EME · EMCOR Group, Inc.
Stock Universe · global stock marketEME Algorithmic Trading Strategy Analysis: High Win Rate, Outlier-Driven Returns, and Strict Capital Preservation
This quantitative research report evaluates a high-scoring core algorithmic trading strategy designed for EME (EME) on the 15-minute timeframe. Spanning approximately 6.99 years of historical evaluation from September 2019 through September 2026, the strategy achieved a total cumulative profit of +6529.35% across 42 completed trades, yielding a DevioLab Score of 83.43 and ranking second overall for the asset. The strategy exhibits an exceptionally high win rate of 88.10% alongside an impressive profit factor of 7.50 and a remarkably low maximum drawdown of 6.85%. However, detailed statistical dissection reveals an asymmetric payoff architecture: while the average trade return stands at 19.10%, the median trade return is 4.92%, and a staggering 72.49% of total gross profit is concentrated within the three largest winning trades, led by an extraordinary peak trade of +556.69%. Despite operating on a 15-minute sampling interval, the system functions as a low-frequency, multi-week swing or position holding model with an average position duration of 1121.28 hours (approximately 46.7 days) and an average frequency of roughly 6.01 trades per year. This analysis examines the operational dynamics, risk parameters, profit skewness, and recent multi-year execution characteristics suitable for implEMEntation in automated trading bots and trading agents.
Analysis as of 24.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The strategy designated as EME 0 +6529.35% 1TRAD-JWC2 represents a core quantitative model engineered for EME stock equity using a 15-minute candle sampling framework. Over a evaluated test horizon of nearly seven years (6.99 years, running from September 16, 2019 to September 11, 2026), the system executed a total of 42 completed trades. Out of these 42 closed positions, 37 resulted in profitable exits while only 5 incurred losses, establishing an exceptionally strong historical win rate of 88.10%. The model achieved a total cumulative gain of +6529.35%, which translates to a highly elevated annualized figure under standard compound calculations, earning a DevioLab Score of 83.43 and securing the number two rank among all strategies evaluated for the EME ticker. From a operational engineering perspective, the strategy is structured as logic suitable for execution by automated trading bots, trading robots, or autonomous trading agents operating within professional quantitative execution suites. Although the underlying data feeds process on a granular 15-minute bar frequency, the system does not engage in intraday noise trading or high-frequency turnover. Instead, it utilizes intraday price action to refine entry and exit timing for substantial multi-day and multi-week market exposure. Its classification as a core strategy reflects its statistical robustness, favorable risk-adjusted payoff, and structural consistency across multi-year market conditions.
Trading rhythm and position duration
A critical finding in the quantitative evaluation of this strategy is the stark contrast between its underlying 15-minute chart resolution and its macro holding behavior. Despite sampling market price action every 15 minutes, the strategy operates with extrEME selectivity, averaging just 6.01 completed trades per year, or approximately 1.75 trades per active month. Over the entire 6.99-year historical dataset, only 42 trade cycles were completed, demonstrating a high degree of signal filtering that ignores routine market fluctuation in favor of high-conviction structural opportunities. The position duration statistics further emphasize this patient approach. The mean holding time across all completed trades is 1121.28 hours, equivalent to approximately 46.7 calendar days. Meanwhile, the median holding duration is 206.50 hours (roughly 8.6 days). The substantial gap between the median and average holding times indicates that while many trades achieve their exit criteria within one to two weeks, a subset of highly successful positions is allowed to run for multiple months to capture macro trend movEMEnts. Inter-trade pacing shows an average gap between exits of 22.58 days and a median gap of 16.74 days. Thus, an automated trading robot deploying this logic operates with low execution frequency, minimizing transaction exposure while holding trades through multi-week market cycles.
Quality of historical results
The statistical profile of the strategy's return distribution displays exceptional overall efficiency, balanced by a heavy right-tail profit asymmetry. The system achieved a profit factor of 7.50, meaning that gross generated profits exceeded total gross losses by seven and a half times. The absolute win rate of 88.10% (37 winning trades against 5 losing trades) indicates a highly reliable entry filter. Across all 42 trades, the average trade return reached 19.10%, while the worst single trade loss was limited to -6.85%. However, a deeper inspection of the trade distribution reveals significant profit concentration. The median trade return is 4.92%, which is substantially lower than the 19.10% mean. This divergence is driven by extraordinary outlier gains, most notably a best single trade return of +556.69%. Furthermore, the three largest winning trades combined accounted for 72.49% of the strategy's cumulative gross profit. This structural feature indicates that while the strategy frequently secures moderate gains near its 4.92% median, its astronomical multi-year compounding relies heavily on capturing rare, massive trend expansions. Automated trading agents executing this framework must therefore remain continuously deployed to ensure they do not miss the infrequent outlier trades that generate the vast majority of net performance.
