by 2026 which generated 15 trades with a 73.33% win rate and a return sum of 55.95%. This unbroken multi-year record of positive annual returns confirms that the strategy's statistical edge is structurally distributed across diverse market regimes rather than concentrated in a single fortunate year.
6. Recent period since 2024-06-01 versus full history
Isolating the recent window beginning June 1, 2024, provides valuable empirical evidence regarding the strategy's ongoing statistical validity in contemporary market conditions. Within this recent timeframe, the strategy closed 38 trades, generating a cumulative return sum of 124.96%. Comparing this recent sample against the full 109-trade dataset shows that more than one-third of all historical trade events occurred during this recent era. The average return per trade during this recent period remained robust at approximately 3.29% per closed trade. This sustained trade frequency and profit generation since mid-2024 indicate that the algorithm's decision logic continues to capture actionable market dynamics in CARR without signs of performance decay.
7. Strengths and limitations
The primary statistical strengths of the CARR algorithmic strategy include its exceptionally high win rate (77.06%), strong profit factor (3.75%), controlled maximum drawdown (10.95%), and low reliance on outlier trades (14.73% profit concentration in top 3 winners). Furthermore, its consistent profitability across seven separate calendar years provides strong evidence of structural stability. Conversely, fundamental limitations must be acknowledged. With an average execution rate of only 17.04 trades per year and average holding times exceeding 12 days, automated trading bots executing this model must maintain long-term operational patience. Low trade frequency means statistical sample growth is gradual, and market participants must accept multi-week periods without closed trade activity. Additionally, simulated backtest results do not incorporate live order-book dynamics, real-world execution slippage, or unmodeled transaction fee schedules.
8. DevioLab analytical conclusion
With a top-ranked DevioLab score of 84.59 for CARR, this core algorithmic trading model represents a robust benchmark for systematic stock market strategies. The quantitative narrative constructed by its metrics is one of methodical risk management and high probability execution. By combining a 77.06% hit rate with a maximum historical drawdown of only 10.95%, the decision logic achieves favorable risk-adjusted dynamics. For developers and quantitative analysts designing automated trading robots or deployment protocols for systematic trading agents, this strategy offers a compelling statistical blueprint that prioritizes edge consistency, multi-day trend capture, and tight downside containment.
9. Data scope and methodology
All analytical conclusions presented in this study are derived strictly from backtested historical simulation data spanning May 4, 2020, to September 26, 2026, on 15-minute price candle intervals for CARR stock. The statistics reflect closed simulated trade outputs under fixed rule parameters. Percentage returns describe individual trade changes and cumulative sums of closed historical trades; they do not represent actual live brokerage account performance or guaranteed future investment returns. This research article is published exclusively for educational and quantitative evaluation purposes and does not constitute financial, legal, or investment advice.