QQQBUSDT
股票工具 · 通过 Binance 执行QQQ · Invesco QQQ Trust: Quantitative Strategy Analysis with DevioLab Score 82.86
In-depth quantitative assessment of a 15-minute algorithmic strategy for Invesco QQQ Trust. The trading model holds the top rank for this ticker, backed by an 84.09 percent win rate and a controlled maximum drawdown of 11.96 percent across 6.53 years of history.
阅读完整分析
Strategy profile
This algorithmic strategy is engineered for the stock market asset Invesco QQQ Trust on a 15-minute chart interval. Evaluated through rigorous historical performance metrics, the model achieved a DevioLab Score of 82.86, placing it first overall among tested strategies for the QQQ ticker. Selected as an independent top performer, the strategy balances substantial compound growth with defined risk bounds. Over the analyzed history, it generated a total return of 1347.69 percent, translates to an annualized performance of roughly 134.80 percent. All figures represent simulated historical execution and do not guarantee future live performance.
Trading rhythm and position duration
Throughout the 6.53-year dataset span, the strategy executed a total of 88 completed trades. This corresponds to an average trade frequency of 13.48 trades per year. Exact position holding hours and inter-trade durations are not populated in the export parameters, requiring an evaluation based on total activity volume. The low total trade count confirms that the strategy operates with extreme selectivity rather than high-frequency scalp trading, patiently awaiting specific technical setups on the 15-minute chart of Invesco QQQ Trust.
Quality of historical results
The quality of historical trades is underscored by an 84.09 percent win rate, with 74 profitable trades against 14 losing trades out of 88 total executions. The profit factor stands at 2.50, demonstrating a healthy margin of total gross gains over total gross losses. The average trade yield is 3.39 percent, with a median return of 2.95 percent and a peak trade return of 16.79 percent. Notably, the top three winning trades combined account for only 12.91 percent of the gross profit. This confirms that gains are distributed broadly across successful trades rather than reliant on a few lucky spikes.
Risk, drawdown and losing behavior
The maximum historical equity drawdown was restricted to 11.96 percent, an impressively modest figure relative to the strategy's triple-digit annualized return metric. The single largest trade loss was held to negative 8.33 percent. Streak analysis shows a maximum winning run of 18 consecutive trades compared to a maximum losing streak of just 3 trades. This ratio reflects effective loss containment and robust recovery capabilities during unfavorable market conditions.
Behavior through time and yearly stability
The dataset spans from February 10, 2020, through August 20, 2026. It is important to note that February 10, 2020, marks solely the start of this historical data sample, not the inception or listing date of Invesco QQQ Trust. While granular year-by-year statistical breakdown records are not explicitly itemized in this export, the steady long-term rate of 13.48 trades per year highlights a consistent, patient entry cadence over the multi-year evaluation horizon.
Strengths and limitations
The primary strengths of this model include an exceptional 84.09 percent win rate, a strong profit factor of 2.50, and a well-managed maximum drawdown of 11.96 percent. Profit distribution is well-balanced across trades without concentration risk. Conversely, the main limitation is the sample size of 88 total trades over 6.53 years, which warrants cautious statistical interpretation, alongside extended idle periods such as the zero-trade regime observed since June 2024.
DevioLab analytical conclusion
The algorithm for Invesco QQQ Trust stands out as a top-tier quantitative model, earning the rank one spot for its ticker with an overall score of 82.86. Its high win rate and strict drawdown management make it a compelling subject of historical strategy research. Investors should account for its selective trading pace and potential for long inactive phases. This report serves as objective historical analysis and does not constitute financial advice.
Data scope and methodology
This analysis is derived from backtested performance data for QQQ on a 15-minute timeframe between February 10, 2020, and August 20, 2026, encompassing 6.53 years. All metrics reflect simulated closed trades under standardized trading assumptions. Historical return percentages are purely illustrative of past dataset conditions and offer no guarantee of future trading outcome.