AAOIBUSDT
Instrument boursier · exécution via BinanceQuantitative Analysis of the 15-Minute Algorithmic Trading Strategy for Applied Optoelectronics, Inc. (AAOI)
A comprehensive evaluation of a moderate-frequency algorithmic trading strategy applied to Applied Optoelectronics, Inc. (AAOI) stock. This analysis explores the statistical characteristics of the strategy over a 6.93-year historical simulation, highlighting a profound tension between an exceptionally high win rate and a comparatively low profit factor, alongside significant periods of recent inactivity.
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Strategy profile
This report examines a specific algorithmic trading strategy engineered for Applied Optoelectronics, Inc., trading under the ticker AAOI in the stock market. The strategy operates on a 15-minute timeframe, utilizing this intraday resolution to identify entry and exit points. Over a simulated historical period spanning 6.93 years, from September 2019 to August 2026, the system executed a total of 123 completed trades. Classified as a moderate-frequency approach, the algorithm is highly selective, waiting for precise market conditions rather than remaining continuously exposed to market volatility. The strategy has achieved a DevioLab score of 62.15, securing the sixth rank for this specific ticker, and is designated as an independent best core strategy. This profile suggests a mathematically rigorous approach that prioritizes setup quality over transaction volume.
Trading rhythm and position duration
The operational cadence of this strategy is notably deliberate. With an average of 17.75 trades per year, the system executes approximately one to two trades per month. This frequency indicates that while the algorithm analyzes price action on a 15-minute interval, it does not function as a high-frequency scalping tool. Instead, the 15-minute chart is likely utilized to optimize the timing of entries and exits for broader price swings. The dataset does not provide specific metrics regarding the average or median holding hours, nor the days between exits. Consequently, we cannot definitively state whether positions are closed within the same trading session or held overnight. However, the moderate trade count combined with the substantial median trade percentage strongly implies that positions are held long enough to capture significant price movements, rather than being flipped for fractional gains within minutes.
Quality of historical results
The historical performance metrics of this strategy present a fascinating quantitative paradox. Out of 123 completed trades, 100 were profitable, resulting in an impressive win rate of 81.30 percent. The median trade yielded a return of 11.27 percent, while the average trade reached 11.73 percent. These figures demonstrate that the strategy consistently captures substantial positive movements when it is right. The compounding effect of these frequent, double-digit wins over the 6.93-year period resulted in an extraordinary total simulated profit. Furthermore, the top three winning trades account for only 9.85 percent of the gross profit, indicating that the strategy's success is not dependent on a few lucky outliers, but rather on a broad base of consistent winners. The best single trade achieved a remarkable 70.77 percent gain. However, this high accuracy is counterbalanced by a profit factor of only 1.11. A profit factor this close to 1.0 indicates that the total gross profit is only marginally higher than the total gross loss, revealing that the strategy's losing trades, though infrequent, are exceptionally damaging to the accumulated gains.
Risk, drawdown and losing behavior
The risk profile of this AAOI strategy requires careful scrutiny, particularly given the implications of the low profit factor. The maximum historical drawdown stands at a severe 58.07 percent. This indicates that despite the high win rate, the equity curve experienced a period where more than half of the portfolio value was erased. The worst single trade resulted in a loss of 49.84 percent. The fact that a single loss can consume nearly half the portfolio explains why the profit factor remains at 1.11 despite an 81.30 percent win rate. The strategy features a longest winning streak of 14 consecutive trades, which drives rapid equity growth, but the longest losing streak is merely 2 trades. This demonstrates that the severe drawdowns are not caused by prolonged periods of poor performance, but rather by sudden, catastrophic losses in individual positions. Such a profile demands extreme caution, as the algorithm clearly struggles with risk containment when market conditions move violently against its directional bias.
Behavior through time and yearly stability
Assessing the stability of an algorithmic strategy over time is crucial for understanding its adaptability to different market regimes. The dataset covers a broad span of 6.93 years, providing a substantial timeframe that likely includes various bullish, bearish, and consolidating phases for Applied Optoelectronics, Inc. Unfortunately, the underlying statistics do not include a granular yearly breakdown of performance. Without this year-by-year data, it is impossible to determine whether the massive compounding of returns occurred steadily throughout the entire period or was concentrated in a specific era of outsized volatility. Similarly, we cannot pinpoint when the severe 58.07 percent drawdown occurred. The lack of annual resolution means that while the aggregate performance over nearly seven years is mathematically positive, the journey to those returns remains opaque, masking potential periods of stagnation or elevated risk.
Strengths and limitations
The primary strength of this algorithmic strategy is its remarkable precision when entering the market, evidenced by the 81.30 percent win rate and a longest winning streak of 14 trades. The ability to consistently capture median gains of over 11 percent on individual stock trades is highly commendable and speaks to the robustness of the entry logic. Additionally, the broad distribution of gross profits, with the top three winners accounting for less than 10 percent of the total, shows a healthy lack of reliance on outliers. However, the limitations are equally profound. The most glaring weakness is the severe risk profile, characterized by a worst trade of nearly negative 50 percent and a maximum drawdown of 58.07 percent. The low profit factor of 1.11 confirms that the strategy sacrifices risk control for entry accuracy. Furthermore, the complete absence of trades since June 2024 and the lack of specific holding duration data limit the ability to confidently project this strategy into current market environments.
DevioLab analytical conclusion
The DevioLab assessment of this 15-minute algorithmic strategy for AAOI reveals a system of extremes. It is a highly selective, moderate-frequency model that excels at identifying profitable setups, allowing theoretical capital to compound at an astonishing rate over a multi-year simulation. However, the structural mechanics of the strategy present a classic asymmetrical risk dilemma. The algorithm operates by accumulating many moderate wins while occasionally suffering devastating losses. A profit factor of 1.11 alongside an 81 percent win rate is a definitive mathematical signature of a system that fails to cut losses early. While the historical aggregate numbers are undeniably positive, the practical application of a strategy that requires enduring a 58 percent drawdown and individual trade losses of 50 percent requires an ironclad risk management overlay that the core algorithm currently lacks. The recent inactivity further suggests that the strategy is best viewed as a historically intriguing model currently sidelined by evolving market dynamics.
Data scope and methodology
The analysis provided in this report is based exclusively on the provided statistical dataset for the specified algorithmic strategy. The data encompasses a historical simulated period beginning on September 16, 2019, and concluding on August 21, 2026, covering exactly 6.93 years of market data. The percentages and performance metrics described herein represent historical simulated closed trades. They do not reflect an exact brokerage account return, do not account for unlisted slippage or external execution fees, and absolutely do not guarantee future performance. The absence of specific data points, such as yearly breakdowns and holding durations, has been explicitly noted where it limits the depth of the analysis. This report is strictly for analytical and research purposes and does not constitute financial or investment advice.