AXON · Axon Enterprise, Inc.
Stock Universe · global stock marketAXON · AXON Quantitative Strategy Analysis: Macro Position Duration and Outlier-Driven Gains on a 15-Minute Base Framework
This analytical report evaluates an algorithmic trading model developed for AXON (AXON Enterprise) evaluated across a 5.59-year historical dataset spanning from February 2021 to September 2026. Despite executing on a 15-minute chart intraday interval, the system exhibits an ultra-low trade frequency, closing just 19 trades over the multi-year history, which translates to roughly 3.4 trades per year. The strategy achieves a overall return of +2704.71% with a win rate of 73.68% and an extraordinary profit factor of 6.25. However, statistical evaluation reveals that position holding times are extended, averaging 1,948.81 hours (approximately 81.2 days), and gross profit is moderately concentrated, with the top three winning trades generating 52.20% of total gains. While recent performance since June 2024 accounts for 11 of the 19 total trades and +239.95% in cumulative sum, the low overall sample size requires careful statistical consideration regarding signal stability and future regime robustness.
Analysis as of 21.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative trading model evaluated here targets AXON (AXON Enterprise) in equity markets, utilizing a 15-minute candlestick chart interval as its baseline data structure. Over a historical backtest span of 5.59 years—from February 16, 2021, to September 21, 2026—the system completed a total of 19 trades. Generating a cumulative net return of +2704.71% and an annualized equivalent figure of 536.08%, the strategy achieves a DevioLab score of 63.88, ranking it third among evaluated models for this ticker. Designed to function as the decision architecture for automated trading bots, trading robots, or quantitative execution agents, the logic filters out intraday noise to capture extended structural price moves. Rather than engaging in frequent turnover, the model acts as a highly selective trend-following or momentum-positioning agent, requiring significant confluence before triggering trade executions.
Trading rhythm and position duration
A compelling structural paradox of this strategy is the contrast between its 15-minute operational interval and its long-term holding horizon. Although technical price inputs are sampled every 15 minutes, the execution frequency remains exceptionally low at 3.4 trades per year, or approximately one trade per active month. Position durations reflect a macro swing or position trading style: the average holding time spans 1,948.81 hours (roughly 81.2 days), while the median holding duration reaches 1,366.75 hours (approximately 57.0 days). Furthermore, the temporal spacing between closed trades is wide, with an average of 110.33 days and a median of 82.41 days between position exits. This confirms that the strategy is designed not for short-term scalping or high-frequency intraday flipping, but for riding long-term corporate trends while remaining entirely cash-bound during unfavorable market regimes.
Quality of historical results
The statistical distribution of return outcomes highlights high payoff efficiency combined with significant trade-level tail skewness. Out of 19 closed transactions, 14 were profitable while 5 resulted in losses, yielding a win rate of 73.68%. The strategy achieves a remarkably high profit factor of 6.25, indicating that gross historical gains exceeded gross losses by more than sixfold. The average closed trade realized +22.98%, whereas the median trade realized +14.67%, demonstrating a positive rightward skew where top-performing trades pulled the mean well above the median. The strategy's single best trade produced a +114.42% gain, while the worst trade incurred a loss of -22.40%. A key quality insight resides in profit concentration: the three largest winning trades collectively accounted for 52.20% of the gross cumulative profit. While the win rate is high, gross returns remain heavily dependent on capturing rare, outsized trend expansions.
Risk, drawdown and losing behavior
Risk metrics across the 5.59-year historical sample reflect moderate peak-to-trough drawdowns alongside well-contained losing streaks. The historical maximum drawdown recorded for the portfolio equity curve was 22.40%, which closely matches the strategy's worst individual trade loss of -22.40%. Over the course of the backtest, the longest consecutive losing streak was capped at just 2 trades, whereas the longest winning streak extended to 12 consecutive trades. The ability to avoid prolonged losing runs supported equity curve stability during expansionary phases. However, because positions remain active for an average of 81 days, an open drawdown can persist over several calendar months before a position closes. Traders deploying automated trading agents with this architecture must possess the psychological tolerance required to hold positions through multi-month price swings without premature manual interventions.
Behavior through time and yearly stability
Evaluating yearly historical performance reveals a distinct evolution in trade cadence and profitability. In 2021, the strategy recorded a single losing trade of -1.34% (0% win rate). The year 2022 generated 2 trades with a 50% win rate and a slight net profit sum of +3.39%. The model hit its primary growth surge in 2023 and 2024: 2023 produced 3 trades, all winners (100% win rate), accumulating +148.94%, while 2024 saw 6 trades, all winners (100% win rate), generating +162.35%. In 2025, trade frequency moderated to 3 completed trades (2 wins, 1 loss) for a net sum of +83.35% (66.67% win rate). In the partial year of 2026 up to September, 4 trades were completed (2 wins, 2 losses), generating +39.96% (50% win rate). Overall, performance was highly concentrated during the 2023–2025 expansion, demonstrating that cumulative returns were driven by specific multi-year macro trends rather than uniform annual distributions.
Recent period since 2024-06-01 versus full history
Examining performance during the recent sample window from June 1, 2024, to September 21, 2026, provides valuable context regarding the model's recent behavior. Of the 19 completed trades in the overall dataset, 11 trades completed within this recent window. These 11 recent trades produced a combined sum of +239.95%, representing a substantial portion of the overall +2704.71% total baseline profit. This indicates that trade generation accelerated significantly in the latter half of the backtest window compared to the quiet 2021–2022 period. The high concentration of recent closed trades reflects an active environment for the strategy's entry criteria over the past two years, validating that the underlying logic remained functional and responsive during recent market regimes without structural degradation.
Strengths and limitations
The primary analytical strengths of this strategy include its high win rate (73.68%), exceptional profit factor (6.25%), capped losing streaks (maximum 2 consecutive losses), and low transaction frequency that inherently minimizes trade-related exposure. Furthermore, the 12-trade winning streak demonstrates an capability to compound capital during sustained trend regimes. Conversely, the model presents clear statistical limitations. With only 19 completed trades across 5.59 years, the small sample size introduces statistical sensitivity, where individual trade outcomes heavily weight summary metrics. Additionally, top-heavy profit distribution—with the top three trades generating 52.20% of gross gains—means that missing a small fraction of winning signals would severely impair historical performance. Long holding durations also subject positions to overnight gap risk and multi-month holding stress.
DevioLab analytical conclusion
The AXON 15-minute quantitative strategy represents an ultra-selective trend-capturing system optimized for long-horizon position holding. Achieving a DevioLab score of 63.88 and ranking third for the AXON ticker, the strategy demonstrates how fine-grained 15-minute price monitoring can be harnessed to execute low-frequency, high-payoff positions. While an overall gain of +2704.71% and a profit factor of 6.25 demonstrate exceptional backtested efficiency, deployment via trading bots or execution agents requires strict adherence to system parameters. Capital allocators must recognize that long-term performance rests upon enduring extended holding periods averaging over 80 days and accepting performance reliance on rare outlier wins. Appropriately sized capital and patient risk management remain essential when implementing this logic.
Data scope and methodology
This analysis is based entirely on historical backtested strategy data for AXON over the period from February 16, 2021, to September 21, 2026. All statistics, including return percentages, holding durations, win rates, drawdowns, and trade counts, represent simulated closed transactions calculated without embedded assumptions regarding live brokerage slippage, variable order execution fees, or margin interest. The recent sub-period statistics represent trades with closed timestamps on or after June 1, 2024 UTC. Historical backtest performance is an analytical tool for evaluating strategic edge and market fit; it does not constitute financial advice, nor does it guarantee equivalent future profitability in live market conditions.