EIX · Edison International
Stock Universe · marché actions mondialEIX Quantitative Analysis: High-Precision Positional Dynamics and Low-Frequency Edge in Algorithmic Trading
This quantitative evaluation analyzes a top-ranked algorithmic trading strategy for EIX across a 6.96-year historical evaluation window from September 2019 to September 2026. Evaluated on a 15-minute execution timeframe, the strategy demonstrates a distinctive low-frequency, long-duration operational profile, completing just 49 total trades with an average holding time of 864.80 hours (approximately 36 days). The backtest reveals an exceptional historical win rate of 83.67%, a profit factor of 3.75, and a cumulative return of +939.84%, accompanied by a maximum drawdown of 18.97%. Notably, the strategy displays symmetrical gain distribution, with both average and median trade returns aligning precisely at +5.29%, while the top three winning trades account for only 21.19% of total gross profit. With a DevioLab score of 81.68 and ranking first for its symbol, this model illustrates how low-frequency automated decision rules operating on intraday price data can effectively harvest medium-term trends while controlling drawdown risk.
Analyse au 24.10.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
1. Strategy profile
The evaluated algorithmic model, designated for EIX within stock market environments, represents a highly selective quantitative system optimized for medium-term position holding. Designed to serve as the core decision logic for automated trading bots, trading robots, or autonomous trading agents, the strategy operates on a 15-minute price aggregation interval (15m). Despite utilizing an intraday time step for signal generation and trade management, the model exhibits a distinctly patient behavioral signature rather than high-frequency intraday churn. Over an evaluation span of approximately 6.96 years—stretching from September 16, 2019, through September 1, 2026—the system registered a total of 49 completed transactions. This yields a baseline trade frequency of 7.04 trades per year, or roughly 1.23 trades per active month. The strategy achieves a DevioLab overall score of 81.68, securing the top ranking (rank 1) among quantitative models evaluated for EIX. Over the full historical timeline, the algorithm achieved a cumulative closed-trade profit of +939.84%, translating to an annualized performance metric of 90.73%. The profile reflects an intentional trade-off: sacrificing frequent execution opportunities to isolate higher-conviction structural price movements while maintaining strict statistical criteria for entry and exit.
2. Trading rhythm and position duration
A critical structural dynamic of this strategy is the interplay between its intraday execution bar (15m) and its multi-week position duration. While signal monitoring occurs on a granular 15-minute schedule, actual position holding times are extended. The historical average holding duration stands at 864.80 hours—roughly 36 days—while the median holding duration is 478.75 hours (approximately 20 days). This positive skew between median and average holding times indicates that while a typical position spans two to three calendar weeks, certain persistent trends justify holding positions for well over a month. The rhythm between execution events further underscores this deliberate pacing: the strategy registers an average of 51.73 days between trade exits, with a median spacing of 42.11 days. Rather than engaging in short-term scalping or rapid swing trading, the decision architecture uses the 15-minute resolution to pinpoint refined entry and exit timestamps within broader macro price developments. Consequently, automated trading agents implementing this logic spend long periods sitting in established market positions, minimizing unnecessary trade turnover.
3. Quality of historical results
The historical quality metrics of the strategy highlight notable reliability and balanced profit generation across its 49 trade outcomes. Out of these 49 trades, 41 concluded as winning transactions, resulting in a win rate of 83.67%, against just 8 losing trades. The strategy achieved a Profit Factor of 3.75, meaning gross profits generated by winning positions exceeded gross losses incurred by losing trades by nearly four to one. A key statistical feature lies in the exact alignment between the average trade return of +5.29% and the median trade return of +5.29%. In quantitative backtests, significant divergence between mean and median values often flags skewed performance driven by rare, extreme outliers. Here, the identical mean and median indicate a highly consistent profit distribution. Furthermore, the top three winning trades generated 21.19% of total gross profit, confirming that historical performance was not dependent on a few isolated windfall gains. The single best trade delivered a gain of +25.07%, while the single worst trade resulted in a loss of -14.90%, showing a controlled payoff structure where average gains comfortably surpass typical adverse outcomes.
