AVY · Avery Dennison Corporation
Stock Universe · 全球股票市场AVY Algorithmic Trading Strategy Analysis: Structural Consistency and Controlled Drawdown in Multi-Week Automated Execution
This quantitative research report evaluates the top-ranked algorithmic trading strategy for AVY on the 15-minute chart, designed for deployment as decision logic in automated trading bots and trading agents. Spanning 7 years of backtested execution history, the strategy delivered a cumulative trade profit sum of +1864.08% across 65 completed trades, backed by an exceptional 80.00% win rate and a profit factor of 7.50. Rather than high-frequency execution, the model operates on a low-frequency, long-duration swing framework with an average holding period of 606.32 hours (approximately 25 days) and a maximum peak-to-trough drawdown limited to 9.27%. This analysis details the underlying payoff structure, profit concentration metrics, annual performance consistency, and recent output recorded since mid-2024.
分析日期： 21.09.2026. 策略数据单独更新。阅读完整分析
1. Strategy profile
The strategy, identified in system logs as AVY 0 +1864.08% 1TRAD-HBW3, represents the primary decision engine for AVY in the DevioLab framework, ranking first among evaluated systematic models for this asset with a DevioLab score of 84.55. Operating on a 15-minute candlestick cadence across a 7-year sample from September 16, 2019, through September 21, 2026, the strategy serves as logic designed for automated trading bots, trading agents, and systematic execution infrastructure. Over this period, the model closed 65 trades, achieving a high win rate of 80.00% with 52 winning trades against 13 losing trades. The strategy accumulated a cumulative gross return sum of +1864.08% across all closed positions, translating to an annualized performance metric of 76.37%. The overall statistical profile reflects a patient, selective quantitative engine that prioritizes trade selection quality over signal generation frequency.
2. Trading rhythm and position duration
A defining characteristic of this strategy is the stark contrast between its micro-level execution interval (15-minute price bars) and its macro-level holding duration. Despite sampling price action every 15 minutes, the strategy executes an average of only 9.27 trades per year, or approximately 1.25 trades per active month. The mean duration for open positions stands at 606.32 hours (roughly 25.26 calendar days), while the median holding duration is 360.00 hours (15.00 calendar days). On average, 37.82 days elapse between trade exits, with a median exit spacing of 28.63 days. This confirms that automated trading robots applying this decision logic are not engaging in high-frequency scalping or daily intraday churn. Instead, the 15-minute time step is utilized to pinpoint precise entry and exit triggers for positions that evolve over several weeks, allowing the system to capture extended multi-week directional swings in AVY.
3. Quality of historical results
The strategy's payoff mechanics demonstrate remarkable quality, underpinned by an exceptional profit factor of 7.50. The average return per completed trade is +5.11%, closely mirrored by a median trade return of +4.56%. This tight alignment between average and median returns indicates that overall performance is not inflated by isolated black-swan outliers, but is driven by a repeatable core distribution of profitable trades. The single best trade achieved a return of +29.14%, whereas the worst trade incurred a loss of -9.27%. Furthermore, the top three winning trades account for only 18.27% of gross profit sum, confirming that gross returns are broadly distributed across the 52 winning trades rather than concentrated in a handful of lucky signals. This distributed payoff architecture enhances the statistical durability of the automated trading logic.
4. Risk, drawdown and losing behavior
Risk mitigation metrics within the dataset highlight a remarkably disciplined historical loss profile. The maximum peak-to-trough drawdown recorded across the entire 7-year evaluation period was restricted to 9.27%, matching the magnitude of the single worst trade (-9.27%). The strategy experienced a maximum consecutive losing streak of just 2 trades, contrasted against a maximum winning streak of 10 consecutive profitable trades. Combined with an 80.00% historical accuracy rate, losing events have historically occurred as brief, isolated pullbacks rather than systemic equity regime collapses. This controlled drawdown behavior provides a comfortable operational buffer for systematic trading agents, reducing the likelihood of catastrophic equity curve decay during adverse market phases.
5. Behavior through time and yearly stability
Annual performance metrics across the 2020-2026 backtest window reveal consistent profitability without a single negative year recorded. Output peaked during 2020 with 14 trades yielding a +119.10% total return sum at an 85.71% win rate, followed by 2021 with 8 trades generating +72.91% at an 87.50% win rate. During challenging market environments such as 2022, the strategy maintained profitability, generating +19.66% across 10 trades with a 70.00% win rate. Subsequent years maintained steady closed returns: 2023 saw +35.22% across 9 trades (88.89% win rate), 2024 produced +30.77% across 7 trades (85.71% win rate), 2025 logged +14.52% across 8 trades (62.50% win rate), and 2026 generated +40.13% across 9 trades (77.78% win rate). Annual trade counts remained stably bounded between 7 and 14 trades per calendar year, reflecting structural consistency in signal generation.
6. Recent period since 2024-06-01 versus full history
In the recent evaluation window covering closed trades with exit timestamps on or after June 1, 2024, the strategy recorded 22 completed positions generating a combined trade profit sum of +68.63%. Accounting for roughly one-third of total historical trade volume (22 out of 65 completed trades), this recent subset demonstrates sustained activity and ongoing efficacy in recent market environments. The average yield per trade in this recent segment stands at approximately +3.12%, compared to the long-term historical mean of +5.11%. While the average gain per trade experienced a slight moderation, the frequency of signals remained robust, providing substantial contemporary verification that the underlying logic continues to function effectively for automated trading bots without suffering structural breakdown.
7. Strengths and limitations
The primary strengths of this AVY strategy center on its high win rate (80.00%), outstanding profit factor (7.50), strictly controlled historical drawdown (9.27%), and low reliance on outlier trades (top 3 winners contribute under 18.30% of gross profits). The low annual trade volume (9.27 trades per year) prevents over-trading. Conversely, the key limitation lies in sample size density: with only 65 total trades completed over 7 years, statistical confidence intervals around expectancy are broader than those of high-frequency models. Additionally, the multi-week average holding time of ~25 days exposes open positions to overnight gap risk, macro economic releases, and earnings announcements, which must be factored into real-world automated trading robot risk management.
8. DevioLab analytical conclusion
The AVY 0 +1864.08% 1TRAD-HBW3 strategy achieves its top rank on DevioLab through an exceptional balance of high accuracy, robust profit factor, and low historical equity drawdown. Designed to act as robust decision logic for systematic trading agents, the model successfully avoids the pitfalls of curve-fitting by demonstrating consistent annual profitability across diverse market regimes from 2020 through 2026. While the low total trade count requires ongoing monitoring of future execution samples, the broad distribution of returns and strong post-June 2024 performance reinforce its standing as a high-quality quantitative framework for long-horizon swing execution in AVY.
9. Data scope and methodology
All figures presented in this research report are derived exclusively from historical backtested execution data for asset symbol AVY on the 15-minute timeframe between September 16, 2019, and September 21, 2026. Metrics describe closed trade returns generated by systematic execution simulation and do not account for real-time order routing, exchange slippage, liquidity constraints, or commission fees. Historical simulation metrics offer statistical insights into algorithmic behavior but do not guarantee future live performance. This analysis is presented purely for quantitative research and software educational purposes and does not constitute financial or investment advice.