AZO · AutoZone, Inc.
Stock Universe · mercado global de açõesQuantitative Strategy Analysis: AZO · AutoZone High-Precision 15-Minute Algorithmic Model
This quantitative evaluation analyzes a top-ranked algorithmic trading strategy applied to AZO (AutoZone) across a 6.94-year historical backtest dataset spanning September 2019 through August 2026. Designed on a 15-minute execution interval, the quantitative model generates long-horizon swing positions with an average holding duration of 428.03 hours (approximately 17.8 days) and a median holding duration of 282.50 hours. Over 81 completed historical trades, the strategy achieved an 85.19% win rate, a profit factor of 5.0, and a maximum drawdown capped at 13.44%, yielding a cumulative closed profit of +2609.67%. Uniquely, gross profits are well-distributed across trades, with the top three winning positions representing only 21.85% of total gross profit. Recent performance from June 1, 2024 onward accounts for 32 completed trades and a cumulative return of +86.22%, proving continued alignment across recent market regimes.
Análise em 21.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The algorithmic trading model analyzed here operates on the 15-minute chart for AZO (AutoZone) and represents the primary core strategy for this equity, earning a DevioLab quantitative score of 85.94 and ranking first among strategies evaluated for this ticker. Spanning an extensive observation period of nearly seven years from September 16, 2019, to August 24, 2026, the strategy serves as decision logic for automated trading bots, trading robots, and quantitative trading agents seeking structured exposure to single-stock price trends. Rather than engaging in micro-level intraday scalping, the 15-minute resolution functions as an entry and exit trigger frame for multi-day and multi-week positioning. Across 81 completed trades, the model has delivered a cumulative historical performance of +2609.67%, which translates to an annualized return figure of 120.74%. The underlying framework relies on a highly selective entry rule set that prioritizes signal quality over market exposure, resulting in long periods of flat allocation interspersed with high-conviction trades.
Trading rhythm and position duration
Despite operating on a 15-minute price series, the strategy displays a surprisingly patient trading rhythm. It executes an average of only 11.67 trades per year, which corresponds to approximately 1.42 completed trades per active month. The temporal spacing between trade exits underscores this selective cadence, averaging 29.47 days between completed trades with a median exit interval of 24.51 days. Position holding times reflect a macro-swing duration rather than short-term momentum trading. The average holding duration per trade stands at 428.03 hours (approximately 17.8 days), while the median holding duration is 282.50 hours (roughly 11.8 days). This multi-week holding timeframe allows positions to capture extended trends in AZO without being prematurely shaken out by intraday volatility. The divergence between the fast 15-minute chart resolution and the long position duration indicates that the decision logic utilizes intraday price action primarily to optimize execution timing while riding multi-week equity trends.
Quality of historical results
The quality of historical returns generated by this strategy is characterized by exceptionally high hit rates and balanced trade distribution. Out of 81 completed trades, 69 were closed at a profit and 12 resulted in losses, yielding an overall win rate of 85.19%. The strategy achieved a profit factor of 5.0, meaning that gross realized profits exceeded gross realized losses by five times. The average gain per trade is +4.57%, while the median trade yield stands at +3.37%. Performance exhibits a healthy, non-skewed profile across the historical trade log: the top three winning trades combined generated 21.85% of total gross profits. This indicates that the strategy's +2609.67% total return is not dependent on a few isolated, extreme outliers, but rather built upon repeatable, moderate gains. The single best trade realized a gain of +38.49%, demonstrating that when an exceptional trend develops, the trailing exit logic permits substantial upside capture while maintaining strong statistical baseline quality.
Risk, drawdown and losing behavior
Risk containment is a central highlight of this strategy's empirical footprint. Over nearly seven full years of simulated trading in AZO, the maximum peak-to-trough drawdown was constrained to 13.44%. This modest drawdown is particularly notable when contrasted with the cumulative closed trade gain of +2609.67%. The strategy's worst single losing trade was capped at -10.11%, reflecting a well-defined risk management boundary relative to its average winning upside. Furthermore, losing streaks have historically been brief and contained. The longest consecutive losing streak observed across the entire 81-trade dataset was just 2 trades. In contrast, the longest winning streak reached 18 consecutive profitable trades. The combination of an 85.19% win rate and a maximum consecutive loss count of 2 indicates that equity drawdowns tend to be shallow and brief, reducing the historical psychological and capital strain often associated with trend-following quantitative models.
