DG · Dollar General Corp.
Stock Universe · mercado bursátil globalQuantitative Strategy Analysis: DG Algorithmic Strategy Achieves 2053.69% Return via Moderate-Frequency Swing Execution
This empirical study evaluates the rank-one algorithmic trading strategy for DG stock on a 15-minute execution timeframe, covering a 7.09-year evaluation window from September 2019 to October 2026. Generating a cumulative backtested return of 2053.69% across 121 closed trades, the system demonstrates strong pay-off efficiency highlighted by a 73.55% win rate, a 3.75 profit factor, and a remarkably low reliance on outlier trades, with the top three winners accounting for only 15.18% of gross profits. Operating with an average holding period of 312.25 hours and a trade frequency of 17.07 exits per year, the strategy exemplifies a disciplined multi-day swing framework suitable for automated trading bots and trading agents seeking high signal selectivity and controlled drawdown dynamics.
Análisis al 17.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
1. Strategy profile
The strategy, designated within the DevioLab framework as DG 0 +2053.69% 1TRAD-JDB8, represents the core rank-one system evaluated for DG equity trading on a 15-minute bar execution schedule. Over a historical evaluation period spanning 7.09 years from September 16, 2019, to October 17, 2026, the strategy recorded 121 completed trade cycles. Achieving a DevioLab overall score of 77.40, the system achieved a total cumulative closed-trade return of 2053.69%, translating to a annualized rate of 370.75%. Built as an explicit quantitative decision logic, the strategy provides automated trading bots and trading robots with an objective, rules-based mechanism for navigating price action in DG stock without human bias or discretionary interventions. The strategy relies on a selective structural design that generates roughly 1.66 closed positions per active month, prioritizing high-conviction market opportunities over high-frequency exposure.
2. Trading rhythm and position duration
Despite operating on a granular 15-minute price interval for execution precision, the strategy exhibits the characteristics of a multi-day swing to position trading model rather than a high-frequency or day-trading system. The strategy records an average position holding duration of 312.25 hours (approximately 13.0 days) and a median holding duration of 197.75 hours (roughly 8.2 days). This divergence between the 15-minute signal evaluation frequency and multi-day trade persistence allows automated trading agents to filter out intra-day noise while securing execution precision at position entries and exits. The temporal spacing between trade exits further reinforces this deliberate pacing: the strategy registers an average of 21.36 days between trade exits and a median spacing of 16.72 days. With an annualized trade frequency of 17.07 trades per year, the model maintains extended periods of non-exposure, protecting capital during choppy or uncertain structural regimes while participating fully in established trend movements.
3. Quality of historical results
The historical return profile reveals exceptional distribution balance and high execution efficiency. Out of 121 completed trades across the full history, 89 ended in profit while 32 resulted in losses, yielding a win rate of 73.55%. The statistical quality of this performance is underscored by a profit factor of 3.75, demonstrating that gross historical gains outweighed gross historical losses by nearly four to one. A key structural strength of the system is the symmetrical balance between its average trade performance (+2.92%) and its median trade performance (+3.07%). Because the median trade percentage closely matches and slightly exceeds the mean, the strategy's profitability is shown to be a stable product of consistent medium-sized wins rather than skewed by a handful of extreme unexpected returns. This structural stability is further validated by the winner concentration metric: the top three winning trades collectively generated just 15.18% of total gross profit. With the best historical trade reaching +27.97% and the worst trade contained at -13.14%, the payoff profile demonstrates effective upside participation paired with disciplined downside containment.
4. Risk, drawdown and losing behavior
Risk management metrics across the 7.09-year historical dataset indicate a resilient defense against deep capital erosion, despite the volatility inherent in equity markets. The maximum backtested drawdown was held to 24.66%, a moderate figure when compared to the 2053.69% total cumulative return generated over the same horizon. The strategy's winning and losing sequence dynamics highlight strong structural resilience: the system achieved a maximum consecutive winning streak of 14 trades, whereas the longest consecutive losing streak was strictly limited to 4 trades. This asymmetry between win streaks and loss streaks indicates that adverse market regimes were quickly recognized and managed, preventing catastrophic loss compounding. The worst single trade loss of -13.14% represents a well-bounded tail-risk event relative to the cumulative performance gains, illustrating that risk controls successfully prevented single-trade disasters from impairing overall strategic performance.