Risk, drawdown and losing behavior
Risk containment within the strategy is exceptionally disciplined. The historical maximum drawdown observed across the entire 6.99-year backtest was restricted to just 6.85%. Remarkably, this maximum portfolio equity drawdown exactly mirrors the strategy's worst single trade loss of -6.85%. This mathematical identity indicates that equity declines were almost entirely driven by single trade stop exits rather than compound sequences of consecutive losing trades or systemic strategy decay. This interpretation is reinforced by the strategy's streak metrics. The maximum consecutive losing streak across all 42 trades was exactly 1 trade. At no point in the nearly seven-year backtest history did the strategy experience back-to-back losing trades. Conversely, the strategy achieved a peak winning streak of 11 consecutive profitable closed positions. Combined with an 88.10% win rate and a profit factor of 7.50, the statistical evidence reveals a risk managEMEnt design that cuts failing positions cleanly at approximately 6.85% while allowing winning trades to develop smoothly without inducing persistent account equity drawdowns.
Behavior through time and yearly stability
Evaluating the annual performance breakdown provides essential context regarding how the strategy generated its return profile over time. In 2024, the system registered 8 completed trades, all 8 of which were profitable, achieving a 100% win rate and a total annual return sum of +633.96%. This explosive year reflected the realization of major profit-taking events on multi-month trend holdings, including the top-performing outlier trades. In 2025, trade frequency increased to 20 completed trades, producing 17 wins and 3 losses for an 85.00% win rate and a cumulative return sum of +96.76%. In 2026, through September 11, the strategy logged 14 trades with 12 wins and 2 losses, recording an 85.71% win rate and a return sum of +71.46%. Comparing these periods shows remarkable consistency in hit rate (ranging between 85.00% and 100% annually) alongside varying total return magnitudes. The variation in cumulative annual return is directly tied to whether a given year encompassed the final exit of a primary multi-hundred-percent trend position or consisted primarily of standard median-style swing gains.
Recent period since 2024-06-01 versus full history
A detailed examination of the recent execution window beginning June 1, 2024 provides crucial insight into the strategy's modern relevance and structural stability. Out of the 42 total historical trades recorded since September 2019, 40 completed trades closed on or after June 1, 2024. Over this recent window, the strategy generated a sum of trade returns equal to +223.74%. Because 40 of the 42 total lifecycle trades were finalized within this recent multi-year window, the modern dataset represents virtually the entire empirical sample of the strategy's exit activity. This confirms that the statistical validity of the strategy is not reliant on stale historical market regimes from 2019 or 2020. Instead, the strategy's high win rate, tight drawdown control, and trend capture capabilities have been actively demonstrated in recent EME price action, proving that the underlying logic remains aligned with current market structure.
Strengths and limitations
The strategy possesses clear statistical strengths, primary among which is its elite risk-to-reward architecture. A 6.85% maximum drawdown paired with a 7.50 profit factor and an 88.10% win rate provides an exceptionally robust defensive profile. The complete absence of consecutive losing streaks exceeding 1 trade minimizes equity curve stagnation, while the high DevioLab Score of 83.43 reflects top-tier ranking performance for EME stock trading environments. Conversely, the strategy exhibits specific limitations that quantitative analysts and algorithmic traders must consider. First, the strategy relies heavily on profit concentration, with the top 3 winning trades generating 72.49% of all gross profits and a single best trade returning +556.69%. If market conditions shift such that tail-end trend explosions become rarer, the real-world performance will gravitate closer to the 4.92% median trade return. Second, the long holding periods—averaging 1121.28 hours—require substantial patience and capital commitment, making the system unsuitable for operators seeking frequent intraday turnover or high trade volume.
DevioLab analytical conclusion
The quantitative evaluation of EME 0 +6529.35% 1TRAD-JWC2 confirms its status as an exceptionally strong core algorithmic model. Achieving the second highest rank for EME with a DevioLab Score of 83.43, the strategy succeeds by combining extrEME trade selection discipline with broad profit asymmetry. Its core design principle relies on taking infrequent, highly filtered positions on 15-minute price structures and holding them through full trend maturation while maintaining strict stop-loss protection near 6.85%. For quantitative traders deploying automated trading bots or trading agents, this strategy offers a compelling capital preservation blueprint with explosive upside potential. Success in live deployment depends on strict adherence to the execution logic without manual intervention, as missing even a single signal could mean missing one of the rare outlier winners that account for the bulk of long-term compound growth. The strategy represents a high-conviction, low-drawdown swing trading system well-suited for automated portfolio allocation.
Data scope and methodology
The performance metrics discussed in this analytical report are derived from historical backtested trade logs for asset EME over the period spanning September 16, 2019 to September 11, 2026. All trade statistics, including win rates, profit factors, holding durations, drawdowns, and return sums, represent completed simulated closed positions generated on a 15-minute chart resolution. The recent monitoring period incorporates all trades closed on or after June 1, 2024 UTC. These findings reflect rigorous historical simulation and mathematical modeling for educational and analytical research purposes. They do not represent live brokerage account statEMEnts, guaranteed trading returns, or direct financial advice. Real-world implEMEntation through automated trading software may experience variability due to market slippage, broker execution latency, liquidity constraints, commissions, and unexpected corporate events.