AOS · A. O. Smith Corporation
Stock Universe · globaler AktienmarktAOS 15-Minute Algorithmic Strategy Analysis: High Win-Rate Efficiency and Controlled Risk in Equity Automation
This quantitative evaluation examines the top-ranked algorithmic trading strategy for AOS on the 15-minute timeframe, recorded in DevioLab systems with a performance score of 84.63. Across a seven-year historical dataset spanning from September 2019 to September 2026, the strategy generated a cumulative closed-trade return of +2495.72 percent across 115 completed trades, yielding an annualized return of 83.87 percent and an extraordinary profit factor of 5.0. Characterized by an 80.87 percent win rate, a maximum drawdown contained at 13.68 percent, and a low profit concentration where the top three winning trades account for only 13.56 percent of gross profit, the model demonstrates remarkable statistical balance. Designed as decision logic for automated trading bots, trading robots, and quantitative trading agents, this strategy pairs granular 15-minute price evaluation with extended multi-day position holding times to capture broad directional trends while minimizing trade turnover.
Analyse vom 17.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy Profile
The quantitative strategy designated for AOS operates on a 15-minute price evaluation interval, functioning as a primary core strategy within the DevioLab database where it holds the rank of 1 for this ticker with an analytical score of 84.63. Over a historical evaluation period of seven full years running from September 16, 2019, to September 17, 2026, the algorithmic model completed 115 trades. Historical backtesting records a cumulative return sum of +2495.72 percent, translating to an annualized performance metric of 83.87 percent. The overarching statistical footprint is defined by a profit factor of 5.0 and a win rate of 80.87 percent, underpinned by 93 winning trades against 22 losing trades. Operating as structural logic suitable for automated trading bots and algorithmic trading agents, the strategy focuses on capturing durable medium-term moves in AOS equity prices while avoiding high-frequency churn.
Trading Rhythm and Position Duration
Despite utilizing a 15-minute execution timeframe to observe market structure, the strategy operates as a patient swing or position-style model rather than a high-frequency trading bot. The average position holding time spans 289.30 hours, or approximately 12.05 days, while the median holding duration sits at 190.75 hours, or roughly 7.95 days. This substantial gap between intra-day signal monitoring and multi-day position holding indicates that 15-minute candlestick data is used for precise entry and exit timing rather than rapid scalping. On average, the algorithm completes 16.42 trades per year, which corresponds to approximately 1.64 trades per active month. Furthermore, the average spacing between trade exits is 22.00 days, with a median exit interval of 18.42 days. For automated trading robots deployed on this asset, this structural rhythm means long periods of position holding or inactive observation, drastically reducing trade exposure and market noise.
Quality of Historical Results
The performance profile exhibits exceptional consistency across individual trades. The average trade yield stands at +3.20 percent, which closely matches the median trade return of +3.28 percent. This symmetry between mean and median outcomes confirms that historical profitability is driven by repeatable, standard trades rather than being skewed by a few extreme leverage-like spikes. The strategy recorded a maximum single winning trade of +24.04 percent and a maximum losing trade of -11.85 percent. Furthermore, an inspection of profit distribution reveals that the three largest winning trades account for just 13.56 percent of total gross profit. In quantitative analysis, a low profit concentration metric demonstrates that structural health is distributed broadly across the 93 winning trades rather than depending on a handful of lucky outlier events. This broad-based payoff efficiency directly underpins the strategy's elevated profit factor of 5.0.
Risk, Drawdown and Losing Behavior
Historical risk metrics for this AOS strategy reflect strong capital protection properties. The maximum peak-to-trough equity drawdown experienced across the seven-year history was held to 13.68 percent. This controlled drawdown profile is particularly notable when contrasted with the overall historical profit sum of +2495.72 percent, yielding a highly favorable return-to-drawdown ratio. The strategy's streak metrics reinforce this stability: the longest consecutive winning streak reached 27 trades, whereas the longest consecutive losing streak was capped at just 3 trades. Because the worst individual trade loss was contained at -11.85 percent and losing streaks remained tightly clustered in short windows, the automated trading logic prevented severe compounding capital erosion during adverse market conditions.
Behavior Through Time and Yearly Stability
A year-by-year examination of the statistical dataset reveals consistent positive historical returns across every calendar year from 2019 through 2026. In 2019, across a partial operating window, the strategy executed 2 trades with a 50.00 percent win rate and a +0.91 percent return. Activity expanded significantly in 2020, producing 19 trades, an 89.47 percent win rate, and a net sum of +102.37 percent. In 2021, the model achieved its highest annual win rate of 95.24 percent across 21 trades, yielding +83.59 percent. During the challenging broader equity market conditions of 2022, performance remained net positive with 16 trades, a 56.25 percent win rate, and a +20.50 percent return. Subsequent years sustained strong momentum: 2023 generated +59.59 percent across 14 trades (78.57 percent win rate); 2024 produced +35.52 percent across 15 trades (86.67 percent win rate); 2025 logged +27.91 percent across 16 trades (75.00 percent win rate); and 2026 recorded +37.60 percent across 12 trades (83.33 percent win rate). The year-over-year persistence confirms that trade returns are evenly distributed throughout the multi-year dataset.
Recent Period Since 2024-06-01 Versus Full History
Analyzing recent performance from June 1, 2024, to September 17, 2026, provides a focused perspective on modern market efficacy. Within this window, the strategy completed 33 closed trades, accumulating a combined trade return of +71.15 percent. This active period represents nearly 28.7 percent of all trades recorded in the seven-year backtest. The high trade count during this window demonstrates sustained execution quality in modern equity conditions. Comparing the recent average yield per trade against full-history baseline averages shows that the strategy's core edge has remained functional and stable without exhibiting degradation or sudden divergence from its multi-year operational characteristics.
Strengths and Limitations
The primary statistical strengths of this quantitative model lie in its exceptionally high win rate of 80.87 percent, an outstanding profit factor of 5.0, a contained maximum drawdown of 13.68 percent, and a remarkably low profit concentration where the top three trades contribute only 13.56 percent of total gains. These factors demonstrate a structural trading edge that relies on consistent, medium-sized wins rather than high-risk, asymmetric bets. Conversely, the primary limitation of the strategy is its low total trade frequency. With only 115 completed trades over seven years (an average of 16.42 trades per year and 22.00 days between exits), the strategy requires considerable patience. Developers and traders utilizing this logic in automated trading agents must accommodate multi-week holding periods and extended inter-trade lull periods without attempting to force trade frequency.
DevioLab Analytical Conclusion
The DevioLab score of 84.63 and top rank for the AOS symbol reflect a robust, highly optimized quantitative trading model. By blending short-interval 15-minute execution signals with disciplined multi-day trade management averaging 289.30 hours per position, the strategy successfully filters out market noise while capturing substantive directional swings. Its ability to maintain positive net returns across every single calendar year, combined with a brief maximum losing streak of just 3 trades and a low drawdown of 13.68 percent, marks it as an exemplary candidate for automated trading bot architecture. For quantitative systems operating on AOS equity data, this strategy offers a proven statistical blueprint that balances strong capital growth with rigorous downside confinement.
Data Scope and Methodology
All figures, metrics, and analytical observations presented in this report are calculated directly from historical backtested closed-trade datasets for AOS on a 15-minute execution interval between September 16, 2019, and September 17, 2026. Trade percentages represent simulated performance metrics of closed positions without leverage, commission overhead, slippage models, or liquidity friction accounted for. Historical strategy performance, backtested results, and quantitative rankings are provided strictly for educational and analytical research purposes and do not constitute financial advice or guaranteed future trading performance.