AME · AMETEK, Inc.
Stock Universe · global stock marketQuantitative Analysis of the Algorithmic Strategy for AME · AME: High-Precision Positioning and Long-Horizon Execution
This analytical report provides an in-depth evaluation of the top-ranked quantitative trading strategy for AME stock on the 15-minute timefrAME. Over a 6.94-year historical evaluation window from September 2019 to August 2026, the strategy generated a cumulative historical return of +981.68% across 23 completed trades, achieving an annualized return of 118.50%. Driven by an 82.61% win rate, a profit factor of 8.75, and a peak historical drawdown restricted to 14.63%, the model demonstrates a selective positioning profile. By pairing fine-grained 15-minute price evaluation with long multi-month position holding times averaging 2,207 hours, the algorithm filters out lower-timefrAME volatility while capitalizing on macro price trends. This study breaks down the strategy's statistical distribution, risk mechanics, yearly profitability, and performance evolution across its recent evaluation window.
Analysis as of 16.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy operating under the designation AME 0 +981.68% 1TRAD-EBV1 holds the rank 1 position on DevioLab for AME stock with a DevioLab quantitative score of 85.385. Evaluated over a nearly seven-year timefrAME spanning September 16, 2019, through August 26, 2026 (6.94 years), the system delivered an overall historical return of +981.68%, translating to an annualized return of 118.50%. The model processes market structure using 15-minute price bars, yet its operational cadence is defined by ultra-low trade frequency, closing just 23 trades over the entire backtested period. Designed as decision logic suitable for automated trading bots, systematic trading robots, and autonomous trading agents, the algorithm focuses on identifying high-conviction macro entries rather than engaging in high-frequency turnover. The strategy completed 19 winning trades against 4 losing trades, establishing an overall win rate of 82.61% and an extraordinary profit factor of 8.75.
Trading rhythm and position duration
Despite utilizing a 15-minute bar interval for trigger evaluation, the strategy's execution cadence reflects macro-style position management rather than short-term intraday trading. Over the 6.94-year historical dataset, the algorithm recorded an average trade frequency of 3.31 trades per year, or 1.21 trades per active month. Position durations are exceptionally extended, with an average holding time of 2,207.00 hours (approximately 91.95 days) and a median holding time of 1,368.75 hours (approximately 57.03 days). The time elapsed between closed trades is similarly spaced, averaging 107.12 days between exits with a median interval of 66.57 days. This creates a statistical dynamic where automated trading agents implementing this logic spend long periods holding positions, absorbing short-term noise to capture macro multi-month price expansions. The substantial divergence between the average holding duration and median duration highlights that while typical trades stay open for roughly two months, select outsized trend movements keep positions active for considerably longer spans.
Quality of historical results
The quality of historical performance is underscored by a heavy statistical edge, reflected in the 8.75 profit factor and an 82.61% win rate across 23 closed trades. The strategy achieved an average trade return of +12.28%, compared to a median trade return of +8.85%. This positive tilt between average and median expectations confirms that while typical trades yield single-digit percentage gains, the model actively allows superior winners to run. The single best historical trade delivered a gain of +57.22%, whereas the single worst trade resulted in a loss of -14.63%. Concentration analysis reveals that the top three winning trades accounted for 41.54% of total gross historical profit. While this indicates a moderate reliance on outlier trend events, the strategy remains structurally sound because even excluding these top three trades, the remaining 16 winning positions contributed a robust majority of the strategy's gains, proving that profitability is distributed across multiple trades rather than single-event anomalies.
Risk, drawdown and losing behavior
Risk management metrics demonstrate disciplined downside containment throughout the 6.94-year dataset. The strategy's maximum historical drawdown reached -14.63%, which precisely matches the magnitude of its worst individual trade (-14.63%). This mathematical alignment indicates that peak equity declines were primarily driven by individual trade exits at stops rather than compounded consecutive losses across open positions. The strategy sustained a longest winning streak of 6 consecutive positive trades, whereas its longest losing streak was capped at just 1 trade. Across all historical market environments, the strategy never recorded back-to-back losing exits. This low sequence risk provides psychological and capital stability for deployment within trading robots and automated trading agents, as equity drawdowns historically recovered rapidly due to the high win rate and high profit factor.
