AFL · Aflac Incorporated
Stock Universe · global stock marketAFL · AFL Algorithmic Strategy Analysis: Low-Frequency Trend Capture with a 91.3% Historical Win Rate
This quantitative research report evaluates an algorithmic trading strategy developed for AFL · AFL within the stock market, operating on a 15-minute timeframe. Over an analytical evaluation period of approximately 7.0 years—spanning from September 16, 2019, to September 13, 2026—the strategy generated a cumulative historical return sum of +1153.18% across 23 completed trades. Characterized by an exceptionally selective entry model, the strategy recorded a 91.3% win rate (21 winning trades against 2 losing trades), a profit factor of 6.25, and a maximum drawdown of 24.18%. Position holding times are unusually extended for a 15-minute chart strategy, averaging 2166.44 hours (approximately 90.3 days) with a median duration of 812.75 hours. Designed to serve as the decision engine for automated trading bots and algorithmic trading agents, the system combines high hit-rate stability with macro-style position trades. However, performance analysis also reveals notable structural trade-offs, including a reliance on top performers—where the three largest winning trades account for 55.16% of gross historical profits—and a highly compressed sample size of 3.29 trades per year.
Analysis as of 13.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy designated as AFL 0 +1153.18% 1TRAD-BFJ2 evaluates price action for AFL · AFL on a 15-minute price chart interval. Evaluated over a continuous historical backtest dataset running from September 16, 2019, to September 13, 2026 (spanning 6.99 years), the model completed a total of 23 closed trades. Achieving a total historical profit sum of +1153.18% and an annualized performance metric of 126.31%, the strategy achieves a DevioLab Score of 74.50, securing the number 1 rank among evaluated strategies for this specific ticker. Functionally, the algorithm is structured to provide execution signals suitable for deployment within automated trading robots, trading agents, or systematic execution frameworks. Rather than seeking rapid market turnover, the model utilizes its 15-minute bar resolution to pinpoint specific entry parameters, subsequently holding positions across long temporal horizons to capture macro movement in the underlying equity.
Trading rhythm and position duration
Despite utilizing a granular 15-minute chart resolution, the strategy exhibits an exceptionally low execution frequency. Across the nearly 7-year evaluation history, the system averaged just 3.29 completed trades per year, or 1.15 trades per active trading month. Position holding times reflect a macro multi-week to multi-month operational regime rather than intraday trading. The average holding duration per position stands at 2166.44 hours (approximately 90.27 days), while the median holding duration is 812.75 hours (approximately 33.86 days). This substantial divergence between average and median holding times indicates that while many positions resolve within several weeks, a subset of trades remains open for several months to capture sustained trend cycles. Similarly, the temporal distance between trade exits is broad, averaging 107.93 days between completed trades with a median gap of 46.89 days. The strategy deliberately trades with high selectivity, remaining flat or holding active positions for extended periods without frequent turnover.
Quality of historical results
The statistical profile of the strategy is defined by high directional accuracy and a strong profit factor of 6.25. Out of 23 completed historical trades, 21 resulted in net positive returns, establishing a win rate of 91.3%. The mean return per trade reached +13.61%, significantly outperforming the median trade return of +6.53%. This positive skew between mean and median performance stems from the magnitude of the strategy's top-performing trade, which achieved an individual gain of +71.99%, compared to the worst trade outcome of -24.18%. A critical metric in evaluating the payoff distribution is the gross profit concentration: the three largest winning trades generated 55.16% of the strategy's total gross historical gains. While the high win rate demonstrates consistent trade selection, over half of the cumulative performance is attributable to a tiny cluster of outlier trends, underscoring the importance of capturing rare, high-magnitude trend legs.
Risk, drawdown and losing behavior
Risk metrics for the strategy present an interesting structural dynamic. The maximum historical peak-to-trough drawdown recorded was 24.18%, which exactly mirrors the magnitude of the strategy's single worst losing trade (-24.18%). The model achieved an unbroken winning streak of 21 consecutive profitable trades, while its longest losing streak was limited to just 1 trade. Because losses occurred in isolation rather than in consecutive clusters, equity drawdown was driven by the depth of individual losing trades rather than compounding series of failures. However, the presence of a -24.18% losing trade alongside an average trade yield of +13.61% highlights that when market conditions trigger an adverse exit, the downside magnitude can be substantial relative to typical win sizes. Automated trading bots operating this logic must be engineered to withstand deep, singular open-position retracements without premature manual intervention.
