BX · Blackstone Inc.
Stock Universe · 全球股票市场BX Stock Algorithmic Strategy Analysis: 75% Win Rate and 3283% Historical Return Evaluation
This quantitative evaluation analyzes a 15-minute timeframe swing-trading algorithmic strategy for BX stock across a 7.03-year history from September 2019 to September 2026. Demonstrating a high 75% win rate across 136 completed trades, the strategy achieved a cumulative historical return of +3283% (+147.54% annualized) with a maximum drawdown of 20.82% and a Profit Factor of 1.25. Position durations average 218.21 hours, reflecting a disciplined multi-day swing approach. With a DevioLab Score of 70.10 and rank 2 for the ticker, the model demonstrates robust historical performance and well-distributed returns, including +104.96% cumulative gain across 48 completed trades in the recent testing window since June 1, 2024.
分析日期： 25.09.2026. 策略数据单独更新。阅读完整分析
1. Strategy profile
The quantitative model designated for BX stock is a systematic swing-trading strategy engineered on a 15-minute price execution interval. Spanning a historical dataset of 7.03 years between September 16, 2019, and September 25, 2026, the strategy generated 136 completed trade signals. Out of these closed trades, 102 ended in profit while 34 resulted in losses, establishing a strong historical win rate of 75.00%. The system accumulated a total closed return of +3283.00%, translating to an annualized performance rate of +147.54%. Designed as the core decision logic for automated trading bots, trading robots, and quantitative trading agents, the strategy operates with structural rules tailored to capture multi-day price swings. With a DevioLab Score of 70.10, it holds the second rank among strategies evaluated for the BX ticker, reflecting solid long-term stability and effective market capture.
2. Trading rhythm and position duration
Despite utilizing intraday 15-minute price bars for entry and exit trigger calculations, the strategy exhibits a distinctly patient swing-trading rhythm rather than high-frequency intraday churn. The average holding duration per position is 218.21 hours (approximately 9.09 calendar days), while the median holding time stands at 153.50 hours (roughly 6.4 days). Trade frequency remains moderate, averaging 19.36 trades per year, which corresponds to 1.86 trades per active month. On average, 18.95 days elapse between closed position exits, with a median spacing of 14.01 days. This paced execution structure indicates that automated trading agents deploying this logic do not over-trade or incur excessive market exposure. Instead, positions are held long enough to navigate medium-term price trends while filtering out noise inherent to short-term intraday fluctuations.
3. Quality of historical results
Analyzing the return profile reveals strong alignment between typical trade outcomes and macro strategy performance. The strategy delivers an average trade return of +3.03%, closely matched by a median trade return of +2.94%. This narrow gap between average and median values demonstrates remarkable distributional consistency, proving that historical performance is not driven by extreme statistical noise. Furthermore, the top three winning trades account for only 15.17% of total gross profit, confirming that capital growth is broadly distributed across the winning trade population rather than reliant on a tiny fraction of fortunate outliers. The best individual trade achieved a gain of +33.15%, while the worst trade incurred a loss of -15.08%. The strategy maintains a Profit Factor of 1.25. While the 75% win rate is exceptionally high, the 1.25 profit factor reflects the trade-off that individual losing trades, though infrequent, absorb a meaningful portion of gross gains.
4. Risk, drawdown and losing behavior
Risk parameters indicate a controlled risk profile punctuated by manageable drawdown periods. The historical peak-to-trough maximum drawdown recorded across the 7.03-year evaluation window was 20.82%. The strategy's longest winning streak reached 12 consecutive positive trades, whereas the longest losing streak was limited to 4 consecutive negative trades. The contrast between a 12-trade win streak and a 4-trade loss streak underscores the strategy's tendency to capitalize on sustained trending conditions. However, because the worst single losing trade reached -15.08%, a cluster of consecutive losses can induce rapid drawdown episodes. This dynamic highlights the statistical tension between a high hit rate and trade payoff asymmetry; trading bots executing this logic must be prepared for occasional drawdowns when market conditions temporarily diverge from the strategy's core entry signal parameters.
5. Behavior through time and yearly stability
Yearly breakdown statistics illustrate consistent performance across varied market cycles. Following an initial single-trade baseline in late 2019 (-1.08%), the strategy achieved positive cumulative returns in every full calendar year from 2020 through 2026. In 2020, it recorded 18 trades with an 88.89% win rate and a sum return of +153.46%. In 2021, 13 trades generated +30.87% at an 84.62% win rate. During the challenging market conditions of 2022, win rate contracted to 57.14% across 21 trades, yet the strategy maintained profitability with a sum return of +39.12%. Performance rebounded in 2023 with 25 trades, a 76.00% win rate, and +66.70% sum return, followed by 2024 where 21 trades delivered an 85.71% win rate and +66.19%. In 2025 and 2026, the strategy produced 19 trades (+34.10% sum, 63.16% win rate) and 18 trades (+23.01% sum, 77.78% win rate) respectively, demonstrating remarkable year-over-year persistence.
6. Recent period since 2024-06-01 versus full history
The performance window starting June 1, 2024, serves as an essential benchmark for evaluating modern strategy efficacy. During this recent timeframe, the strategy closed 48 trades, accumulating a combined return sum of +104.96%. Representing over 35% of the total 136 historical trade signals within approximately two years of data, this heightened transaction density confirms active trade generation in current market conditions. The performance achieved during this recent period aligns seamlessly with the strategy's long-term historical trajectory, demonstrating that the underlying decision algorithms continue to capture structural price movements effectively without suffering from alpha decay or regime dislocation.
7. Strengths and limitations
Key statistical strengths of the strategy include an impressive 75.00% historical win rate, tight median-to-average trade consistency (+2.94% versus +3.03%), low gross profit concentration in top winners (15.17%), and robust yearly consistency across seven distinct testing years. Furthermore, the strong performance in the recent sample since June 2024 verifies ongoing functionality. Conversely, key limitations center on the Profit Factor of 1.25 and the worst-case loss scenario of -15.08%. Because individual losses can be noticeably larger than average gains, downside volatility can temporarily offset several winning trades during choppy market phases. Operators deploying trading robots with this decision logic must account for the 20.82% maximum drawdown capability.
8. DevioLab analytical conclusion
In conclusion, the algorithmic strategy for BX stock presents a compelling quantitative profile combining high trade accuracy with steady profit distribution. Holding a DevioLab Score of 70.10 and ranking second for the asset, the model offers a disciplined framework for systematic traders. Its multi-day holding structure (median 153.50 hours) effectively bridges short-term 15-minute signal accuracy with medium-term trend capture. For quantitative traders and automated trading agents seeking high-probability swing exposure on BX equity, the strategy represents a historically verified model with proven multi-year reliability.
9. Data scope and methodology
The analytical insights presented in this report are based strictly on backtested, simulated historical trade data generated between September 16, 2019, and September 25, 2026. The evaluation covers 136 completed closed trades executed on 15-minute historical equity data for BX. All return percentages, holding durations, win rates, and drawdown figures reflect historical closed transactions without accounting for slippage, broker commissions, or borrow fees. Historical performance results are hypothetical and provided for quantitative research purposes only; past simulated returns do not guarantee future live trading results.