Behavior through time and yearly stability
An examination of yearly statistical breakdowns demonstrates sustained multi-year performance consistency across diverse equity market environments. In 2020, the strategy completed 10 trades, achieving a perfect 100.00% win rate and producing a summed return of +105.96%. The following year, 2021, saw 7 completed trades, again delivering a 100.00% win rate with a cumulative yield of +79.57%. As market dynamics evolved in 2022, trade frequency increased to 13 completed trades with 10 wins and 3 losses (76.92% win rate) generating +36.90%. In 2023, the model recorded 15 completed trades with 14 wins and 1 loss (93.33% win rate), accruing +43.47%. In 2024, performance remained stable with 14 trades, 10 wins, and 4 losses (71.43% win rate) yielding +42.70%. In 2025, 13 trades produced 10 wins (76.92% win rate) and +43.94%. In the partial dataset for 2026 through August, 9 trades yielded 8 wins (88.89% win rate) and +17.95%. Across all observed full calendar years, annual summed profits consistently landed between +36% and +105%, displaying remarkable structural stability over time.
Recent period since 2024-06-01 versus full history
Evaluating the recent performance sample beginning June 1, 2024 provides a clear view of how the strategy has adapted to recent market conditions. Within this window, the model registered 32 completed trades out of its historical total of 81, representing nearly 40% of all trade events in the dataset. Across these 32 recent trades, the strategy accumulated a total gain of +86.22%. Comparing this to the long-term history demonstrates that trade generation has slightly accelerated in the recent period, reflecting a higher concentration of valid setup conditions on the 15-minute timeframe. The substantial trade sample size of 32 closed positions since mid-2024 confirms that recent returns are statistically robust and not derived from a single fluke trade. The strategy's ability to maintain a strong hit rate and positive expected return across recent months validates its continued relevance and statistical stability without exhibiting signs of signal decay or over-optimization.
Strengths and limitations
The primary structural strength of this strategy lies in its high win rate of 85.19% coupled with a solid 5.0 profit factor and a modest maximum drawdown of 13.44%. The multi-week position duration minimizes exposure to noise while keeping execution overhead low, making it an efficient candidate for execution by automated trading bots and trading agents. The concentration of the top three winning trades at 21.85% highlights that profits are well-distributed across many trades. Conversely, the strategy's primary limitation is its low trade frequency of 11.67 trades per year. With an average of roughly 29.47 days between trade exits, automated trading robots executing this logic must endure extended periods of inactivity where capital remains unallocated or bound in long-running positions. Additionally, because individual positions are held for an average of nearly 18 days, short-term tactical traders seeking daily turnover will find this slow-paced approach unsuited to their operational objectives.
DevioLab analytical conclusion
The AZO 15-minute algorithmic model stands out as a highly engineered quantitative strategy that combines swing-trading holding periods with granular 15-minute entry precision. Achieving a top rank on DevioLab with a score of 85.94, the strategy effectively balances aggressive upside (+2609.67% total closed profit) with controlled downside risk (13.44% maximum drawdown). Its high historical hit rate of 85.19% and low consecutive loss record provide strong structural resilience. For quantitative developers and traders deploying automated trading agents or trading robots, this strategy offers a compelling template for single-stock swing trading, provided that operators are comfortable with low trade frequency and multi-week position holding times.
Data scope and methodology
The findings in this report are based strictly on historical backtested trade logs for the AZO strategy covering the period from September 16, 2019 to August 24, 2026. The dataset encompasses 81 completed simulated closed trades executed on a 15-minute bar interval. All performance metrics, including trade returns, holding durations, win rates, drawdowns, and yearly breakdowns, reflect closed-trade figures calculated on a fixed-lot or standard compounding basis as provided in the source research statistics. These statistics describe historical simulated results and do not represent live brokerage trading, guaranteed future performance, or individual financial advice. Realized live trading results may differ due to market conditions, execution latency, slippage, spread dynamics, and commission costs.