CMG · Chipotle Mexican Grill, Inc.
Stock Universe · mercado bursátil globalCMG Algorithmic Trading Strategy Analysis: High Payoff Consistency and Structural Patience in CMG Positional Trading Logic
This quantitative evaluation analyzes a top-ranked algorithmic trading strategy engineered for CMG equity trading on a 15-minute timeframe. Over a seven-year historical testing window from September 2019 to September 2026, the strategy achieved a cumulative simulated gain of +3010.14% across 42 completed trades. Key statistical features include an 85.71% win rate, a profit factor of 6.25, and a maximum historical drawdown capped at 16.15%. Position holding times average nearly 970 hours, revealing that despite its granular 15-minute execution interval, the strategy operates as a low-frequency positional system that captures multi-week market trends while maintaining an evenly distributed gross profit structure.
Análisis al 05.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
1. Strategy profile
The CMG core quantitative strategy represents the highest-ranked algorithmic design for Chipotle Mexican Grill stock (CMG) within the DevioLab framework, earning a score of 85.19 and the top rank for this asset. Designed to operate on a 15-minute candlestick chart, the model utilizes high-resolution price data to pinpoint trade timing while pursuing macro price shifts that unfold over extended periods. Across its full historical scope from September 16, 2019, through September 14, 2026, the strategy recorded 42 completed trades, generating a total cumulative profit sum of +3010.14%. This long-term statistical track record highlights the strategy's role as a prospective decision model for automated trading bots, trading robots, and algorithmic trading agents designed to navigate high-conviction swing and positional movements without overtrading. Rather than reacting to daily market noise, the system functions with high selectivity, seeking structural trade setups characterized by favorable reward-to-risk asymmetry.
2. Trading rhythm and position duration
An examination of the strategy's operational pacing reveals a distinct separation between its chart interval and its market exposure duration. Although trade entries and exits are evaluated on a 15-minute bar frequency, the system executes only 6 trades per year on average, corresponding to 1.17 completed trades per active month. Position durations are substantial: the average holding time spans 969.35 hours (approximately 40.4 days), while the median holding period stands at 552.13 hours (roughly 23.0 days). This disparity indicates that while half of the positions resolve within three to four weeks, a subset of winning trades extends across several months to capture large price swings. The time elapsed between trade exits reflects a similar pattern, with an average interval of 57.68 days and a median interval of 42.14 days. For quantitative traders operating automated trading bots, this ultra-low execution frequency means market exposure is concentrated in high-conviction windows, eliminating unnecessary churn and portfolio exposure during indecisive market regimes.
3. Quality of historical results
The historical performance quality of this strategy is defined by exceptional payoff consistency and minimal dependence on profit outliers. Out of 42 completed positions, 36 closed in profit and 6 closed in loss, producing an overall win rate of 85.71%. The resulting profit factor of 6.25 indicates that historical gross gains exceeded gross losses by more than six times. Crucially, the strategy's average trade return of +9.32% closely tracks its median trade return of +8.21%, demonstrating that historical profitability was driven by repeatable trade outcomes rather than skewed by anomalous gains. Furthermore, the single best historical trade achieved a return of +38.05%, while the top three winning trades collectively accounted for only 22.21% of total gross profit. This low profit concentration confirms that the quantitative edge is broadly distributed across the dataset rather than reliant on a handful of extraordinary market events.
4. Risk, drawdown and losing behavior
Risk metrics across the seven-year testing sample highlight tightly controlled adverse movement relative to total historical returns. The maximum peak-to-trough drawdown experienced by the strategy was 16.15%, which matches the exact return of its worst individual losing trade (-16.15%). This exact numerical alignment suggests that historical drawdowns were tied directly to single position liquidations rather than multi-trade compounding losses or extended system degradation. The longest historical losing streak observed throughout the entire dataset was just 2 consecutive trades, compared to a peak winning streak of 10 consecutive profitable trades. By maintaining an 85.71% win rate and short losing sequences, the strategy limits prolonged drawdowns, providing a stable equity curve profile suitable for algorithmic execution where capital preservation and psychological tolerance during drawdowns are critical.
