CME · CME Group Inc.
Stock Universe · mercado global de açõesCME Algorithmic Strategy Analysis: High Win-Rate Multi-Day Logic on CME Stock
An in-depth quantitative examination of the CME multi-day 15-minute trading strategy reveals a remarkably high win rate of 91.57% across 83 completed backtested transactions between September 2019 and October 2026. Generating a cumulative historical sum of 1297.58% with a profit factor of 7.50 and a maximum drawdown of 14.58%, the strategy relies on selective market participation and multi-week position holding rather than high-frequency noise trading. This article analyzes the interaction between position duration, profit concentration, annual consistency, and recent execution performance to evaluate the structural logic of this automated trading algorithm.
Análise em 04.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
1. Strategy profile
The quantitative model evaluated in this research report is an automated trading strategy built for CME stock operating on a 15-minute bar execution interval. Across a testing window spanning over seven years from September 16, 2019, to October 4, 2026, the strategy recorded 83 completed closed trades, accumulating a combined trade return sum of 1297.58% and an annualized rate of 173.38%. It currently holds the top rank among evaluated models for this ticker on DevioLab with a DevioLab Score of 88.40. The structural hallmark of this strategy is extreme trade selectivity combined with a robust payoff balance: 76 out of 83 trades terminated in profit, yielding a 91.57% historical win rate. This strategy is engineered as an automated decision logic suitable for execution by trading bots, trading robots, or autonomous trading agents configured for stock market environments. Rather than attempting to exploit micro-fluctuations, the algorithm filters market structure on 15-minute price action to identify high-probability multi-day position setups, producing an exceptional overall profit factor of 7.50.
2. Trading rhythm and position duration
Analyzing the operational frequency and holding periods reveals a distinct swing-to-position trading rhythm despite the strategy operating on a intraday 15-minute bar setup. The strategy completed only 83 trades across 7.05 years, translating to an average of 11.77 trades per year and approximately 1.38 trades per active month. Position duration is characterized by extended exposure: the average holding time stands at 361.74 hours (roughly 15 days), while the median holding time is 221.25 hours (approximately 9.2 days). The difference between the mean and median holding periods indicates that while many positions resolve within one to two weeks, a subset of trades remains open for substantially longer durations to capture persistent directional trends. Furthermore, the average interval between consecutive trade exits is 31.18 days, with a median spacing of 21.99 days. This low trade turnover demonstrates that the algorithm operates with substantial temporal patience, spending extended intervals flat between setups. For deployment in automated trading bots or trading agents, this low frequency minimizes trade exposure frequency, though it requires trading systems designed to manage multi-week position holding periods without premature exit interference.
3. Quality of historical results
The statistical distribution of return outcomes highlights impressive consistency and balanced return distribution across historical trades. The average trade gain across all 83 executed positions is 3.54%, closely matching the median trade gain of 3.04%. This tight alignment between average and median expectations indicates that overall performance is driven by a stable, recurring baseline gain rather than being distorted by a small number of extreme outliers. The single best trade achieved a return of 17.95%, while the top three winning trades collectively accounted for just 13.44% of total gross historical profits. This low profit concentration ratio confirms that the strategy return profile is broad-based across its sample set. The profit factor of 7.50 reflects an extraordinary asymmetry between gross winning returns and gross losing returns. Combined with a 91.57% hit rate, the strategy demonstrates that high win-rate models do not necessarily suffer from asymmetrical negative tail risk, provided that individual losing trades are contained relative to average gains.
4. Risk, drawdown and losing behavior
Risk management metrics reveal a controlled drawdown profile relative to the strategy aggregate profitability. The maximum historical drawdown experienced by the strategy was 14.58%. This peak-to-trough equity decline occurred within a trading framework that sustained a maximum losing streak of only 2 consecutive trades across the entire 7.05-year history, contrasted with a peak winning streak of 29 consecutive profitable trades. The worst individual closed trade registered a loss of -14.36%, which closely approximates the magnitude of the maximum equity drawdown. This relationship suggests that peak drawdown events were primarily driven by isolated adverse trade exits rather than compound multi-trade decay sequences. Because losing trades occurred infrequently (7 total losses out of 83 trades), the primary emotional and operational challenge for automated trading robots executing this logic is enduring multi-week holding periods during open-trade drawdowns before signal confirmation triggers a close.
