AWK · American Water Works Company, Inc.
Stock Universe · global stock marketAWK Quantitative Strategy Analysis: Low-Frequency Swing Model Achieving 92.86% Historical Win Rate and 6.88 Profit Factor
This analytical report evaluates the historical performance statistics of the top-ranked algorithmic trading strategy for AWK stock on the 15-minute chart interval. Over a 6.84-year evaluation dataset from September 2019 to July 2026, the strategy recorded a cumulative backtested return of +838.61% across 28 completed trade cycles. Driven by an exceptional 92.86% win rate (26 winning trades against 2 losses) and a profit factor of 6.88, the algorithm operates on a highly selective entry model that averages 4.10 trades per year. Position durations reflect a multi-week swing framework with an average holding time of 838.91 hours (approximately 35 days). Furthermore, the strategy demonstrated strong downside containment, capping its historical maximum drawdown at 3.57%. This analysis details the structural trade mechanics, yearly stability, recent performance metrics, and inherent trade-offs for implementation in automated trading bots and algorithmic trading systems.
Analysis as of 21.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic model designated AWK 0 +838.61% 1TRAD-QHB8 occupies the rank 1 position in DevioLab analysis for AWK stock, earning a DevioLab quantitative score of 88.25. Classified as a core strategy based on independent selection criteria, the model operates using 15-minute chart price data over an extensive testing window spanning 6.84 years, from September 16, 2019, through July 17, 2026. Across this historical span, the algorithm completed 28 closed trades, generating a total cumulative gain of +838.61% and an annualized equivalent figure of 96.15%. Out of the 28 completed cycles, 26 trades closed with net profits while only 2 resulted in realized losses, establishing a baseline historical win rate of 92.86%. Rather than generating frequent signals, the system focuses on high-conviction structural setups. This low-frequency logic is designed to serve as decision rules for automated trading bots, trading robots, or quantitative trading agents, allowing systematic execution engines to capture major multi-week price trends in equity markets without subjecting the account to continuous market friction.
Trading rhythm and position duration
An examination of execution timing reveals a patient, low-turnover trading methodology. The strategy executes an average of 4.10 trades per year, which translates to approximately 1 completed trade per active month. Despite deriving its signal mechanics from a granular 15-minute timeframe, the strategy does not engage in intraday scalping or rapid turnover. The historical average holding duration stands at 838.91 hours (roughly 34.95 days), while the median holding duration is 503.88 hours (approximately 20.99 days). This gap between mean and median holding times indicates that while many positions resolve within three weeks, a subset of trades remains open for substantially longer periods to capture sustained trend continuations. Furthermore, the average time between trade exits is 86.70 days, with a median spacing of 92 days. The strategy typically remains out of the market or patiently holds single positions across several calendar months before triggering an exit, allowing automated trading agents to maintain low execution overhead while executing deliberate swing or position moves.
Quality of historical results
The strategy demonstrates robust statistical efficiency across its 28 historical trades, achieving a overall profit factor of 6.88. The average trade yield across all closed positions is +8.97%, compared to a median trade profit of +6.28%. The presence of a positive gap between the mean and median trade return highlights a favorable right-skewed payoff distribution, where larger winning trades lift the overall mathematical expectation. The single best trade in the dataset yielded a return of +41.25%, while the worst recorded trade lost -3.57%. Examining profit concentration reveals that the top three winning trades collectively generated 39.99% of the strategy gross profit. While this indicates a moderate reliance on key outlier gains, the remaining 23 winning trades contributed the majority 60.01% of gross profits. Coupled with a 92.86% win rate, the historical statistical return profile is supported by a consistently high baseline hit rate rather than being dependent entirely on a single fortunate market spike.
