EG · Everest Group, Ltd.
Stock Universe · global stock marketHigh-Precision Multi-Day Swing Logic: Statistical Analysis of the EG · EG Algorithmic Trading StratEGy
A comprehensive quantitative examination of the top-ranked core algorithmic trading stratEGy for EG stock, evaluated across a seven-year backtested historical dataset from September 2019 to September 2026. Designed as an automated decision engine for trading bots, robots, and quantitative trading agents operating on 15-minute price data, this stratEGy exhibits remarkable structural stability and disciplined selective exposure. Over 77 completed historical trades, the model recorded an 85.71% win rate, a 3.75 profit factor, and a cumulative closed-trade gain of +3,096.72%, all while maintaining a remarkably compressed maximum drawdown of 6.33%. This analysis decomposes its statistical signature across duration dynamics, profit dispersion, risk containment, and multi-year temporal consistency.
Analysis as of 24.10.2026. Strategy data is updated separately.Read full analysis
StratEGy profile
The algorithmic stratEGy designated for EG stock stands as a flagship core model within the DevioLab framework, earning a top rank of 1 for the ticker alongside a composite DevioLab score of 87.59. Developed to function as the core decision logic for automated trading bots, algorithmic execution engines, and multi-asset trading agents, the stratEGy operates by processing 15-minute price candles. Across a extensive seven-year historical evaluation window spanning from September 16, 2019, through September 14, 2026, the algorithm completed 77 closed trades. Over this timeframe, the model achieved a cumulative backtested return of +3,096.72%, translating to an annualized benchmark equivalent of 248.60%. The profile reflects a low-frequency, high-selectivity approach where entry signals are generated sparsely, ensuring that automated trading systems executing this stratEGy remain unburdened by high transaction churn while seeking high-conviction structural shifts in EG stock.
Trading rhythm and position duration
Despite utilizing a 15-minute candle interval for price evaluation, the stratEGy's operational rhythm is fundamentally distinct from micro-scalping or intraday noise trading. The historical statistics reveal an average holding duration of 392.37 hours (approximately 16.3 days) and a median holding duration of 262.00 hours (roughly 10.9 days). Positions are routinely held across multiple trading sessions, allowing macro directional trends to unfold rather than reacting to short-term market fluctuations. Exit frequency reflects this patient posture: the average time between trade exits is 31.67 days, with a median of 27.01 days. Across the entire dataset, the stratEGy averages 11.01 completed trades per year, or approximately 1.45 trades per active month. For automated trading robots and automated software agents, this cadence indicates that capital remains uncommitted for extended intervals, with long holding periods interspersed between infrequent, highly filtered execution events.
Quality of historical results
The payoff profile of this stratEGy exhibits exceptional mathematical quality, defined by a high hit rate and a balanced distribution of gains. Out of 77 total lifetime trades, 66 were closed in profit against just 11 losing positions, yielding an overall win rate of 85.71%. The profit factor stands at an impressive 3.75, signifying that gross realized profits exceeded gross realized losses by nearly four to one. The mean closed trade return of +4.86% closely aligns with the median trade return of +4.24%, proving that individual trade outcomes are tightly clustered around a positive baseline rather than skewed by extreme statistical tail events. The single best trade yielded +25.49%, while the concentration of total gross profit within the top three winning trades is remarkably low at 13.90%. This low concentration confirms that the stratEGy's historical return curve is built upon a broad, repeatable foundation of positive trades rather than dependence on a few lucky windfalls.
Risk, drawdown and losing behavior
Risk metrics within the historical dataset highlight a tightly controlled equity curve. The stratEGy experienced a maximum historical drawdown of only 6.33% across the full seven-year sample, a exceptionally low peak-to-trough decline given the multi-thousand-percent cumulative yield. Downside risk on individual positions was similarly constrained: the worst closed trade in the entire 77-trade record resulted in a loss of -4.94%. Furthermore, the model's loss propagation remained tightly bounded, rEGistering a maximum consecutive losing streak of only 2 trades. In contrast, the stratEGy achieved a peak winning streak of 19 consecutive profitable trades. For quantitative trading agents operating under automated risk rules, this combination of an 85.71% hit rate, brief loss streaks, and an ultra-shallow maximum drawdown suggests that historical equity contractions were brief and rapidly absorbed by subsequent winning cycles.