Behavior through time and yearly stability
A year-by-year examination reveals varying activity levels that correspond to changing volatility regimes in AME stock. In 2020, the strategy closed 5 trades with an 80.00% win rate (4 wins, 1 loss), generating a sum return of +104.50%. Following a quiet period in 2021 with no completed trades, the strategy executed 2 trades in 2022 (50.00% win rate) yielding +20.83%. Performance picked up in 2023 with 1 completed win generating +47.72%, followed by 2024 where 3 out of 3 trades were profitable (100.00% win rate), returning +40.61%. In 2025, 5 trades produced an 80.00% win rate (4 wins, 1 loss) with a sum return of +23.62%. In 2026, activity peaked at 7 trades with an 85.71% win rate (6 wins, 1 loss), accumulating +45.23%. Every single calendar year in which trades were closed achieved positive net strategy returns, demonstrating operational stability across differing broader equity market regimes.
Recent period since 2024-06-01 versus full history
Analyzing performance from June 1, 2024, through August 26, 2026, provides a clear view of the strategy's recent operational stability. During this recent evaluation window, the algorithm completed 15 trades, accumulating a combined sum of trade returns equal to +109.46%. Remarkably, 15 of the lifetime total 23 closed trades occurred within this 2.25-year recent window, representing a clear acceleration in transaction frequency compared to the earlier years of the dataset. Despite the increased exit cadence, performance quality remained intact, delivering positive net gains that accounted for a substantial portion of the lifetime cumulative equity curve. This recent stability confirms that the algorithm's multi-month trend filter continues to function effectively under modern price action on AME.
Strengths and limitations
The primary strength of this quantitative strategy lies in its payoff efficiency, characterized by an 8.75 profit factor, an 82.61% hit rate, and a low peak drawdown of 14.63%. By executing infrequently and holding winning positions for an average of over 90 days, the strategy minimizes exposure to short-term market noise while fully capturing primary asset trends. However, key statistical limitations must be noted. The primary limitation is the modest sample size of 23 total completed trades over nearly seven years. While low frequency is an inherent characteristic of high-conviction macro models, small sample sizes introduce wider statistical confidence intervals around expected forward win rates. Additionally, with 41.54% of gross profits concentrated in the top three trades, long-term performance remains dependent on capturing occasional large multi-week trend movements.
DevioLab analytical conclusion
With a top-ranked DevioLab score of 85.385 for AME stock, this algorithmic trading strategy presents a robust quantitative model tailored for medium-to-long-term positioning. Its integration of 15-minute price monitoring with multi-month position holding allows automated trading bots and systematic trading agents to execute entries with precision while avoiding lower-timefrAME overtrading. The strategy's historical track record of positive annual performance, single-trade max drawdown containment, and steady modern performance since mid-2024 establish it as a premier model for systematic equity trading. Prospective operators should nevertheless maintain prudent risk management parAMEters, acknowledging that past simulated backtests serve as analytical baselines rather than forward return guarantees.
Data scope and methodology
The metrics presented in this report are calculated from historical simulated trade data generated by the algorithmic strategy on AME stock using 15-minute candle data between September 16, 2019, and August 26, 2026. All trade statistics, including sum of returns (+981.68%), annualized return (118.50%), profit factor (8.75), drawdown (-14.63%), and recent period performance, reflect completed closed trades recorded in the system dataset. Trade metrics represent unweighted additions of individual closed percentage returns and do not reflect specific live account compounding, leverage, unmodeled broker commissions, dynamic slippage, or order book execution friction. This document is provided strictly for quantitative research and educational evaluation and does not constitute financial advice or investment recommendations.