5. Behavior through time and yearly stability
An examination of the yearly performance breakdown shows consistent profitability across distinct market environments, with seven out of eight calendar years posting positive cumulative closed returns. In 2019 (partial history), the strategy logged 3 completed trades, all profitable (100% win rate), returning +6.54%. In 2020, activity scaled to 14 trades with an 85.71% win rate and an +80.70% sum. The system sustained momentum into 2021 with 10 trades (60.00% win rate, +19.66% return) and 2022 with 17 trades (82.35% win rate, +61.78% return). The strategy experienced its sole negative calendar year in 2023, recording 16 trades with an even 50.00% win rate and a modest drawdown sum of -8.77%. Profitability recovered sharply in 2024, yielding 23 trades, a 65.22% win rate, and +13.25% return. Performance accelerated dramatically in 2025, generating 21 trades, a 76.19% win rate, and a +117.39% net sum, followed by 2026 (partial year) with 17 trades, an 88.24% win rate, and +63.35% return. This multi-year trajectory confirms that performance gains were broadly distributed across various structural market cycles rather than concentrated in a single fortunate snapshot.
6. Recent period since 2024-06-01 versus full history
Evaluating recent historical performance provides crucial insight into the strategy's contemporary relevance and adaptability to modern market microstructure. In the sample period from June 1, 2024, to October 17, 2026, the strategy executed 50 completed trade cycles, accounting for 41.32% of all completed trades across the 7.09-year dataset. During this recent window, the strategy accumulated a combined return sum of +176.23%. This high concentration of recent trade activity and robust profitability demonstrates that the core logic has remained effective in recent conditions rather than relying on stale historical regimes. The recent average trade yield aligns closely with full-history performance, confirming that the system continues to capture structural trend opportunities in DG stock with high statistical fidelity.
7. Strengths and limitations
The primary quantitative strength of this strategy lies in its outstanding return-to-risk architecture, characterized by a 73.55% win rate, a 3.75 profit factor, and a broad-based distribution of profits where the top three trades contribute only 15.18% of gross gains. Its low trade frequency (17.07 trades per year) and substantial average holding period (312.25 hours) minimize exposure to short-term market noise while maintaining low operational drag for automated deployment. Conversely, structural limitations include extended periods of inactivity, with average spacing between exit events exceeding 21 days, which may require capital patience from algorithmic operators. Additionally, while the maximum drawdown of 24.66% is favorable relative to total gain, it still represents a meaningful historical equity retraction that potential operators must accommodate within their broader portfolio risk buDGets.
8. DevioLab analytical conclusion
With a top-ranked position for DG equity strategies and a DevioLab score of 77.40, the system presents a compelling case study in quantitative swing design. By pairing 15-minute trade execution precision with multi-day position hold times, the strategy achieves an effective balance between signal timing and trend capture. The statistical alignment of average trade (+2.92%) and median trade (+3.07%), alongside robust modern performance (+176.23% return across 50 trades since mid-2024), confirms that historical profits reflect a repeatable, broad-based distribution rather than an unrepresentative tail event. For quantitative researchers and developers configuring automated trading agents, this strategy offers a validated framework for systematic DG stock trading.
9. Data scope and methodology
The empirical findings in this report are calculated exclusively from backtested closed-trade records for DG stock on a 15-minute bar interval between September 16, 2019, and October 17, 2026. All statistics, including win rates, drawdowns, profit factors, and trade durations, represent historical simulated outcomes under ideal backtest conditions. They do not account for external real-world factors such as variable order book slippage, exchange fees, dynamic interest rates, dividend adjustments, or unexpected execution delays. Historical performance results are strictly analytical metrics designed for system evaluation and do not constitute investment advice or guarantee future trading performance.