Behavior through time and yearly stability
An examination of the yearly breakdown reveals significant variability in trade execution frequency across different calendar years, alongside consistent win-rate stability. In 2020, the strategy completed 5 trades, winning 4 and losing 1 (80.0% win rate), accumulating a combined return sum of +97.51%. From 2021 through 2023, trading activity slowed dramatically: the model closed exactly 1 trade in 2021 (+60.35%), 1 trade in 2022 (+10.12%), and 1 trade in 2023 (+17.23%), maintaining a 100% win rate during this 3-year period. Execution velocity increased in subsequent years: 2024 saw 3 trades (100% win rate, +77.64% sum), 2025 recorded 7 trades (100% win rate, +26.80% sum), and 2026 recorded 5 trades with 4 wins and 1 loss (80.0% win rate, +23.37% sum). Every individual calendar year produced a positive net sum of returns, demonstrating structural consistency despite multi-year fluctuations in trade frequency.
Recent period since 2024-06-01 versus full history
The window starting June 1, 2024, represents a highly active execution phase within the strategy's dataset. Of the 23 total historical trades completed since September 2019, 15 trades closed on or after June 1, 2024. During this recent evidence window, the strategy accumulated a combined return sum of +127.81%. Comparing this recent performance to the full historical record reveals that 65.2% of all completed trade exits occurred within the final ~2.25 years of the backtest. While the earlier portion of the history (2020–2023) was characterized by multi-month position holds and sparse trade realizations, recent years featured a tighter clustering of trade exits. Importantly, recent trade execution maintained alignment with overall strategy standards, delivering substantial positive cumulative return without degrading historical win-rate dynamics.
Strengths and limitations
The primary structural strength of this strategy lies in its high win rate of 91.3% combined with an exceptional profit factor of 6.25 and a top DevioLab rank for AFL. Its ultra-low execution frequency (3.29 trades per year) minimizes transaction frequency, making it an efficient logic structure for automated trading agents seeking low operational churn. Conversely, the strategy exhibits clear statistical limitations. The most prominent constraint is the small total sample size of 23 completed trades over 6.99 years, which introduces statistical uncertainty regarding sample robustness. Furthermore, the concentration of 55.16% of gross profits in the top 3 winning trades indicates that long-term outperformance relies heavily on capturing a few major market moves. Finally, the worst trade of -24.18% demonstrates that individual losing trades can cause notable drawdowns when position exits are breached.
DevioLab analytical conclusion
With a DevioLab Score of 74.50 and the number 1 ranking for AFL, this algorithmic strategy presents a specialized quantitative profile. It functions effectively as a long-horizon trend capture model optimized for execution via trading robots or automated agents. The system sacrifices trade frequency in favor of extreme selectivity, yielding 21 wins out of 23 historical trades and a net historical profit sum of +1153.18%. Investors and quantitative analysts evaluating this model must weigh its impressive historical hit rate and strong profit factor against its low trade count and profit concentration in key winning positions. As with all quantitative research based on historical simulation, past backtested metrics reflect historical path dependency and do not guarantee future live performance.
Data scope and methodology
This analysis is based strictly on historical backtested performance data for the strategy AFL 0 +1153.18% 1TRAD-BFJ2 applied to AFL · AFL stock data on a 15-minute timeframe between September 16, 2019, and September 13, 2026. All cited metrics—including holding durations, win rates, drawdown figures, and yearly return sums—are calculated strictly from the 23 completed closed trades present in the dataset. Recent period metrics reflect trades with completed sell timestamps on or after June 1, 2024, UTC. The historical start date marks the beginning of the strategy evaluation window rather than the asset listing date. These statistics represent simulated quantitative research for educational and analytical purposes and do not constitute financial advice or live trade execution guarantees.