AON · Aon plc Class A
Stock Universe · mercado global de açõesAON · AON Algorithmic Strategy Analysis: High Win-Rate Multi-Week Trend Model with Exceptional Drawdown Containment
This quantitative evaluation analyzes a top-ranked algorithmic trading strategy designed for AON (AON stock), operating on a 15-minute timeframe over a seven-year evaluation window (September 2019 through September 2026). Holding the #1 rank for AON with a DevioLab Score of 85.14, the model delivers a cumulative closed-trade return of +1849.58% across 81 completed transactions, translating to an annualized backtested return of 148.26%. Despite utilizing 15-minute bar granularity for execution signals, the system exhibits a multi-week swing or position holding structure, averaging 476.93 hours (approximately 19.9 days) per position and generating only 11.56 trades per year. The statistical profile is characterized by an 87.65% win rate, a profit factor of 2.50, and a tightly constrained maximum historical drawdown of 9.43%. Importantly, gross profits are broadly distributed rather than dependent on extreme single-trade outliers, with the top three winning trades contributing just 14.28% of total gross gains. Recent execution since June 1, 2024, accounts for 31 completed trades and +107.13% in accumulated gain, confirming robust statistical activity in contemporary market conditions. Designed for implementation via automated trading bots, trading robots, or quantitative trading agents, this strategy provides a compelling example of patient, high-precision swing trading in equity markets.
Análise em 17.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
1. Strategy profile
The evaluated quantitative model is a core algorithmic trading strategy specifically calibrated for AON (AON stock). Evaluated across a comprehensive seven-year historical dataset spanning from September 16, 2019, to September 17, 2026, the strategy recorded 81 total completed trades. Over this multi-year evaluation, it generated a cumulative closed-trade profit of +1849.58%, corresponding to an annualized historical performance metric of 148.26%. Based on its rigorous risk-adjusted performance metrics, the system achieves a DevioLab Score of 85.14, earning the #1 ranking among all evaluated quantitative strategies for this asset. Operating on a 15-minute price bar interval (15m), the strategy utilizes granular price action data to evaluate entry and exit conditions. However, its behavioral footprint is distinct from high-frequency or day-trading algorithms. Designed to serve as the execution logic for automated trading bots, trading robots, or autonomous trading agents, the system automates position sizing, trade management, and rule-based exits without requiring discretionary human intervention.
2. Trading rhythm and position duration
Although the strategy functions on a 15-minute timeframe, its actual execution tempo is exceptionally selective and structured around extended position holds. Across the 81 completed trades over seven years, the algorithm logged an average trade frequency of 11.56 trades per year, or approximately 1.45 trades per active month. The temporal spacing between trade exits reflects this low-frequency design, averaging 30.30 days between closed trades with a median exit interval of 23.49 days. Position holding times further reinforce this patient profile: the average position duration is 476.93 hours (roughly 19.87 calendar days), while the median holding duration stands at 336.25 hours (exactly 14.01 calendar days). This demonstrates that the algorithm utilizes 15-minute data not to engage in rapid intraday scalping, but to pinpoint optimal execution points within multi-week trend movements. Positions routinely remain open across multiple weekly sessions, absorbing short-term market noise in pursuit of substantial swing moves.
3. Quality of historical results
The historical return profile exhibits strong statistical consistency, marked by an 87.65% win rate (71 winning trades against 10 losing trades) and a overall profit factor of 2.50. The average return across all 81 completed trades stands at +4.05%, while the median trade yield is +2.97%, illustrating that baseline trades routinely capture positive multi-percent price moves. The single best historical trade achieved a gain of +20.31%, while the worst trade resulted in a loss of -9.43%. A crucial measure of strategy health is profit concentration: the three largest winning trades account for only 14.28% of the gross cumulative profit. This remarkably low concentration percentage proves that the strategy's total return was not artificially inflated by a handful of lucky windfall trades. Instead, historical performance is built upon a broad, repeatable foundation of modest, persistent gains generated by an high hit rate.
