AIZ · Assurant Inc.
Stock Universe · mercado bursátil globalAIZ · AIZ Quantitative Strategy Analysis: Evaluating a Low-Frequency, High-Win-Rate 15-Minute Algorithmic Model
This quantitative research study analyzes the top-ranked algorithmic trading strategy for AIZ, evaluating nearly seven years of backtested market history from September 2019 through September 2026. The strategy achieves a DevioLab score of 87.80, earning the rank of number one for the symbol. Across 86 completed trade cycles, the model displays an 81.40% win rate paired with a Profit Factor of 5.0 and a low maximum drawdown of 8.13%. Despite evaluating price action on a 15-minute chart, the algorithm operates as a patient, position-holding trading agent, averaging roughly 18.5 days per position and producing an annual trade frequency of 12.30 transactions. Broad profit distribution across trades, minimal profit concentration in top winners (14.13%), and persistent positive performance in recent evidence since June 2024 highlight a structurally robust quantitative trading logic.
Análisis al 13.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The quantitative strategy designated for AIZ represents an automated decision logic designed to systematically capture medium-term price expansions while maintaining strict drawdown boundaries. Evaluated on a 15-minute timeframe over a 6.99-year backtest horizon spanning September 16, 2019, through September 13, 2026, the model compiled 86 completed trades. Out of these historical trade cycles, 70 ended in positive return territory while 16 resulted in realized losses, establishing an overall win rate of 81.40%. This performance profile translated into an overall historical profit sum of 3,304.92% and an annualized performance metric of 220.42%. With a DevioLab score of 87.80, this strategy holds the highest rank for the symbol AIZ within the DevioLab quantitative database. The decision logic is optimized to function as the core signal engine for automated trading bots, trading robots, and algorithmic trading agents looking for structured exposure to equity price movement. The defining architectural trait of this model is its blend of high intraday bar resolution for signal verification and multi-week holding behavior for profit execution. Rather than churning capital through frequent market entries, the strategy maintains a selective, high-conviction cadence that minimizes exposure to market noise.
Trading rhythm and position duration
A granular examination of the strategy's operational timeline reveals a clear structural distinction between signal generation timing and trade execution duration. While the model processes price action on a 15-minute candle interval, position turnover is deliberate and extended. The average position holding duration spans 444.82 hours, which equates to approximately 18.5 calendar days. The median holding time stands at 321.38 hours, or roughly 13.4 calendar days. This multi-week holding horizon demonstrates that the 15-minute timeframe is utilized strictly for entry precision and stop adjustments rather than high-frequency scalping. The spacing between trade exits further reinforces this measured cadence. The algorithm records an average of 29.38 days between trade closures, with a median spacing of 22.03 days. Across the entire 6.99-year evaluation window, the model averaged 12.30 trades per year, or approximately 1.39 trades per active month. For deployment within an automated trading robot or algorithmic agent, this low-frequency structure implies lower operational overhead and reduced sensitivity to intra-day order execution bottlenecks, making it suitable for systematic portfolio allocation.
Quality of historical results
The statistical quality of the strategy's returns is characterized by consistent profit accumulation and exceptionally well-distributed gains. The historical Profit Factor reaches 5.0, meaning that gross generated profits were five times greater than gross losses across all closed positions. The average trade outcome across all 86 completed positions was +4.55%, while the median trade outcome stood at +3.85%. The close alignment between the average and median return values indicates a stable statistical distribution, confirming that total strategy profitability is not dependent on a few isolated, extreme outperformance events. Further reinforcing this structural stability is the concentration metric: the top three winning trades combined accounted for only 14.13% of the overall gross profit generated across the strategy's history. The single best trade delivered a gain of +26.23%, whereas the single worst trade produced a loss of -7.36%. Because the maximum winning outlier is balanced across a vast majority of winning cycles (70 out of 86 trades), the payoff profile shows that the algorithm consistently harvests modest-to-substantial gains rather than relying on rare black-swan tail events.
