ALL · The Allstate Corporation
Stock Universe · marché actions mondialALL Quantitative Strategy Analysis: High-Expectancy Positional Logic for ALL Stock
This analytical report evaluates the top-ranked algorithmic trading strategy for ALL stock, operating on a 15-minute bar execution schedule over a seven-year evaluation window. Generating a cumulative backtested return of +2,963.55% across 73 completed trades, the strategy demonstrates a high win rate of 84.93% paired with a profit factor of 6.25 and a maximum drawdown capped at 13.17%. By maintaining long multi-week position holdings alongside selective trade selection averaging 10.43 trades per year, the strategy presents a highly disciplined historical return profile suitable for deployment within automated trading bots and algorithmic trading systems.
Analyse au 14.09.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
1. Strategy profile
The quantitative strategy under review represents the primary core model for ALL stock, ranking first overALL among evaluated strategies for this asset with a DevioLab score of 86.25. Evaluated over a seven-year period spanning from September 16, 2019, through September 14, 2026, the model executes entry and exit decisions on a 15-minute timeframe while capturing extended directional moves. Across its full historical scope, the algorithm generated 73 completed trades, achieving 62 winning trades and 11 losing trades. This translates to an overALL win rate of 84.93% and a cumulative simulated gain of +2,963.55%, yielding an annualized return metric of 223.24%. Designed to serve as the programmatic core for automated trading bots, trading robots, and execution agents, the system prioritizes selective market engagement over high-frequency turnover, trading only when specific multi-factor alignment occurs.
2. Trading rhythm and position duration
Despite operating on 15-minute price bars, the strategy displays a distinctly patient, positional holding profile rather than a short-term intraday rhythm. On average, the system conducts 10.43 completed trades per year, which corresponds to approximately 1.18 trades per active month. The statistical distribution of position duration reveals an average holding time of 551.09 hours (approximately 22.96 days) and a median holding duration of 363.50 hours (roughly 15.15 days). Spacing between trade completions is similarly extended, averaging 33.43 days between exits with a median interval of 26.99 days. This temporal structure demonstrates that intraday 15-minute bars are utilized for execution precision and structural monitoring, while the underlying holding horizon spans multiple weeks. For automated trading agents and deployment frameworks, this low turnover reduces execution stress and eliminates exposure to high-frequency market noise.
3. Quality of historical results
The historical performance quality is characterized by strong payoff symmetry and broad distribution of returns rather than reliance on isolated extreme events. The strategy achieved a profit factor of 6.25, indicating that gross profits exceeded gross losses by more than six to one. The overALL average trade return stands at +5.25%, closely mirroring the median trade return of +4.49%. This close alignment between mean and median indicates a stable expected payoff across individual trades without heavy skewing from single extreme outliers. The single best trade in the dataset delivered +49.23%, whereas the top three winning trades collectively accounted for 20.27% of total gross profit. This modest concentration percentage confirms that the cumulative historical gain of +2,963.55% was constructed across a repeatable series of positive trades rather than being driven by a handful of lucky structural shocks.
4. Risk, drawdown and losing behavior
Risk parameters within the historical backtest exhibit notable stability and containment relative to total accumulated return. The maximum observed drawdown across the entire seven-year dataset was 13.17%, which aligns closely with the single worst recorded trade loss of -13.17%. A critical factor supporting this controlled drawdown profile is the strategy's losing streak resistance: across ALL 73 trades, the longest consecutive losing streak was exactly 1 trade. Out of 73 total trade exits, only 11 resulted in losses, while the longest winning streak reached 14 consecutive trades. The coexistence of an 84.93% win rate and a modest 13.17% maximum drawdown demonstrates that historical losses were quickly isolated and failed to cluster into compounded structural drawdowns. However, because individual positions remain open for weeks at a time, equity positions carry overnight and weekend gap risk inherent to US equity stock trading.
5. Behavior through time and yearly stability
An examination of yearly breakdown statistics reveals exceptional operational stability across changing macroeconomic environments. Every single evaluated calendar year from 2020 through 2026 produced positive aggregate returns alongside stable trade generation ranging between 9 and 12 trades annuALLy. In 2020, the strategy generated 9 trades with an 88.89% win rate and a aggregate return of +101.29%. The subsequent years maintained high hit rates and positive gains: 2021 recorded 10 trades at a 90.00% win rate (+42.18%), 2022 generated 9 trades at a 77.78% win rate (+33.95%), 2023 yielded 11 trades at a 90.91% win rate (+63.72%), and 2024 produced 11 trades at a 90.91% win rate (+57.99%). The performance continued into 2025 with 12 trades at a 75.00% win rate (+33.23%) and 2026 with 11 trades at an 81.82% win rate (+50.81%). This multi-year consistency proves that strategy gains were evenly distributed across market cycles rather than concentrated in a single favorable macro regime.
6. Recent period since 2024-06-01 versus full history
Evaluating the performance window from June 1, 2024, through the conclusion of the dataset provides insight into recent strategy behavior. During this recent window, the strategy completed 31 trades, accumulating a cumulative gain of +128.23%. This represents a substantial portion of the total historical dataset, reflecting increased trade completions in recent years while maintaining the core statistical integrity of the system. The strong return generation during this recent sample confirms that the underlying quantitative factors governing entry and exit rules remained effective in modern stock market conditions, showing no evidence of decay or structural friction.
7. Strengths and limitations
The primary structural strengths of this trading strategy include its high hit rate (84.93%), elevated profit factor (6.25), strict drawdown boundary (13.17%), and well-distributed profit contribution, where the top three trades represent only 20.27% of gross profits. Furthermore, its annual consistency across seven consecutive years offers robust statistical backing for quantitative implementations. Conversely, primary limitations center on the total sample size of 73 trades over seven years. Because the strategy averages roughly 10.43 trades annuALLy and retains positions for an average of 22.96 days, traders utilizing automated trading robots must tolerate extended inactive intervals and multi-week market exposure, during which capital remains tied up in single positions.
8. DevioLab analytical conclusion
With a DevioLab score of 86.25 and the top rank for ALL stock, this quantitative strategy represents a robust, highly disciplined approach to stock trading. By pairing intraday 15-minute bar tracking with multi-week trend capture, the algorithm successfully filter out market noise, resulting in exceptional win rates and limited historical drawdown. For developers and institutional operators building automated trading bots, trading agents, or systematic portfolios, the model offers an ideal balance of low execution overhead and strong historical expectancy, provided that position risk during multi-week holding periods is properly managed.
9. Data scope and methodology
This analysis relies exclusively on backtested historical trade data for ALL stock recorded between September 16, 2019, and September 14, 2026. ALL reported statistics, including win rates, drawdown percentages, holding periods, and return totals, derive strictly from simulated closed trade logs executed on a 15-minute candle framework. The results do not account for variable slippage, order routing delays, or transaction costs unless explicitly factored into the strategy parameters. Past simulated performance is provided for research and analytical evaluation purposes only and does not constitute financial advice or a guarantee of future live execution outcomes.