EOG · EOG Resources, Inc.
Stock Universe · mercado global de açõesEOG Algorithmic Trading Strategy Analysis: High Win-Rate Dynamics and Multi-Week Holding Structure
This empirical evaluation analyzes the 15-minute algorithmic trading strategy for EOG, designated as a core model ranked third for its ticker on DevioLab with a composite score of 67.01. Evaluated across a seven-year historical dataset spanning September 16, 2019, through September 14, 2026, the strategy generated a cumulative historical closed-trade return of 3695.74% across 63 completed trades, equating to an annualized figure of 138.99%. Characterized by a high historical win rate of 79.37% and a profit factor of 3.75, the model operates on an ultra-selective entry profile averaging approximately nine trades per year. Positions are held over multi-week horizons, with a median holding time of 456 hours (19 days) and an average duration of 657.16 hours (27.38 days). Despite strong statistical expectancy—evidenced by an average trade gain of 6.82% and a median trade gain of 7.55%—the strategy experiences significant historical equity volatility, reflected in a maximum drawdown of 52.56% and a single worst trade loss of -46.77%. Profit distribution remains structural, with the top three winning trades contributing just 17.51% of gross gains. This analysis provides quantitative research into the operational mechanics, risk profiles, and temporal stability of the strategy for automated trading execution environments.
Análise em 25.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The evaluated quantitative model is an algorithmic trading strategy engineered for EOG stock, utilizing 15-minute intraday price data as its primary sampling interval. Operating within the equities market, the strategy is designed to provide rule-based decision logic suitable for deployment in automated trading bots, trading robots, and quantitative trading agents. Within the DevioLab framework, the strategy holds a core designation, ranking third among evaluated models for EOG with a quantitative score of 67.01. Across the complete historical evaluation dataset spanning from September 16, 2019, to September 14, 2026—a duration of seven full years—the strategy executed a total of 63 completed trades. The historical performance profile exhibits an overall cumulative closed-trade return of 3695.74%, corresponding to an annualized historical return metric of 138.99%. Out of 63 executed trades, 50 closed in net positive territory while 13 recorded losses, yielding a win rate of 79.37%. The overall statistical efficiency of the trade distribution is reflected in a profit factor of 3.75, indicating that gross historical gains exceeded gross historical losses by a ratio of nearly four to one.
Trading rhythm and position duration
A defining structural characteristic of this strategy is the stark contrast between its intraday execution bar (15-minute intervals) and its actual multi-week position holding periods. Rather than engaging in high-frequency or short-term intraday flipping, the strategy operates as a highly selective positional model. Over the seven-year statistical history, the strategy registered only 63 completed trades, which translates to an average frequency of 9.0 trades per year or approximately 1.21 trades per active month. The time elapsed between trade exits reflects this patient cadence, with a median interval of 30.05 days and an average interval of 39.40 days between closed positions. When an entry signal triggers, positions remain open for extended durations: the median holding time per trade is 456.0 hours (19 full calendar days), while the average holding duration extends to 657.16 hours (approximately 27.38 days). This spatial arrangement reveals an algorithm that uses granular 15-minute price action to pinpoint entry and exit thresholds, yet relies on macroeconomic or sustained trend momentum across multi-week horizons to realize its returns. For automated trading agents or trading robots, this structural rhythm implies very low execution frequency alongside prolonged exposure to overnight stock market gaps and multi-week market volatility.
Quality of historical results
Evaluating the trade-level returns demonstrates a highly consistent historical profit distribution anchored by favorable statistical expectancy. The strategy achieved an average trade gain of 6.82% across all 63 completed positions, which is strongly reinforced by a median trade return of 7.55%. The proximity and slight superiority of the median relative to the mean suggest that positive expectancy is not an artifact of a few isolated windfalls, but rather a structural property of the trade sample. This observation is corroborated by profit concentration metrics: the three largest winning trades generated only 17.51% of the strategy's total gross profits. This relatively low concentration ratio confirms that historical net profitability was distributed broadly across the 50 winning trades rather than depending on extreme positive outliers. The single best trade achieved a return of 43.47%. The combination of an 79.37% hit rate, a 3.75 profit factor, and a broad-based gross profit distribution indicates that the strategy historically possessed a robust statistical payoff profile during the observed period.