Risk, drawdown and losing behavior
Risk parameters throughout the 6.84-year historical backtest remained notably contained. The maximum portfolio drawdown recorded across the entire duration was 3.57%, matching the magnitude of the single worst trade (-3.57%). This direct alignment between maximum drawdown and worst trade loss suggests that equity declines were primarily driven by individual trade exits rather than compounding losses across consecutive drawdowns. The longest winning streak spanned 15 consecutive positive trades, whereas the longest losing streak was capped at just 1 single losing trade. In total, only 2 losing trades occurred out of 28 total attempts over nearly seven years. For quantitative trading models operating automated strategies, this historical drawdown profile demonstrates exceptional risk preservation, minimizing equity curve volatility and operational stress during execution cycles.
Behavior through time and yearly stability
Evaluating historical data on a calendar-year basis shows remarkable temporal consistency across differing market conditions. In 2020, the strategy completed 4 trades, achieving a 100% win rate and a combined return sum of +108.26%. In 2021, 4 trades produced +21.37% with a 100% win rate. In 2022, 3 trades yielded +29.06% with a 100% win rate. In 2023, 3 trades recorded +18.27% with a 100% win rate. In 2024, the strategy logged 4 trades (3 wins, 1 loss) for a net sum of +6.90% and a 75% win rate. Performance re-accelerated in 2025, with 6 trades (5 wins, 1 loss) generating +40.25% at an 83.33% win rate. Finally, during the partial year of 2026 up to July, 4 trades delivered +26.96% with a 100% win rate. The strategy achieved positive aggregate returns in all 7 calendar years evaluated, recording a 100% win rate in 5 of those years. This multi-year resilience indicates that the underlying logic adapts well across fluctuating volatility regimes without experiencing extended multi-year performance slumps.
Recent period since 2024-06-01 versus full history
Analyzing performance from June 1, 2024, to July 17, 2026, provides a focused perspective on recent execution dynamics. During this recent sub-period, the strategy closed 12 completed trades, yielding a cumulative return sum of +73.96%. Notably, these 12 recent trades represent nearly 43% of all 28 historical trades recorded since September 2019. This concentration of trades in the recent window shows an increase in signal completion frequency compared to the earlier years of the dataset. Despite the higher deal flow, signal quality remained elevated, contributing +73.96% out of the total lifetime return of +838.61%. The consistency between historical metrics and recent results confirms that the strategy systematic rules remained functionally effective in capturing AWK equity price movements during recent market conditions.
Strengths and limitations
The primary strength of this quantitative model lies in its rare combination of an exceptional 92.86% win rate, a strong 6.88 profit factor, and a tight maximum drawdown of 3.57%. Its low trading frequency (4.10 trades per year) protects capital from over-trading and minimizes exposure to execution friction. However, key analytical limitations must be acknowledged. First, the total trade sample size is compact, consisting of only 28 completed trades over 6.84 years. While statistically powerful in terms of consistency, small sample sizes inherently carry higher parameter sensitivity than high-frequency models with thousands of execution logs. Additionally, long holding periods averaging 838.91 hours mean that automated trading robots utilizing this strategy must tolerate extended multi-week holding periods and prolonged inactive intervals where no active trading signals are generated.
DevioLab analytical conclusion
The AWK 0 +838.61% 1TRAD-QHB8 strategy earns its top DevioLab ranking and 88.25 quantitative score through its high historical accuracy and strict risk containment. By combining a 92.86% win rate with a 6.88 profit factor and a maximum historical drawdown of just 3.57%, the algorithm demonstrates structural stability across changing market phases from 2019 to 2026. The strategy is well suited for deployment within systematic automated trading systems, trading bots, and algorithmic agents optimized for patient, low-frequency swing allocation in equity markets. Investors and quantitative engineers evaluating this model should weigh its outstanding historical risk-adjusted return against its low trade frequency and multi-week holding duration.
Data scope and methodology
All metrics presented in this report are calculated directly from historical simulated backtest results for AWK stock on the 15-minute timeframe, covering the active period from September 16, 2019, to July 17, 2026. Trade metrics, win rates, drawdowns, and holding durations are calculated strictly from closed positions. Recent sub-period figures reflect trades with exit timestamps on or after June 1, 2024. These statistics represent historical algorithmic backtest analysis for quantitative research purposes only and do not constitute investment advice, financial recommendations, or guarantees of future live execution returns.