CMI · Cummins Inc.
Stock Universe · 全球股票市场CMI Quantitative Strategy Analysis: Evaluating a Core 15-Minute Swing Trading System with High Win-Rate Expectancy
This analytical report examines the historical performance profile of a core algorithmic trading strategy evaluated on CMI stock using a 15-minute underlying chart timeframe. Spanning more than seven years of backtested execution history from September 2019 to October 2026, the strategy generated 82 completed trades with an 89.02% win rate, a profit factor of 8.75, and a maximum drawdown capped at 8.25%. With a DevioLab Score of 91.89, ranking first among tested strategies for CMI, the model relies on multi-day position holding periods to achieve robust, well-distributed trade returns. This analysis breaks down the strategy's statistical structure, execution rhythm, drawdown mechanics, yearly consistency, and recent execution performance since June 2024.
分析日期： 05.10.2026. 策略数据单独更新。阅读完整分析
1. Strategy profile
The evaluated algorithm, designated as CMI 0 +3243.38% 1TRAD-OBT7, is a primary core quantitative model designed for CMI stock. Built upon a 15-minute intraday execution timeframe, the strategy operates as an automated decision mechanism suitable for integration within systematic trading bots, trading agents, and automated trading robots. The dataset covers a historical duration of 7.05 years, starting on September 16, 2019, and running through October 5, 2026. Over this evaluation window, the strategy completed 82 closed trade cycles, accumulating a cumulative closed trade return sum of +3243.38%, which converts to an annualized backtest metric of 344.19%. With a DevioLab Score of 91.89, the strategy holds the number-one quantitative rank among tested models for the CMI ticker. The underlying statistical footprint shows 73 winning trades against just 9 losing trades, yielding an overall win rate of 89.02%. The core metrics reflect a strategy design engineered to minimize exposure frequency while maximizing entry selectivity on CMI stock.
2. Trading rhythm and position duration
Despite operating on a 15-minute candle resolution, the strategy's execution cadence is distinctly selective rather than high-frequency. Across the 7.05-year history, the algorithm executed an average of 11.62 trades per year, corresponding to approximately 1.34 trades per active month. The temporal spacing between closed trades is prolonged, recording an average of 31.49 days and a median of 22.97 days between exits. Position holding times reflect a patient, medium-term holding pattern rather than intraday scalping. The average holding duration stands at 351.68 hours, or approximately 14.65 days, while the median holding duration is 240.63 hours, or roughly 10.03 days. This divergence between execution timeframe (15 minutes) and holding length (10 to 15 days) indicates that short-term price action is utilized primarily for signal precision and entry timing, whereas trade lifecycle management allows positions to develop across multiple weeks. For automated trading agents and deployment setups, this translates into low signal turnover and extended holding intervals.
3. Quality of historical results
The pay-off characteristics of the strategy exhibit strong alignment between win rate and average trade yield. The overall profit factor reaches 8.75, backed by an average closed trade return of +4.71% and a median trade return of +4.46%. The close proximity between the mean (+4.71%) and median (+4.46%) suggests that performance is structurally balanced across the trade sample, rather than skewed by a tiny handful of extreme outliers. The largest winning trade produced a gain of +21.57%, while the three largest winning trades combined account for only 13.87% of gross strategy profit. This low profit concentration ratio confirms that historical performance stems from repeatable positive trade outcomes rather than single-event windfall gains. On the negative side, the single worst trade recorded a loss of -8.02%. The combination of an 89.02% hit rate with a favorable payoff relationship underpins the high statistical expectancy observed across the 82-trade dataset.
4. Risk, drawdown and losing behavior
Risk parameters within the backtest reveal tight control over capital degradation. The maximum peak-to-trough drawdown encountered throughout the 7.05-year testing period was limited to 8.25%. When evaluated against the cumulative closed trade performance sum of +3243.38%, the drawdown metric indicates an exceptionally low equity curve variance. Loss clusters were similarly restrained: the longest consecutive losing streak observed was 2 trades, whereas the longest winning streak reached 20 consecutive trades. Out of 82 total trade exits, only 9 ended in a net loss, with the maximum single trade loss (-8.02%) remaining smaller in magnitude than the best individual winner (+21.57%). The low loss frequency, combined with capped drawdown metrics, suggests that the strategy's signal filtering effectively avoids prolonged adverse market cycles in CMI stock.
5. Behavior through time and yearly stability
An examination of the yearly performance breakdown shows steady positive return generation across all fully covered calendar years from 2019 to 2026. In 2019, across the initial partial year, the model logged 2 trades, both profitable (100% win rate), totaling +9.83%. Performance accelerated in 2020 with 9 trades, 100% win rate, and a combined return sum of +75.33%. In 2021, 11 trades were completed with a 90.91% win rate and +27.00% sum. The strategy achieved 10 trades in 2022 (90.00% win rate, +56.48% sum) and 12 trades in 2023 (83.33% win rate, +37.93% sum). In 2024, 10 trades yielded a 100% win rate and +58.69% cumulative return. The final years of the dataset, 2025 and 2026, generated 13 trades (84.62% win rate, +52.57% sum) and 15 trades (80.00% win rate, +68.72% sum) respectively. Annual win rates remained strictly at or above 80% across every single year, demonstrating consistent historical adaptability across varying market regimes.
6. Recent period since 2024-06-01 versus full history
Filtering performance data specifically for trades closing on or after June 1, 2024, provides insight into recent strategy behavior. In this recent window, the strategy completed 34 trades, generating a cumulative closed return sum of +164.81%. These 34 trades represent approximately 41.5% of the total historical trade count (82 trades) recorded over the entire 7.05-year sample. This elevated closed trade density in the post-June 2024 period indicates an increase in completed trade exits relative to earlier years, while maintaining strong overall profitability. The recent average output per trade continues to align comfortably with the historical baseline, confirming that the algorithmic logic has maintained its functional efficacy in recent market environments.
7. Strengths and limitations
The chief analytical strength of this strategy lies in its high statistical efficiency: an 89.02% win rate combined with a profit factor of 8.75 and a maximum drawdown of just 8.25%. The broad contribution of gross profits across multiple trades (top 3 winners contributing only 13.87%) highlights the structural robustness of the model. However, notable limitations exist. With a total historical sample size of 82 completed trades over 7 years, the overall sample size is relatively compact. Additionally, the average holding period of roughly 14.65 days and an average exit gap of 31.49 days require significant patience from operators deploying automated trading bots, as trade opportunities occur infrequently and positions require prolonged incubation before resolution.
8. DevioLab analytical conclusion
Earning a DevioLab Score of 91.89 and holding the top rank for CMI stock, this strategy demonstrates a high-expectancy quantitative architecture. By successfully combining high win-rate precision with strict drawdown management, the model achieves a favorable balance between risk and historical return. Its multi-day holding structure renders it particularly suitable for systematic automated execution via trading agents or trading robots where low execution frequency and structural patience are required. While historical metrics demonstrate high consistency across all evaluated years, live implementation requires continuous tracking to ensure market dynamics remain aligned with backtested statistical boundaries.
9. Data scope and methodology
All figures, metrics, and temporal comparisons present in this analysis are derived strictly from closed simulated backtested trade data recorded between September 16, 2019, and October 5, 2026, for CMI stock on a 15-minute timeframe. Trade performance metrics describe closed position results and do not account for external execution variables such as broker commissions, order book slippage, margin interest, or live account liquidity constraints. The recent execution window (since June 1, 2024) is defined by completed trades whose exit timestamp falls on or after June 1, 2024 UTC. Historical backtested results are presented exclusively for quantitative evaluation and do not guarantee future live performance.