Behavior through time and yearly stability
An examination of the yearly breakdown demonstrates consistent positive performance across every individual calendar year from 2020 through 2026. In 2020, the stratEGy generated +60.16% across 10 trades with a 90.00% win rate (9 wins, 1 loss). In 2021, it recorded +50.87% across 12 trades with a 91.67% win rate (11 wins, 1 loss). Performance remained robust in 2022, accumulating +68.05% across 10 trades at a 90.00% win rate (9 wins, 1 loss). In 2023, trade frequency reached its peak at 14 trades, delivering +55.23% despite a temporary dip in win rate to 71.43% (10 wins, 4 losses). The year 2024 saw 12 completed trades yielding +34.55% with a 66.67% win rate (8 wins, 4 losses). The stratEGy returned to absolute perfection in 2025 and 2026, delivering +51.04% (9 trades, 100.00% win rate) and +54.37% (10 trades, 100.00% win rate) respectively. The annual trade volume stayed remarkably stable within a range of 9 to 14 trades per year, reflecting a structural logic that remains effective across diverse market rEGimes without requiring recalibration.
Recent period since 2024-06-01 versus full history
Evaluating the recent performance window from June 1, 2024, through September 14, 2026, provides vital insight into the stratEGy's contemporary behavior. During this recent window, the stratEGy completed 28 trades, accumulating a combined return of +131.73%. This represents a substantial portion of the total lifetime trade count (28 of 77 trades), indicating an increase in closed-trade activity during the latter portion of the sample. The average yield per trade during this recent period remained well aligned with full-history standards, confirming that the underlying edge has maintained its statistical intEGrity into current market conditions. Rather than suffering from alpha decay, the decision logic powering these trading bots has continued to capture profitable multi-day swings in EG stock with robust precision.
Strengths and limitations
The stratEGy's primary strengths include an extraordinary 85.71% historical win rate, a 3.75 profit factor, an exceptionally shallow maximum drawdown of 6.33%, and a low top-three winner profit concentration of 13.90%. Its multi-year performance is exceptionally consistent, rEGistering positive annual returns across all seven backtested years. However, clear statistical limitations exist. With only 77 total completed trades over seven years, the absolute sample size is relatively compact, meaning individual trade outcomes carry greater statistical weight than in high-frequency datasets. Additionally, because the average position duration exceeds 16 days (392.37 hours), automated trading agents executing this logic must endure prolonged multi-week exposure, subjecting capital to overnight gaps and market-wide macroeconomic shifts inherent in stock trading.
DevioLab analytical conclusion
With a top DevioLab rank of 1 for EG and a composite score of 87.59, this algorithmic stratEGy represents an elite model for quantitative traders seeking disciplined multi-day swing logic. Its combination of low trade frequency, strong trade payoff symmetry (+4.86% mean vs +4.24% median), and constrained peak drawdown (6.33%) makes it an attractive structural model for automated trading bots and trading robots. The empirical evidence demonstrates that the stratEGy's gains are widely distributed across dozens of trade events rather than concentrated in isolated market spikes. Quantitative developers and automated traders should nevertheless evaluate these historical backtested results alongside asset liquidity, position sizing rules, and macro portfolio constraints before deploying automated trading agents based on this decision framework.
Data scope and methodology
This analysis is derived exclusively from backtested simulated historical trade records for EG stock on a 15-minute candle timeframe from September 16, 2019, to September 14, 2026. All statistics, including the 77 completed trades, +3,096.72% overall historical profit, 85.71% win rate, and 6.33% maximum drawdown, reflect closed simulated trade outputs under ideal data conditions. The metrics do not incorporate real-world execution slippage, broker commissions, borrow costs, or unexpected market illiquidity. Historical simulation research serves as an analytical benchmark for quantitative stratEGy design and does not constitute financial advice, an offer to purchase securities, or a guarantee of future automated trading performance.