4. Risk, drawdown and losing behavior
Risk containment is one of the defining statistical characteristics of this strategy. Over seven years of backtested execution, the maximum peak-to-trough drawdown reached exactly 9.43%. Remarkably, this maximum drawdown metric is virtually identical to the strategy's single worst trade loss of -9.43%, suggesting that equity drawdowns were primarily driven by single trade exits rather than compounding cascades of consecutive losses. This interpretation is directly supported by the streak statistics: while the strategy recorded a maximum consecutive winning streak of 16 trades, its longest losing streak was capped at just 2 trades. With 71 wins out of 81 decisions and tight trade-level loss limits, the historical equity curve exhibits smooth, disciplined upward progression with minimal extended drawdowns. For automated trading agents managing capital allocations, this low-drawdown profile offers significant psychological and mathematical advantages.
5. Behavior through time and yearly stability
An examination of the yearly breakdown demonstrates consistent positive output across every single calendar year in the seven-year history dataset. In 2020, the strategy recorded 12 trades, all 12 of which were profitable (100% win rate), accumulating +87.65% in trade returns. Performance remained solid in 2021 with 10 trades (80.00% win rate, +51.15% sum). In 2022, trading activity expanded to 14 trades with 13 wins (92.86% win rate, +44.03% sum). The calendar year 2023 saw a lighter trade volume of 9 trades (77.78% win rate, +20.68% sum), representing the lowest annual sum in the series. Activity rebounded strongly in 2024, generating 12 wins across 12 trades (100% win rate, +62.26% sum). Steady performance continued into 2025 (13 trades, 84.62% win rate, +33.10% sum) and 2026 (11 trades, 72.73% win rate, +29.11% sum). The strategy consistently produced between 9 and 14 trades per year, showing that signal generation remained stable across varying macro regimes.
6. Recent period since 2024-06-01 versus full history
The statistical sample includes substantial recent evidence covering closed trades with sell timestamps on or after June 1, 2024. During this recent window, the strategy completed 31 trades, accumulating a combined closed-trade return of +107.13%. These 31 recent trades represent more than 38% of the entire 81-trade historical sample, indicating a higher concentration of completed cycles in recent market conditions. Comparing recent behavior to full-history metrics reveals that the strategy has maintained its trade quality and edge without degradation. Average gains per trade in the recent period (~3.46% sum per trade) remain well-aligned with the full-history median trade yield of +2.97%. The presence of 31 completed transactions since mid-2024 validates that the underlying trading model continues to find actionable setups in modern equity environments when deployed via automated trading bots.
7. Strengths and limitations
The primary strength of this strategy lies in its outstanding risk-adjusted efficiency, highlighted by an 87.65% win rate, a low 9.43% maximum drawdown, and a balanced profit distribution where no single trade dominates total return. Furthermore, the strategy's multi-week holding duration (median 14 days) insulates it from short-term microstructural noise and high turnover overhead. Conversely, the principal limitation is the strategy's low trade frequency (11.56 trades per year). Traders or deployment pipelines expecting high signal volume may find the long idle periods between trade exits challenging. Additionally, because individual positions remain open for weeks at a time, open equity is exposed to overnight market gaps and macro corporate announcements. Finally, while historical performance across 81 closed trades is robust, past backtested results remain historical simulations and cannot guarantee future market returns.
8. DevioLab analytical conclusion
Ranking #1 for AON with a DevioLab Score of 85.14, this algorithmic trading model presents a highly effective archetype for systematic equity swing trading. By combining a granular 15-minute signal evaluation timeframe with a patient, multi-week position holding strategy, the algorithm achieves an exceptional balance between gain capturing and drawdown suppression. A cumulative historical return of +1849.58% against a maximum historical drawdown of only 9.43% yields a superior return-to-risk ratio. With an 87.65% win rate, a profit factor of 2.50, and 31 successful trades completed in the recent post-June 2024 sample, the empirical evidence confirms structural efficacy. For quantitative developers seeking decision logic for automated trading robots or trading agents on AON, this strategy provides an exceptionally stable, low-turnover foundation.
9. Data scope and methodology
All analytical conclusions in this report are derived strictly from the supplied backtested trading statistics for ticker AON on a 15-minute bar interval between September 16, 2019, and September 17, 2026. Metrics describe closed simulated trades evaluated on a single-position basis without compounding account leverage or discretionary overrides. History start dates refer exclusively to the beginning of this backtest dataset and do not represent asset launch or listing dates. Performance percentages represent closed trade outputs and do not represent exact broker account equity or guaranteed future performance. Trading bot, trading robot, and trading agent references describe automated implementation contexts for the underlying quantitative logic.