4. Risk, drawdown and losing behavior
Risk management metrics demonstrate robust downside mitigation relative to historical profit generation. Over the entire 6.96-year testing window, the maximum closed-trade peak-to-trough drawdown was constrained to 18.97%. When evaluated alongside the full-history cumulative return of +939.84%, the historical drawdown profile demonstrates strong return-to-risk characteristics. The strategy's losing behavior reveals minimal consecutive failure patterns: the longest winning streak spanned 12 consecutive profitable trades, whereas the longest losing streak reached a maximum of only 2 consecutive losses. This suggests that the algorithm's entry logic rarely remained trapped in adverse market regimes for prolonged periods. The worst single trade loss of -14.90% represents the maximum realized single-position decline, serving as a clean empirical boundary for individual trade exposure. The low occurrence of consecutive losses helped protect equity from compound drawdown spirals, allowing automated trading bots executing this script to recover capital efficiently following minor adverse movements.
5. Behavior through time and yearly stability
An examination of the yearly breakdown illustrates how the strategy's low trade frequency manifested across different market years from 2019 through 2026. Performance was consistently positive across every single calendar year, though activity levels varied significantly. In 2019 (covering a partial year from September), the strategy closed 1 trade for +7.36%. In 2020, trade frequency rose to 10 trades with a 90.00% win rate, yielding +98.13% cumulative profit. Activity contracted in 2021 (2 trades, 100% win rate, +19.39%) and 2022 (3 trades, 66.67% win rate, +7.57%), reflecting periods where fewer setup conditions were triggered. Activity surged again in 2023 (9 trades, 88.89% win rate, +44.43%), followed by 2024 (6 trades, 83.33% win rate, +40.12%), 2025 (11 trades, 72.73% win rate, +18.44%), and 2026 (7 trades, 85.71% win rate, +23.62%). Annual win rates remained above 66% across all years, demonstrating structural stability regardless of shifting macro regimes in EIX.
6. Recent period since 2024-06-01 versus full history
Analyzing performance in the recent observation window beginning June 1, 2024, provides empirical insight into current strategy behavior. Out of the 49 total completed trades across the 6.96-year dataset, 21 trades completed their exit sequence on or after June 1, 2024. These 21 recent trades generated a cumulative closed return of +69.96%. This indicates a notable acceleration in trade completion density during the final two years of the backtest compared to earlier low-volume years like 2021 and 2022. Importantly, the strategy maintained strong profitability during this recent era, delivering substantial positive returns without eroding the system's overall return-to-drawdown structure. The presence of 21 well-documented recent trade exits ensures that the strategy's high performance rating is grounded in modern price action rather than relying solely on early historical data.
7. Strengths and limitations
The primary analytical strength of this strategy lies in its high win rate (83.67%), strong profit factor (3.75%), and moderate drawdown profile (18.97%). The statistical symmetry between mean (+5.29%) and median (+5.29%) trade returns, combined with a modest 21.19% concentration in the top three winners, proves that the system generates repeatable, broad-based profits across its trade set. However, clear limitations exist. The primary constraint is sample size: 49 total trades over nearly seven years represent a small statistical dataset, which increases the potential margin of error for historical metrics. Furthermore, the lengthy holding duration (average 864.80 hours) requires high operational patience and exposes active positions to overnight gapping and macro headline risk in stock markets. Automated trading robots must be prepared for long periods of inactivity between execution signals.
8. DevioLab analytical conclusion
The DevioLab score of 81.68 and number one rank for EIX reflect a robust, highly efficient quantitative model optimized for long-duration trend capture on a 15-minute charting framework. By combining high trade accuracy with symmetrical payoff metrics and limited drawdown, the strategy offers a compelling logical framework for automated trading agents seeking low-churn exposure to EIX. The high profit factor of 3.75 and the strong recent validation since mid-2024 reinforce its operational consistency. However, prospective deployment must account for the inherent limitations of a 49-trade historical sample and the psychological requirements of managing positions held open for weeks at a time.
9. Data scope and methodology
All analytical conclusions presented in this report are strictly derived from the historical simulated performance metrics supplied for asset symbol EIX on a 15m time interval from September 16, 2019, to September 1, 2026. The reported statistics describe closed backtested trades and do not represent live account trading execution, actual brokerage order fill rates, commission structures, borrow fees, or slippage dynamics. Recent period statistics reflect all trades closed on or after June 1, 2024 UTC. Past backtested performance is inherently hypothetical and does not guarantee future financial results.