5. Behavior through time and yearly stability
Examining annual performance metrics confirms strong year-over-year operational stability across varying market regimes. Following an initial setup period in 2019 with 3 trades (+3.27% sum, 66.67% win rate), the strategy generated strong positive return sums in every subsequent full calendar year. In 2020, the algorithm recorded 14 trades, all 14 of which were profitable (100% win rate), producing a sum of +82.48%. Performance remained positive in 2021 (+16.35% across 7 trades, 71.43% win rate), followed by strong performance in 2022 (+28.68% across 11 trades, 90.91% win rate), 2023 (+42.15% across 10 trades, 100% win rate), and 2024 (+40.46% across 9 trades, 100% win rate). Continuing into 2025 (+49.50% across 15 trades, 93.33% win rate) and 2026 (+31.05% across 14 trades, 85.71% win rate), the model maintained high win rates between 85% and 100% in seven out of eight calendar periods. The stability in annual trade count (ranging between 7 and 15 trades per full year) demonstrates consistent filtering criteria without period-specific over-trading.
6. Recent period since 2024-06-01 versus full history
Evaluating performance in the recent sample window starting June 1, 2024, provides critical insight into current model relevance. Out of 83 total historical trades, 35 trades concluded on or after June 1, 2024, generating a combined return sum of 113.00%. This represents a significant concentration of trade activity and return accumulation in recent history, reflecting an uptick in qualified signal generation during this multi-year phase. The average gain per trade in this recent window remains aligned with long-term baseline expectations. The presence of 35 fully closed trades in this recent evaluation window confirms that the algorithm operational logic continues to find actionable setups in current market structure, validating that historical performance is supported by active, recent sample size rather than relying solely on older historical data.
7. Strengths and limitations
The primary analytical strength of this strategy lies in its exceptional historical win rate of 91.57%, high profit factor of 7.50, and low profit concentration (13.44% in top 3 trades), demonstrating a broad-based, reliable statistical edge. Furthermore, the maximum drawdown of 14.58% is modest in comparison to the total gain sum of 1297.58%. However, key limitations must be acknowledged. The absolute trade count of 83 completed trades over 7.05 years represents a low-frequency sample size, which increases the statistical variance around expected trade intervals. Additionally, average holding durations of 361.74 hours mean that trading agents executing this logic are exposed to overnight gap risk, earnings announcements, and macroeconomic market shocks over multi-week holding periods. Finally, because the worst historical trade (-14.36%) matches the maximum drawdown scale, individual adverse trade outcomes can have a noticeable short-term impact on equity curves.
8. DevioLab analytical conclusion
The CME 0 +1297.58% 1TRAD-GWE3 strategy demonstrates a robust statistical profile characterized by high precision, strong return consistency, and controlled drawdowns. Ranking first for CME stock on DevioLab with a score of 88.40, the quantitative model proves that 15-minute price data can be successfully leveraged to construct slow, high-conviction swing strategies. The balance between mean trade profit (3.54%) and median trade profit (3.04%) confirms non-skewed profitability, while the consistency across annual periods underscores regime adaptability. For algorithmic traders deploying automated trading bots or trading robots, this strategy offers a compelling research benchmark for low-turnover, high-win-rate stock models, provided the system parameters and capital allocation rules accommodate multi-week holding times.
9. Data scope and methodology
The analysis presented in this report is derived strictly from historical backtested closed trade statistics recorded between September 16, 2019, and October 4, 2026, on 15-minute price data for CME stock. Performance calculations reflect simulated closed trade results and do not account for live order execution dynamics, variable slippage, broker commissions, borrow costs, or exchange fee structures unless explicitly specified. All metrics, including win rates, holding durations, profit factors, drawdowns, and annual breakdowns, reflect historical mathematical outputs of the backtest environment. Historical backtested performance does not guarantee future live trading results, and this research report is provided solely for educational and quantitative research purposes rather than financial advice.