5. Behavior through time and yearly stability
An analysis of yearly performance demonstrates consistent profitability across every calendar year from 2020 through 2026. In 2020, the strategy generated +93.82% across 5 trades with an 80% win rate. Performance remained strong in 2021, yielding +52.58% across 4 trades with a 100% win rate. During the challenging broader equity market conditions of 2022, the model remained profitable, posting a +25.51% sum across 4 trades with a 50% win rate. In 2023, it recorded +37.44% across 3 trades with a 66.67% win rate. Operational activity increased noticeably in subsequent years: 2024 saw 8 trades, all profitable (100% win rate), producing +87.01%; 2025 delivered +25.13% across 7 trades (85.71% win rate); and 2026 logged 11 trades with a 90.91% win rate, totaling +69.76%. The presence of positive total returns across all seven individual testing years demonstrates robust adaptability across varying macroeconomic and volatility environments.
6. Recent period since 2024-06-01 versus full history
Evaluating performance in the recent period beginning June 1, 2024, provides vital context regarding the strategy's modern behavior. Since this date, 24 trades have reached completion, generating a cumulative return sum of +149.55%. Notably, these 24 recent trades represent more than half of the total 42 trades logged across the entire seven-year history. This structural shift highlights a significant acceleration in signal generation during recent market cycles compared to the baseline period from 2019 to 2023, where annual trade counts ranged between 3 and 5. Despite the higher trade density, recent execution quality has remained exceptionally sharp, maintaining the high win rates observed in the annual breakdowns for 2024 through 2026. The substantial concentration of recent trade evidence confirms that the strategy's parameters continue to capture actionable market trends in current trading environments.
7. Strengths and limitations
The primary analytical strengths of this CMG algorithmic strategy lie in its high historical win rate (85.71%), robust profit factor (6.25), and broad distribution of profit gains, as evidenced by the low top-three winner contribution (22.21%). Its controlled drawdown of 16.15% aligns perfectly with its worst single trade, showing that losses were isolated rather than cascading. Conversely, important limitations must be acknowledged. The absolute trade count of 42 completed positions over seven years represents a relatively small statistical sample size, which increases the potential margin of error for future performance expectations. Additionally, the strategy's multi-week average holding duration requires significant capital patience, making it unsuitable for trading styles that require daily turnarounds. The worst historical loss of -16.15% also highlights that single trade exits can incur noticeable position-level drawdowns when trend reversals occur.
8. DevioLab analytical conclusion
With a DevioLab score of 85.19 and a rank of #1 for CMG, this quantitative strategy stands out as a high-conviction positional decision engine. Its performance profile combines strong gross profitability (+3010.14%) with minimal trade frequency, avoiding the excessive costs and slippage drag often associated with lower-timeframe execution. For deployment within automated trading agents or trading robots, the strategy offers a disciplined, trend-capturing framework backed by seven consecutive profitable calendar years. However, quantitative operators should account for the small historical sample size of 42 trades and ensure that position sizing and risk management protocols are calibrated to handle multi-week trade durations and maximum single-trade drawdowns in line with historical bounds.
9. Data scope and methodology
The statistics presented in this analytical review are derived strictly from backtested historical trade logs for CMG on a 15-minute timeframe between September 16, 2019, and September 14, 2026. All reported percentage gains, win rates, drawdowns, and holding times reflect closed historical trade events. Return percentages represent simple summed performance metrics across completed trades and do not reflect compounded portfolio returns, account-level leverage, execution slippage, or variable brokerage commission structures. Recent statistics since June 1, 2024, are calculated strictly from positions whose exit timestamps occurred on or after that date. Past backtested performance is inherently historical and provides no explicit guarantee of future trading outcomes.