Risk, drawdown and losing behavior
While the strategy displays strong cumulative returns and a high win rate, its historical risk metrics reveal substantial drawdown risk and severe downside asymmetry in individual adverse events. The peak-to-trough maximum drawdown recorded across the seven-year sample reached 52.56%. This severe historical equity contraction occurs despite a modest maximum consecutive losing streak of only 3 trades. The primary driver of severe capital degradation is not sequence risk from prolonged losing streaks, but rather magnitude risk in single losing positions. The worst historical trade recorded a loss of -46.77%, which nearly mirrors the magnitude of the strategy's best single winning trade (+43.47%). In an equity trading model where 50 out of 63 trades are profitable, a single loss of nearly 47% highlights that unhedged positions held over multi-week periods are vulnerable to severe market moves or overnight gap downs. The coexistences of a 79.37% win rate and a 52.56% maximum drawdown underscores a critical tension: while winning trades occur with high frequency and moderate gains, rare losing trades can inflict disproportionate damage on portfolio equity.
Behavior through time and yearly stability
An examination of the yearly performance breakdown shows distinct operational phases across the seven-year timeline. In the early portion of the sample, the strategy demonstrated exceptional productivity: 2020 yielded 12 trades with a 66.67% win rate and a cumulative return sum of +89.48%; 2021 generated 10 trades with a 90.00% win rate and +91.73% cumulative return; and 2022 recorded its highest annual return sum at +107.47% across 13 trades with a 92.31% win rate. However, performance softened during the mid-period. In 2023, the model closed 10 trades with a reduced win rate of 60.00% and a net return sum of +34.11%. Activity compressed sharply in 2024, recording only 2 completed trades (1 win, 1 loss) for a combined sum of +14.51%. The strategy regained cadence in 2025 with 7 trades, a 71.43% win rate, and +17.10% total return, followed by a strong rebound in 2026, which recorded 9 trades, a 100.00% win rate, and a return sum of +75.02%. This temporal distribution illustrates that while the strategy maintains long-term positive drift, trade frequency and annual net return fluctuate significantly depending on market regimes.
Recent period since 2024-06-01 versus full history
Analyzing performance during the recent observation window beginning June 1, 2024, provides valuable context on current strategy dynamics relative to its broader history. From June 1, 2024, through the end of the history on September 14, 2026, the strategy executed 18 completed trades, generating a cumulative return sum of 106.63%. This recent phase accounts for 28.57% of total historical completed trades (18 of 63) and a meaningful portion of total net return. The active trade execution during this recent period demonstrates that the underlying algorithm has remained functional and actively generating signals rather than falling dormant. Furthermore, the performance observed since mid-2024—highlighted by 7 trades in 2025 and 9 trades in 2026—shows a recovery in trade frequency compared to the sparse activity seen in early 2024. The empirical metrics from this recent window confirm that the strategy's recent return profile aligns with its multi-year historical expectations.
Strengths and limitations
The primary historical strength of this strategy lies in its exceptional trade efficiency and balanced profit generation. A 79.37% win rate combined with a profit factor of 3.75 demonstrates a strong statistical edge over the observed sample. The low concentration of profit among top winners (17.51%) confirms that returns are structurally distributed across multiple trades. Additionally, the low execution frequency (9 trades per year) limits signal noise and overhead associated with hyperactive trading. Conversely, the strategy's main limitation is its severe downside exposure during adverse market events. With a historical maximum drawdown of 52.56% and a single worst trade of -46.77%, the model exhibits significant left-tail risk. Because average holding periods extend beyond 19 to 27 days, open positions are continuously exposed to broader stock market drawdowns and earnings-related volatility. These factors require careful consideration when evaluating risk tolerance and portfolio allocation.
DevioLab analytical conclusion
With a DevioLab score of 67.01 and a rank of 3 for EOG stock, this 15-minute algorithmic strategy presents a compelling case study in low-frequency positional trading. The empirical evidence demonstrates that analyzing 15-minute price structures can yield highly effective entries for positions held across multi-week timeframes. The strategy excels at capturing structural directional moves while maintaining a high win rate (79.37%) and robust expectancy (median trade of 7.55%). However, the quantitative profile also highlights significant capital risk, evidenced by a 52.56% historical drawdown and severe single-trade losses. For quantitative traders and developers designing automated trading bots or trading agents, the strategy offers a robust historical blueprint with strong statistical expectancy, provided that risk parameters and position-sizing frameworks are configured to withstand extended multi-week holding periods and severe peak-to-trough drawdowns.
Data scope and methodology
The performance metrics presented in this evaluation are derived strictly from simulated historical backtest results for the EOG trading strategy across the period from September 16, 2019, to September 14, 2026. All trade statistics, holding times, win rates, and drawdown figures represent closed completed positions evaluated on historical 15-minute stock price data. Historical backtested performance does not account for real-time execution factors such as order routing delays, bid-ask spread costs, market slippage, margin interest, or exchange fees. The history start date reflects the initiation of the backtest dataset and does not indicate the corporate origin or listing date of the underlying asset. These statistical findings are provided exclusively for quantitative research and software educational purposes and do not constitute investment advice or a guarantee of future live performance.