ALABBUSDT
Aktieninstrument · Ausführung über BinanceALAB · Astera Labs, Inc.: Quantitative Strategy Analysis on 15-Minute Timeframe
A comprehensive analytical evaluation of the algorithmic trading strategy for Astera Labs, Inc. (ALAB) on the 15-minute chart. Holding a DevioLab Score of 81.92, this strategy ranks first for the asset, achieving an 86.96% win rate across 46 completed trades over a 2.41-year history dataset.
Vollständige Analyse lesen
Strategy profile
This algorithmic model was engineered for trading Astera Labs, Inc. stock, represented by the symbol ALAB, utilizing a 15-minute candle interval. According to the DevioLab quantitative scoring framework, the strategy earned a total score of 81.92, ranking first among evaluated systems for this ticker. Selected as an independent core solution, the strategy operates within a spot market context without leverage. The underlying backtest dataset spans from March 20, 2024 to August 18, 2026, representing approximately 2.41 years of continuous historical tracking.
Trading rhythm and position duration
Over the full 2.41-year historical window, the algorithm completed 46 closed trades. This corresponds to a moderate annualized frequency of approximately 19.06 trades per year. This operational tempo is strictly moderate-frequency and does not constitute scalping, as entry triggers require selective market setups. While explicit mean and median holding times in hours are not captured in the primary dataset, the trade frequency confirms a measured approach designed for 15-minute chart dynamics.
Quality of historical results
Historical simulated performance reflects a cumulative return of 55058.02% across all closed trades, which translates to a normalized annualized return figure of 19189.27%. Out of 46 total executions, 40 ended in profit, delivering a high win rate of 86.96%. The average trade profit reached 16.27%, while the median return per trade was 14.89%. The single best trade registered a gain of 60.49%. Gain distribution remains remarkably balanced, with the top three winning trades accounting for only 19.99% of total gross profit, confirming that performance is driven by consistent trade gains rather than extreme outliers.
Risk, drawdown and losing behavior
The system produced a profit factor of 2.78, indicating strong payout efficiency relative to gross losses. Across the dataset, only 6 trades closed with a loss. The maximum adverse outcome in a single trade was -20.96%, which perfectly coincides with the peak-to-trough maximum drawdown of 20.96%. The longest continuous winning streak reached 14 consecutive trades, whereas the longest losing streak was limited to just 1 trade. This demonstrates an immediate statistical recovery pattern after any isolated unprofitable execution.
Behavior through time and yearly stability
Specific yearly breakdowns are omitted due to data structure constraints, but the macroeconomic trend across the 2.41-year sample indicates steady equity progression. Completing 46 total trades with an 86.96% success rate prevents prolonged stagnation periods. The occurrence of a 14-trade winning streak underscores the model's capacity to capitalize effectively on sustained directional volatility in the underlying stock.
Strengths and limitations
Key advantages of the strategy include an impressive 86.96% win rate, a robust 2.78 profit factor, a manageable max drawdown of 20.96%, and brief losing sequences capped at a single trade. Furthermore, profit generation is well-distributed, with top winners accounting for less than 20% of gross gains. Limitations include a modest sample size of 46 trades over 2.41 years, lack of trade activity in the post-June 2024 segment, and absent granular holding duration metrics.
DevioLab analytical conclusion
The quantitative trading strategy for ALAB presents strong statistical credentials, highlighted by its top rank for the ticker and a DevioLab Score of 81.92. It is best suited for traders seeking high-precision, low-frequency entries on 15-minute stock charts. While the high win rate and controlled drawdown provide compelling historical backing, users should maintain realistic expectations and account for the limited sample size when evaluating future applicability.
Data scope and methodology
All figures presented in this study are derived from historical backtesting simulations of closed trades for Astera Labs, Inc. on the 15m interval between March 20, 2024 and August 18, 2026. These historical metrics describe past algorithmic performance, do not represent real-time brokerage returns, and offer no guarantee of future profits.