AES · The AES Corporation
Stock Universe · global stock marketAES Quantitative Strategy Analysis: 96% Win Rate and Controlled Risk in Low-Frequency Position Engine
This analytical research report evaluates the top-ranked algorithmic trading strategy for AES on DevioLab.com, which achieves a DevioLab Score of 87.99 over a 6.39-year dataset running from September 2019 to February 2026. Designed for implementation in automated trading bots, trading robots, and algorithmic decision agents, the strategy operates on a 15-minute price timeframe while executing a highly selective position-holding model. Across 25 completed historical trades, the algorithm generated a cumulative closed-trade return of 5700.53% with an annualized return of 195.12%, maintaining a 96% win rate, a profit factor of 6.88, and a maximum drawdown restricted to 3.07%. This study explores the structural relationships among holding duration, trade frequency, return distribution, and drawdown risk.
Analysis as of 13.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The evaluated algorithmic model represents the primary core strategy for AES stock on DevioLab.com, earning the number one rank for this asset with a DevioLab Score of 87.99. Evaluated across a dataset spanning September 16, 2019, through February 3, 2026—a duration of approximately 6.39 years—the strategy serves as an automated decision engine for trading bots and execution agents operating in equity markets. Rather than generating frequent signals, the system focuses on high-expectancy setups. Over the entire backtested period, the logic executed only 25 completed trades, securing 24 winning positions and 1 loss, which corresponds to a 96% win rate. Cumulative historical closed-trade performance reached 5700.53%, translating to an annualized return figure of 195.12%. The profile is defined by extreme selectivity, high payoff per trade, and minimal realized historical drawdown.
Trading rhythm and position duration
Although the underlying data structure relies on 15-minute candle intervals, the operational behavior of this strategy is firmly characterized by medium- to long-term position holding rather than intra-day activity. The algorithm completed 25 trades over 6.39 years, yielding an average trade frequency of 3.91 trades per year, or 1.09 trades per active month. Positions are held for extended durations, with an average holding time of 874.48 hours (approximately 36.4 days) and a median holding time of 601 hours (25 days). The divergence between the average and median holding durations indicates that while a typical trade remains open for roughly three to four weeks, occasional positions are held for multiple months to capture extended trend movements. Spacing between signal completions is similarly broad, averaging 88.59 days between exits with a median of 78.51 days. This low-velocity execution cadence demonstrates that 15-minute price data is utilized primarily for refined entry and exit execution timing, rather than rapid position flipping.
Quality of historical results
The strategy demonstrates robust statistical quality across its closed-trade distribution. The historical win rate of 96% is paired with an average trade return of +19.36% and a median trade return of +16.04%. The close proximity of the median to the average indicates that performance is not driven by a single hyper-inflated gain, but rather by a consistent series of double-digit percentage expansion trades. The single best historical trade delivered +103.72%, while the single worst trade was contained at -3.07%. With a total profit factor of 6.88, the gross profit heavily outweighs historical gross losses. Analyzing profit concentration reveals that the top three winning trades accounted for 39.67% of total gross profits. While this demonstrates that major trend expansions contribute meaningfully to total profitability, the remaining 60.33% of gross gains are distributed across the other 21 winning trades, establishing a well-supported payoff structure rather than an outlier-dependent return distribution.
Risk, drawdown and losing behavior
Risk containment is one of the strategy's most prominent statistical features. The maximum backtested peak-to-trough drawdown reached 3.07%, matching exactly the loss incurred on the strategy's single losing trade (-3.07%). Over the 6.39-year testing window, the algorithm recorded a longest winning streak of 14 consecutive profitable positions, while its longest losing streak was limited to 1 trade. This minimal drawdown trajectory is structurally linked to the combination of a 96% hit rate and tight loss realization when a trade invalidates. Because losing trades were historically both rare and small, equity curves generated by this strategy show near-monotonically increasing trajectories. However, traders evaluating automated trading agents based on this decision engine must recognize that holding positions for an average of 36.4 days exposes open capital to broad equity market volatility, overnight gaps, and macroeconomic tail risks that may not be fully reflected in closed-trade stop metrics during stable historical periods.
Behavior through time and yearly stability
An examination of the yearly breakdown reveals consistent profitability across every full calendar year in the evaluation window. In 2020, the strategy recorded 3 trades, all winning, yielding a cumulative return of +77.47%. In 2021, 4 trades produced 4 wins and a cumulative +138.22%. During the challenging equity market conditions of 2022, the strategy maintained a 100% win rate across 4 trades, generating +70.02%. The year 2023 yielded +37.84% across 3 winning trades. In 2024, trade activity increased to 6 completed trades, yielding 5 wins and 1 loss (an 83.33% win rate) for a combined annual return of +71.25%. In 2025, 4 completed trades produced 4 wins for +73.05%. Finally, in the early portion of 2026 up to February 3, 1 trade completed successfully for +16.04%. The strategy maintained annual returns ranging between +37.84% and +138.22% across six consecutive full years, proving that its performance is stable across different market regimes.
Recent period since 2024-06-01 versus full history
Evaluating recent strategy behavior since June 1, 2024, provides vital insight into whether the model continues to function effectively under current market conditions. In this recent window, the strategy completed 8 trades, generating a cumulative return sum of +119.62%. Comparing this to the long-term history shows a slight acceleration in trade realization: nearly one-third of the strategy's total 25 historical trades occurred within the last 20 months. Out of these 8 recent trades, 7 were profitable and 1 was the single historical loss of -3.07%. The average performance per trade in the recent period remains fully aligned with full-history standards, confirming that the quantitative rules powering this trading robot continue to capture actionable asset swings without experiencing efficiency degradation or structural regime failure.
Strengths and limitations
The primary strengths of this strategy include its exceptional win rate (96%), a profit factor of 6.88, a minimal maximum drawdown of 3.07%, and remarkable multi-year return consistency across diverse market environments. The high median trade (+16.04%) confirms strong per-trade expectancy. Conversely, the strategy's main statistical limitation lies in its small sample size. Executing only 25 total trades over 6.39 years provides a limited dataset for statistical stress testing. Additionally, average holding periods of over 36 days require trading bots or execution agents to maintain patience through market fluctuations, leading to long periods of inactivity where capital remains tied up or idle between setups.
DevioLab analytical conclusion
Ranking first for AES with a DevioLab Score of 87.99, this algorithmic strategy offers a compelling template for automated trading systems that prioritize high precision over trading frequency. By combining 15-minute entry precision with multi-week position management, the strategy successfully avoids over-trading while capturing large trends in AES stock. The historical performance profile—marked by +5700.53% total returns and a tiny 3.07% peak drawdown—demonstrates effective risk-reward control. Quantitative developers deploying this logic into automated trading agents must balance these favorable metrics against the small sample size of 25 trades, ensuring proper capital allocation and risk management protocols for future live execution.
Data scope and methodology
This analysis is based on historical backtested strategy performance for AES on 15-minute candle data between September 16, 2019, and February 3, 2026. All statistics, including win rates, drawdowns, trade durations, and yearly breakdowns, represent simulated closed trades generated by the strategy logic. Performance figures do not reflect live execution slippage, broker fees, or market impact, and historical metrics do not guarantee future live trading performance.