BABABUSDT
Instrumento de ações · execução via BinanceBABA · Alibaba Group Holding Limited: Quantitative Analysis of a 15-Minute Algorithmic Strategy
An analytical study of an algorithmic trading system applied to Alibaba Group Holding Limited on the 15-minute timeframe. Based on nearly seven years of historical data covering 225 closed trades, the strategy demonstrates a profit factor of 2.50 and controlled equity drawdowns.
Ler análise completa
Strategy profile
This algorithmic trading model is tailored for Alibaba Group Holding Limited (ticker BABA) operating on the 15-minute interval. Within the DevioLab quantitative evaluation framework, the strategy achieved a score of 78.79, ranking 2nd among all evaluated algorithms for this equity ticker. The historical evaluation spans 6.92 years from September 16, 2019, to August 18, 2026. The algorithm focuses on capturing short-to-medium price imbalances on intraday movements without over-trading. By seeking high-probability entry setups, the model accumulates steady returns over time rather than relying on aggressive exposure.
Trading rhythm and position duration
Across the full dataset of nearly seven years, the algorithm generated 225 completed trades, translating to an average trading frequency of approximately 32.5 trades per year. This highlights a highly selective entry system that avoids market noise and executes trades only when specific statistical parameters are satisfied. While specific metrics for holding hours and days between exits are not recorded in the source dataset, the rate of roughly two to three trades per month reflects a patient posture. The strategy is not a scalping model; its moderate frequency minimizes trading friction, execution drag, and transaction costs.
Quality of historical results
The historical performance reveals strong consistency across the trade sample. Out of 225 completed trades, 155 were profitable, establishing a win rate of 68.89%. The strategy achieved a overall profit factor of 2.50, demonstrating a solid margin of gross gains relative to gross losses. The total cumulative historical return reached 3852.13%, which translates to an annualized return of 275.02%. The average trade yielded 1.81%, while the median trade stood slightly higher at 2.06%. Notably, the top 3 winning trades generated only 7.40% of total gross profits, proving that performance is broadly distributed across the dataset rather than driven by extreme outliers.
Risk, drawdown and losing behavior
The maximum historical equity drawdown was restricted to 21.12%. For single-stock trading in a volatile instrument such as Alibaba Group Holding Limited, maintaining drawdowns near this level points to effective risk parameters. The worst single trade resulted in a loss of -18.55%, compared to a peak winning trade of 19.62%. The model also demonstrated steady streak resilience: its longest winning streak reached 8 consecutive trades, whereas its longest losing streak was capped at 4 consecutive trades.
Behavior through time and yearly stability
Over the 6.92-year period, the model navigated various macroeconomic regimes and structural trend shifts in BABA. By maintaining a disciplined regime of 32.5 trades per year, the strategy avoids overtrading during choppy or unfavorable market environments. Granular yearly breakdowns are not provided in the source dataset. However, the overarching statistical metrics indicate that the strategy maintained a positive expectancy across the full timeline through selective signal filters.
Strengths and limitations
Key strengths of this model include a strong 68.89% win rate, a robust profit factor of 2.50, and a contained maximum drawdown of 21.12%. Furthermore, the low concentration of gross profits in the top 3 trades (7.40%) confirms healthy return distribution. The primary limitation lies in the modest sample size of 225 trades over nearly seven years. Investors must also be prepared for extended periods of inactivity, as demonstrated by the zero trades recorded since June 2024.
DevioLab analytical conclusion
Scoring 78.79 and ranking 2nd for BABA, this 15-minute algorithmic strategy provides a well-structured balance between high win accuracy and controlled drawdown risk. It represents a disciplined quantitative approach to trading equity volatility. While historical backtest metrics demonstrate a clear statistical edge, past performance is purely analytical and does not guarantee future results. Traders should evaluate the low trade frequency and inactive periods within their broader portfolio context.
Data scope and methodology
This analysis is derived from simulated closed trades on spot BABA stock data spanning September 16, 2019, to August 18, 2026. All percentage metrics reflect historical simulated closed performance. These statistics do not represent actual live brokerage execution or guarantee future returns. This document is provided strictly for educational and quantitative research purposes and does not constitute investment advice.