Risk, drawdown and losing behavior
Risk containment within the AIZ strategy is one of its most prominent quantitative strengths. Over the full 6.99-year testing period, the maximum equity drawdown recorded was 8.13%. When viewed alongside a full-history profit metric of 3,304.92%, this drawdown profile reflects exceptional downside control. The strategy experienced a maximum consecutive losing streak of just 2 trades, whereas its longest winning streak reached 12 consecutive profitable exits. This asymmetry between win streaks and loss streaks explains why equity declines remained shallow and temporary. The single worst trade recorded in the dataset was a controlled loss of -7.36%. The fact that the maximum drawdown (8.13%) is only slightly larger than the worst individual trade (-7.36%) confirms that capital decay during adverse market phases was primarily driven by isolated losing trades rather than compounding series of consecutive stop-outs. For automated trading agents operating under strict risk mandates, this historical behavioral profile provides a predictable downside parameter.
Behavior through time and yearly stability
An evaluation of the yearly breakdown demonstrates remarkable consistency across varying stock market environments from 2019 through 2026. In 2019, during the initial fraction of the testing period, the model closed 1 trade with a 100% win rate, yielding +4.75%. In 2020, trade frequency expanded to 14 trades, delivering an 85.71% win rate and a cumulative return sum of +91.87%. The following year, 2021, generated 11 trades with a 72.73% win rate and a +28.76% return sum. In 2022, despite broader market headwinds, the algorithm maintained stability with 13 trades, a 76.92% win rate, and a net gain of +37.39%. The positive trajectory continued through 2023, recording 13 trades, a 76.92% win rate, and +72.14% in total returns. In 2024, the strategy executed 11 trades with an 81.82% win rate for a +60.67% return sum. Year 2025 achieved the highest annual win rate at 92.31% across 13 trades, adding +57.71%. Finally, during the partial year of 2026, the strategy closed 10 trades with an 80.00% win rate and +38.17%. The complete absence of losing years across the seven-year timeline demonstrates robust market adaptability.
Recent period since 2024-06-01 versus full history
Examining performance metrics during the recent evidence window beginning June 1, 2024, provides insight into whether the strategy maintains its historical edge in recent market conditions. Between June 1, 2024, and September 13, 2026, the strategy completed 29 trades, generating a cumulative return sum of +135.70%. This represents a substantial portion of the total historical trade count, accounting for approximately 33.7% of all completed transactions in roughly 32% of the total strategy timeframe. Comparing this recent window to the broader 6.99-year history confirms that the strategy's signal frequency and profitability have remained active and effective. The recent average trade return within this sub-window remained strong, aligning with the long-term historical average trade of +4.55%. There is no evidence of signal decay, operational drift, or performance degradation in recent years, reinforcing the validity of the underlying quantitative decision model.
Strengths and limitations
The primary quantitative strength of this trading strategy lies in its high statistical accuracy, evidenced by an 81.40% win rate, a 5.0 Profit Factor, and a low maximum drawdown of 8.13%. The profit distribution is well-balanced, with the top three winning trades contributing only 14.13% of total gross profit, ensuring that returns are distributed across many trades. Furthermore, the strategy demonstrates multi-year stability, posting positive aggregate returns in every calendar year from 2019 through 2026. Conversely, the principal limitation of the model is its small absolute sample size. With 86 completed trades over nearly seven years, the trade sample is modest, producing an average of only 12.30 trades per year. Investors or automated trading bot operators seeking high execution frequency or daily active trading will find this strategy slow-moving. Additionally, position holding periods averaging 18.5 days require patience and exposure to overnight and weekend market risks.
DevioLab analytical conclusion
The AIZ quantitative trading strategy achieves the rank of number one for the symbol on DevioLab with a composite score of 87.80. The strategy combines high signal precision with patient position management, turning 15-minute price data into a robust multi-week trading logic. With a total historical profit sum of 3,304.92%, a low 8.13% drawdown, and consistent yearly profitability, the model serves as an excellent reference logic for automated trading bots and systematic trading agents. While historical metrics demonstrate high statistical reliability and strong downside containment, quantitative traders must recognize that historical backtest results do not guarantee future live execution outcomes. Changes in market volatility, structural shifts in equity price behavior, and unforeseen liquidity conditions can alter performance dynamics over time.
Data scope and methodology
This research analysis is based on historical backtested strategy data for the asset symbol AIZ on a 15-minute chart interval covering the period from September 16, 2019, to September 13, 2026 (a total duration of 6.99 years). The dataset comprises 86 closed trade transactions. All metric calculations, including win rates, holding durations, annual breakdowns, and recent performance windows, are computed strictly from historical closed trade records. This study is provided for educational and analytical research purposes only and does not constitute financial advice or live trade recommendations.