ECL · Ecolab Inc.
Stock Universe · global stock marketECL · ECL Algorithmic Strategy Analysis: Evaluating a High-Win-Rate 15-Minute System for Automated Trading
An in-depth quantitative examination of the top-ranked algorithmic trading strategy for ECL on the 15-minute timeframe. Across nearly seven years of historical evaluation spanning 120 completed trades, the strategy achieved an 82.50% win rate, a 2.50 profit factor, and a cumulative closed-trade profit sum of +1236.78%. Driven by longer positional holding periods averaging over 300 hours despite operating on a 15-minute execution chart, this system presents a distinctive structural profile suitable for automated trading bots and trading robots seeking lower execution frequency with high statistical hit rates.
Analysis as of 23.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy designated as ECL 0 +1236.78% 1TRAD-KSY2 represents the premier core system for ECL stock in the DevioLab quantitative database, holding the rank of number one for this asset with a DevioLab score of 79.67. Evaluated over a historical sample extending from September 16, 2019, to September 11, 2026—a duration of approximately 6.99 years—the strategy operates on a 15-minute candle interval. Across this entire sample, the algorithm executed 120 completed trades, generating a total cumulative profit sum across closed trades of +1236.78% and an annualized rate of 136.66%. The foundation of this historical output rests on an exceptional win rate of 82.50%, with 99 out of 120 trades closing in positive territory against 21 losing trades. Designed as robust decision logic for execution via automated trading bots or trading agents, the strategy demonstrates how high-precision entry filtering on lower-timeframe charts can be paired with extended position management to capture broad multi-day trend moves while maintaining high statistical efficiency.
Trading rhythm and position duration
Despite utilizing a 15-minute price candle for signal generation, the strategy exhibits a distinctly patient trading rhythm that separates it from standard intraday systems. Over the 6.99-year backtest horizon, the algorithm generated an average of 17.17 trades per year, which translates to approximately 1.79 trades per active month. Position holding times reflect a multi-day to multi-week perspective rather than quick intraday turnover. The average holding duration per position stands at 301.47 hours (roughly 12.56 calendar days), while the median holding time is 193.00 hours (about 8.04 days). This divergence between mean and median holding times indicates that while most trades resolve within eight to ten days, select positions extend significantly longer to capture sustained directional trends. Furthermore, the average interval between position exits is 21.37 days, with a median of 16.99 days. For deployment in automated trading robots, this structural cadence implies low operational overhead and minimal exposure to market noise, allowing automated agents to operate with high structural selectivity.
Quality of historical results
A critical feature of this strategy is the structural quality and symmetry of its return distribution. The strategy achieves a profit factor of 2.50, meaning gross gains exceeded gross losses by two and a half times. The average trade yield stands at +2.43%, closely mirrored by a median trade yield of +2.83%. The close proximity of the median to the mean highlights a remarkably stable distribution where typical trades regularly hit the expected performance baseline, rather than relying on extreme statistical outliers. The best single trade recorded a gain of +22.51%, while the worst trade resulted in a loss of -18.37%. Crucially, the top three winning trades collectively account for only 12.48% of the strategy's total gross profit. This exceptionally low profit concentration demonstrates that the strategy's total cumulative gain of +1236.78% is broad-based and repeatable, derived from a high density of consistent winning trades across the dataset rather than a handful of lucky windfall events.
Risk, drawdown and losing behavior
Every quantitative system involves risk trade-offs, and evaluating losing periods provides vital context for automated trade execution. The strategy experienced a maximum historical drawdown of 22.21%. When contextualized against the total cumulative gain of +1236.78% and a profit factor of 2.50, this drawdown figure reflects manageable historical equity retracement relative to overall growth. The algorithm's losing behavior is characterized by brief, controlled clusters. The longest winning streak reached 14 consecutive trades, whereas the longest losing streak was restricted to just 3 consecutive trades. Out of 120 total trade events, only 21 yielded negative returns. However, the worst single loss of -18.37% indicates that when market conditions diverge sharply from the strategy's rules, individual drawdowns can approach the magnitude of the full strategy peak-to-trough dECLine. Operators utilizing trading bots must recognize that while losing sequences are historically rare, individual stop events require adequate risk buffer to withstand periodic positional adjustments.
Behavior through time and yearly stability
Analyzing the annual breakdown from 2019 through 2026 demonstrates consistent equity growth across varied market regimes. In 2019, during a partial initial sample, the strategy logged 3 trades with a 100% win rate and a +6.14% return. In 2020, trade frequency expanded to 16 trades, yielding 13 wins (81.25% win rate) and a peak annual total of +78.03%. Performance remained robust in 2021, generating +48.96% across 21 trades with a 90.48% win rate. The year 2022 presented the most challenging conditions in the dataset, resulting in 17 trades with 9 wins and 8 losses (52.94% win rate) and a modest net return of +3.19%. Following this period of market friction, the strategy recovered strongly in 2023 with +39.84% across 14 trades (92.86% win rate). Subsequent years maintained high consistency: 2024 produced +39.82% over 12 trades (91.67% win rate), 2025 delivered +36.50% across 19 trades (73.68% win rate), and 2026 recorded +39.07% over 18 trades (94.44% win rate). With positive closed returns in every single calendar year, the strategy showcases durable historical adaptability.
Recent period since 2024-06-01 versus full history
Evaluating recent activity provides valuable perspective on whether an algorithmic system maintains its structural edge in contemporary market conditions. In the window from June 1, 2024, to September 11, 2026, the strategy closed 45 trades, generating a cumulative return sum of +91.62%. This recent sample accounts for 37.5% of all total historical trades recorded since September 2019, confirming a substantial level of recent strategy activity. Comparing recent execution to the full history shows consistent alignment: the annualized return during this recent window remains strong, matching the high win-rate environment observed across full historical operations. Because the recent trade count is substantial rather than sparse, statistical conclusions drawn from recent history reinforce the persistent effectiveness of the entry and exit parameters on ECL stock.
Strengths and limitations
The primary structural strength of this strategy lies in its high win rate of 82.50% combined with a low profit concentration of 12.48% across its top three winners, ensuring that strategy returns are well-distributed across many completed trades. Additionally, an average holding time of over 300 hours paired with low trade frequency (1.79 trades per active month) minimizes execution stress and makes the logic highly suited for automated trading agents. On the limitation side, the strategy's maximum historical drawdown of 22.21% and worst single trade loss of -18.37% demonstrate that positions can incur notable unrealized or realized losses during sharp counter-trend movements. Furthermore, the performance dip in 2022, where win rate dropped to 52.94%, highlights that prolonged trendless or volatile asset environments can temporarily suppress profitability.
DevioLab analytical conclusion
With a DevioLab score of 79.67 and the top ranking for ECL stock, this 15-minute core strategy presents a compelling quantitative profile characterized by high win frequency, low profit concentration, and multi-year consistency. By combining a fine-grained 15-minute timeframe for precision monitoring with multi-day position holding times, the system effectively bridges short-term execution control with macro trend capture. For quantitative traders and automated trading system architects, the empirical record demonstrates robust multi-year viability, provided capital management parameters are configured to accommodate maximum drawdowns on the order of 22%.
Data scope and methodology
The statistics in this report are derived from historical backtesting of closed trade events for ECL on the 15-minute timeframe between September 16, 2019, and September 11, 2026. All reported percentage gains, trade counts, win rates, drawdown figures, and holding durations reflect closed-trade historical simulations. Performance measurements do not account for external transaction costs, slippage, borrowing costs, or live execution latency, and they do not reflect actual brokerage account returns. Historical backtested performance provides empirical insight into strategy behavior but does not guarantee future results.