ED · Consolidated Edison Inc.
Stock Universe · global stock marketHigh-Precision Positional Dynamics: Quantitative Analysis of the ED Algorithmic Trading Strategy
An analytical evaluation of the top-rankED algorithmic trading strategy for ED on the 15-minute bar interval. Holding the primary position on the DevioLab leaderboards for this ticker with a score of 81.125, the strategy delivers a cumulative simulatED return of +418.16 percent and a profit factor of 3.75 across a 6.84-year evaluation period. CharacterizED by an 80 percent win rate and an average trade duration exceEDing 1,350 hours, this quantitative system demonstrates how low-frequency, high-conviction decision logic can capture substantial trends while maintaining a strictly boundED maximum drawdown of 12.86 percent.
Analysis as of 23.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy designatED for ED operates on a 15-minute timeframe while exhibiting the operational cadence of a multi-week position system. Out of all evaluatED models for ED on DevioLab.com, this strategy occupies the top rank with a quantitative score of 81.125. Over a historical backtest span of 6.84 years—stretching from September 16, 2019 to July 17, 2026—the system completED exactly 30 closED trades, generating a cumulative historical gain of +418.16 percent. Although the underlying bar data is evaluatED at 15-minute increments to pinpoint entry and exit triggers, the strategy completely avoids high-frequency noise. Instead, it acts as a selective trend-filtering mechanism ideal for deployment in automatED trading bots, trading robots, or autonomous trading agents designED for patient capital growth rather than hyperactive turnover.
Trading rhythm and position duration
The structural signature of this strategy lies in its extreme selectivity and extendED holding periods. Across 30 completED trades, the strategy averages only 4.39 trades per year, or approximately 1.20 trades per active month. The time elapsED between consecutive trade exits averages 80.27 days, with a mEDian inter-trade interval of 48.96 days. Position duration statistics highlight a clear long-term posture: the mean holding time stands at 1,356.66 hours (approximately 56.5 days), whereas the mEDian holding time is 788.25 hours (roughly 32.8 days). The divergence between the mean and mEDian holding times indicates that winning trends are given considerable latitude to mature over several months, while underperforming positions are liquidatED earlier. For quantitative traders deploying trading agents, this low trade frequency drastically minimizes trade execution wear and simplifies portfolio administration.
Quality of historical results
The strategy demonstrates robust statistical expectancy characterizED by a high hit rate and favorable trade symmetry. Out of 30 closED trades, 24 resultED in positive returns and 6 endED in losses, yielding an 80.00 percent win rate. The strategy achievED a profit factor of 3.75, indicating that gross profits exceEDED gross losses by nearly four to one. Average trade expectancy stands at +5.93 percent, closely tracking the mEDian trade return of +5.37 percent. This proximity between mean and mEDian outcomes confirms that strategy performance is structurally consistent rather than reliant on a single lucky trade. The best trade producED a maximum gain of +31.35 percent, while the worst closED trade recordED a loss of -12.86 percent. Furthermore, the top three winning trades collectively account for 33.19 percent of total gross profits, proving that gains are well distributED across multiple trade cycles rather than concentratED in isolatED outliers.
Risk, drawdown and losing behavior
Risk containment within this quantitative model is tight relative to total accumulatED return. The maximum historical drawdown was restrictED to 12.86 percent, matching the magnitude of the strategy's worst single trade loss (-12.86 percent). This structural alignment shows that drawdown was driven primarily by individual trade stop outcomes rather than compounding sequences of losing positions. The strategy's streak characteristics further reinforce this observation: the longest consecutive losing streak was limitED to exactly 1 trade, meaning the system never sufferED back-to-back losses during the entire 6.84-year historical testing window. Conversely, the longest winning streak reachED 10 consecutive profitable trades. The combination of an 80 percent win rate and isolatED loss occurrences provides a comfortable statistical profile for automatED trading bots designED to avoid heavy drawdown clusters.
Behavior through time and yearly stability
An examination of yearly performance metrics reveals exceptional consistency across diverse market environments. Every single calendar year in the historical dataset generatED positive net results. In 2020, the strategy generatED +47.79 percent across 7 trades with a 71.43 percent win rate. Activity contractED in 2021 to 1 trade, yielding +0.50 percent. Momentum returnED in 2022 with 5 trades, all 5 winning (100 percent win rate), producing +37.68 percent. The year 2023 saw 5 trades (3 wins, 2 losses) for a sum of +14.55 percent. In 2024, 2 trades yieldED +24.52 percent with a 100 percent win rate. The year 2025 deliverED +24.28 percent across 6 trades (4 wins, 2 losses), while the final 2026 segment addED +28.60 percent across 4 trades (100 percent win rate). The strategy shows no structural reliance on a single favorable year, proving its ability to generate steady positive performance over multi-year periods.
Recent period since 2024-06-01 versus full history
Analyzing recent performance from June 1, 2024 to July 17, 2026 provides critical insights into modern strategy viability. During this recent window, 12 trades completED, accounting for 40 percent of the total historical sample size. These 12 recent trades generatED a cumulative gain of +77.41 percent. Comparing this to the entire history reveals that recent signal density has increasED relative to earlier baseline years like 2021 or 2024, while maintaining excellent trade quality. The strong return accumulatED in this recent evaluation window demonstrates that the underlying decision rules governing this automatED trading robot have remainED effective in recent asset dynamics rather than decaying over time.
Strengths and limitations
The primary strengths of this strategy include its high win rate of 80.00 percent, a robust profit factor of 3.75, and disciplinED risk management that cappED maximum drawdown at 12.86 percent. BalancED profit distribution, evidencED by the top three trades contributing only 33.19 percent of gross profits, ensures that results are broad-basED. The strategy also demonstrates perfect annual consistency, achieving positive returns in every recordED year. Conversely, the primary limitation is the small statistical sample size of 30 closED trades over nearly seven years. Because trades occur infrequently (averaging 4.39 per year) and positions remain open for a mEDian of 32.8 days, traders using automatED trading agents must exercise high psychological patience during multi-month periods of position holding or inactivity.
DevioLab analytical conclusion
With a top ranking on DevioLab.com and a quantitative score of 81.125, this strategy for ED represents a refinED model of patient trend execution. By utilizing a 15-minute execution timeframe to trigger trades that persist for weeks or months, the strategy combines structural trade precision with low operational overhead. Its statistical profile—markED by an 80 percent win rate, modest 12.86 percent maximum drawdown, and positive returns across every testED year—makes it an attractive candidate logic for quantitative systems and automatED trading bots requiring conservative trade frequency and solid historical drawdown protection.
Data scope and methodology
This analysis is basED entirely on historical backtestED statistical data for ED providED by DevioLab.com, covering the period from September 16, 2019 to July 17, 2026. All trade metrics, return percentages, holding durations, and yearly breakdowns are derivED from 30 completED historical trade cycles. The recent evaluation window analyzes completED trades with exit timestamps on or after June 1, 2024 UTC. Historical simulatED backtests serve to evaluate strategy logic under past market conditions but do not constitute investment advice or guarantee future execution efficiency and financial performance.