APH · Amphenol Corp.
Stock Universe · mercado global de açõesQuantitative Strategy Analysis: APH Low-Frequency Swing Model Delivers 86.54% Win Rate Across Seven-Year Testing Window
This statistical analysis evaluates an algorithmic trading model developed for APH on a 15-minute timeframe across a seven-year backtest horizon from September 2019 to September 2026. Demonstrating an exceptional win rate of 86.54% across 52 completed trades, the strategy relies on extended position holding times averaging 903.72 hours and low trade frequency of 7.42 trades per year. With a profit factor of 5.0, a maximum drawdown of 20.94%, and a well-balanced distribution where the top three winning trades generate only 17.96% of gross profit, the system displays remarkable pay-off consistency across diverse market regimes. Recent performance since June 2024 reflects sustained efficiency with 25 completed trades yielding a cumulative sum of +252.60%. Designed as quantitative logic suitable for automated trading bots, trading robots, or automated trading agents, this strategy provides a compelling case study in structural patient swing trading within equities.
Análise em 18.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
1. Strategy profile
The quantitative strategy under evaluation, designated for APH on a 15-minute price bar interval, represents a disciplined low-frequency swing trading framework. Over a backtested history spanning seven years from September 16, 2019, to September 18, 2026, the model completed exactly 52 historical trades. With a DevioLab score of 78.51 and ranking second among strategies tested on this symbol, the system prioritizes high-conviction exposure over frequent transaction volume. Across the full historical dataset, the strategy achieved 45 winning positions and 7 losing positions, yielding a win rate of 86.54%. Cumulative backtested trade returns sum to +6023.58%, underpinned by a profit factor of 5.0. This strategy architecture is explicitly configured to serve as algorithmic decision logic for execution by automated trading bots, trading robots, or algorithmic trading agents seeking disciplined, hands-off stock market exposure.
2. Trading rhythm and position duration
A defining structural feature of this algorithmic model is its patient, extended holding period combined with sparse signal generation. Operating on a 15-minute bar resolution, the system averages just 7.42 completed trades per year, or approximately 1.16 trades per active month. The mean duration for open positions stands at 903.72 hours (approximately 37.6 days), while the median holding time is 539.63 hours (roughly 22.5 days). The divergence between the mean and median holding periods indicates that while many trades resolve within three to four weeks, select positions are held for multi-month durations to capture broader trend continuation. Between completed closed trades, the average interval is 46.18 days, with a median spacing of 39.03 days. This low-frequency trading rhythm demonstrates that the underlying algorithm is designed to remain idle during choppy market conditions, deploying capital only when explicit structural criteria are met.
3. Quality of historical results
Examine the quality of returns reveals a symmetrical and non-skewed profit distribution. The strategy generated an average trade return of +8.83% against a median trade return of +8.99%. The close alignment between the mean and median indicates that historical gains were not driven by accidental statistical outliers, but rather by repeatable, consistent profitability per trade setup. Furthermore, the top three winning trades accounted for only 17.96% of total gross profit. In quantitative evaluation, a low top-three concentration ratio confirms that strategy performance is distributed broadly across its winning trades rather than dependent on a tiny handful of extreme gainers. The strategy's best single trade achieved +43.34%, illustrating solid upside capture when trends fully develop, while the robust profit factor of 5.0 confirms that gross profits outweighed gross losses by a fivefold margin.
4. Risk, drawdown and losing behavior
Risk management metrics reveal a tightly controlled downside footprint balanced by rare, moderate drawdowns. The strategy experienced a maximum historical drawdown of 20.94%, which exactly matches its single worst trade loss of -20.94%. This alignment indicates that peak-to-trough equity declines were driven primarily by isolated single-trade stop losses or adverse market gaps rather than cascading series of accumulated losses. The strategy exhibited remarkable streak resilience, achieving a peak winning streak of 19 consecutive profitable trades, while its longest losing streak was limited to just 2 trades. Out of 52 total trades, only 7 resolved in losses. The primary operational discomfort for an automated trading bot executing this logic is not trade-level frequency of loss, but rather the psychological and capital commitment required during holding periods of over 900 hours when an individual trade moves into a temporary -20% drawdown.
5. Behavior through time and yearly stability
Annual performance breakdown from 2020 through 2026 demonstrates consistent profitability across different stock market environments. In 2020, the strategy generated +35.56% across 6 trades with an 83.33% win rate. In 2021, returns expanded to +58.49% over 8 trades with an 87.50% win rate. Performance remained positive during the challenging equity environment of 2022, generating +36.56% across 6 trades despite two losing entries (66.67% win rate). The system recorded a perfect 100% win rate in 2023 across 4 trades, accumulating +43.98%. Activity accelerated significantly in 2024 (+42.10% over 7 trades), 2025 (+129.44% over 11 trades, 90.91% win rate), and 2026 (+112.83% over 10 trades, 100% win rate). The stability of annual positive return sums highlights that the strategy's edge is structurally embedded rather than localized to a single favorable regime.
6. Recent period since 2024-06-01 versus full history
Analyzing recent data since June 1, 2024, reveals a distinct increase in trade opportunity frequency alongside preserved signal quality. In this recent window, the model completed 25 trades, generating a cumulative return sum of +252.60%. Considering that the full seven-year dataset comprises 52 trades, nearly half of all historical closed trades occurred in the post-June 2024 period. This indicates an environment characterized by clearer technical setups or higher volatility suited to the algorithm's entry conditions. Despite the increased execution pace, performance metrics remained pristine, matching or exceeding full-history averages. The recent sample demonstrates that the strategy's decision logic continues to function effectively under contemporary market conditions without evidence of alpha decay.
7. Strengths and limitations
The primary analytical strengths of this strategy include an exceptionally high win rate (86.54%), strong overall pay-off ratio (profit factor 5.0), broad profit distribution with minimal outlier dependency (17.96% top-three winner share), and consistent multi-year profitability. The model's low trade count reduces execution exposure, making it an ideal candidate for automated trading agents or trading robots. Conversely, key structural limitations stem from the small total trade sample size of 52 closed positions over seven years, which increases statistical variance in confidence bounds. Additionally, holding positions for an average of 37.6 days exposes capital to overnight equity gap risk and corporate actions, while the worst single trade loss of -20.94% underscores that when losses do occur, they can be substantial relative to the average win.
8. DevioLab analytical conclusion
With a DevioLab score of 78.51 and a rank of 2 for APH, this 15-minute quantitative model stands out as a high-precision, low-turnover swing strategy. Its design trade-off is clear: by accepting extended position durations and weeks of inactivity between trades, the algorithm filters out market noise and achieves an impressive 86.54% historical win rate. The high alignment between average (+8.83%) and median (+8.99%) trade performance further validates the structural integrity of its reward profile. For quantitative traders seeking logic to deploy within automated trading bots or trading agents, the system offers a robust template focused on patience, factor exposure, and drawdown containment.
9. Data scope and methodology
All metrics presented in this analysis are derived strictly from historical backtested simulation data for APH across the period from September 16, 2019, to September 18, 2026. Trade statistics, holding durations, win rates, and drawdown figures reflect closed positions generated on a 15-minute bar timeframe. Historical performance simulation does not account for live market execution factors such as slippage, variable order routing, broker commission structures, or liquidity constraints. Historical trade performance is not indicative or predictive of future live results. This research report is produced solely for technical analysis and strategy evaluation purposes and does not constitute financial, legal, or investment advice.