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Stock Universe · 全球股票市场CB · Quantitative Strategy Analysis: High Hit-Rate Swing Automation with Low Historical Drawdown
This analytical report evaluates a top-ranked algorithmic trading strategy executed on CB stock across a 15-minute price timeframe. Spanning a seven-year historical dataset from September 2019 through September 2026, the quantitative strategy recorded 107 completed trades, achieving an 85.05% win rate and a profit factor of 5.0. Designed to serve as the core decision engine for automated trading bots and trading robots, the framework combines patient multi-day position holding with low transaction frequency. The performance profile is characterized by high trade selectivity, a maximum historical drawdown capped at 6.79%, and minimal profit concentration, with its top three winning trades contributing just 15.69% of total gross gains. This study details the statistical relationships governing the strategy's historical payout structure, exposure cadence, yearly consistency, and recent execution performance.
分析日期： 26.09.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The CB 15-minute quantitative framework represents the primary rank-1 strategy within DevioLab's coverage for CB stock, attaining an overall DevioLab score of 89.51. Operating on intraday 15-minute price bars over a full seven-year sample period from September 16, 2019, to September 14, 2026, the strategy relies on selective signal generation rather than high-frequency execution. Over the entire sample, the system generated 107 closed trades, translating to an average annual output of 15.29 trades or approximately 1.55 trades per active month. This measured pace positions the framework as an automated swing-trading model designed for deployment within trading bots and algorithmic trading agents that prioritize high-conviction entries over excessive market exposure. Across 107 completed trade events, 91 were closed in profit against 16 losing trades, producing an overall win rate of 85.05%. Total cumulative trade profit across the history reached 1384.57%, corresponding to an annualized gain metric of 146.20%. The strategy demonstrates an exceptionally high profit factor of 5.0, meaning historical gross profits exceeded gross losses by a factor of five to one. These metrics illustrate an automated model built on tight risk control and broad-based win generation.
Trading rhythm and position duration
Although entries are anchored to 15-minute candle formations, the strategy's position holding times reflect a distinct multi-day swing approach rather than intraday scalping. The average holding duration per trade stands at 233.51 hours (approximately 9.73 days), while the median holding time is 164.50 hours (roughly 6.85 days). The divergence between average and median holding durations indicates that while a typical position remains open for nearly a week, a subset of trades extends into multi-week trends to capture broader price movements. Spacing between trade exits is similarly extended: the average duration between completed trade exits is 23.93 days, with a median spacing of 18.48 days. This reveals that the algorithm operates with prolonged inactive intervals, remaining flat for extended periods while awaiting specific technical alignment in CB stock. For automated trading agents and execution scripts, this low-frequency, medium-duration rhythm reduces exposure to market noise and intraday choppy conditions, ensuring that trading robots are engaged only during structured price transitions.
Quality of historical results
Evaluating the structural balance of closed trades reveals that the strategy's profitability is fundamentally broad-based rather than reliant on random windfalls. The average return across all 107 trades is +2.83%, while the median trade return is +2.02%. The close proximity between average and median trade outcomes demonstrates that positive trade outcomes are consistently distributed across the entire sequence of trades rather than heavily skewed by a few extreme outliers. The system's single best trade recorded a gain of +30.05%, whereas the worst individual loss was limited to -6.52%. A critical metric illustrating structural stability is the concentration of gross profit: the top three winning trades collectively generated 15.69% of the strategy's total gross profit. Because more than 84% of total gross gains originated outside the top three trades, the historical return curve demonstrates high internal resilience. With an 85.05% hit rate and a payoff structure where average gains comfortably exceed the worst single loss, the strategy relies on repeatable edge propagation across consecutive cycles.
Risk, drawdown and losing behavior
Risk containment is one of the standout characteristics of this quantitative framework. Across seven years of backtested execution on CB stock, the maximum peak-to-trough drawdown was contained at 6.79%. This remarkably low drawdown figure is particularly notable when contrasted with the cumulative closed-trade return of 1384.57%, indicating a high degree of capital preservation during unfavorable market regimes. The losing streak behavior further highlights this stability: the strategy experienced a maximum consecutive losing streak of just 2 trades, compared to a maximum winning streak of 15 consecutive profitable trades. Across the 16 losing trades recorded in seven years, the worst single closed position registered a loss of -6.52%. The combination of a high win rate (85.05%), short losing streaks, and bounded tail risk prevents equity compounding from suffering severe drawdowns. For trading robots operating under automated risk management constraints, this statistical profile suggests minimal equity curve distress and low historical exposure to cascading losses.
Behavior through time and yearly stability
An examination of the yearly breakdown demonstrates consistent positive performance across every calendar year analyzed from 2019 through 2026. In 2019 (partial year from September), the strategy completed 6 trades with an 83.33% win rate and +4.71% net gain. In 2020, trade frequency rose to 15 trades, yielding 14 wins (93.33% win rate) and a net sum of +71.34%. Year 2021 registered 15 trades with 11 wins (73.33% win rate) and +36.10% gain, marking the lowest annual win rate in the dataset but remaining strongly positive. Performance continued smoothly into 2022 (17 trades, 82.35% win rate, +45.72% sum) and 2023 (12 trades, 83.33% win rate, +25.89% sum). In 2024, 11 trades produced 9 wins (81.82% win rate) and +32.14% gain. Results remained robust in 2025 (15 trades, 86.67% win rate, +49.60% sum) and 2026 (16 trades, 93.75% win rate, +37.64% sum). The annual trade count remained remarkably steady between 11 and 17 trades per full year, reflecting stable signal filtering across varying stock market environments.
Recent period since 2024-06-01 versus full history
Analyzing performance from June 1, 2024, through the end of the history window provides a dedicated perspective on recent strategy behavior. During this recent period, the system closed 36 trades, generating a cumulative return sum of +99.25%. Comparing this to the full historical trade count of 107 trades shows that approximately 33.6% of all recorded strategy exits occurred in this recent window. The elevated trade frequency in the recent sample—averaging over 1.5 trades per month—corresponds with strong profitability, generating nearly 100 percentage points of cumulative gain without degrading performance quality. The average trade gain during the recent window remained consistent with the multi-year mean of +2.83%. This ongoing stability confirms that the algorithm's decision logic retained its quantitative efficacy in more recent market structures, providing active verification that the model's core edge did not experience parameter decay or structural degradation.
Strengths and limitations
The core strength of this CB stock strategy lies in its combination of a high hit rate (85.05%), high profit factor (5.0), and small maximum drawdown (6.79%). The low concentration of gross profit in top winners (15.69%) confirms that profitability is supported by recurring wins rather than single market anomalies. Short losing streaks (maximum 2 consecutive losses) minimize prolonged equity drawdown stress. Conversely, the primary limitation of the strategy is its relatively low trade volume. With only 107 completed trades across seven years (averaging 15.29 trades per year), sample size statistical confidence grows more slowly compared to higher-frequency models. Additionally, because average holding durations approximate 9.73 days, capital can remain locked in active positions for multi-day periods, requiring patient execution parameters. Automated trading agents deployed on this logic must account for extended idle windows between signal triggers.
DevioLab analytical conclusion
With a DevioLab score of 89.51 and a rank of 1 for CB stock, this 15-minute quantitative strategy presents a well-balanced historical profile optimized for systematic swing trading. The strategy balances disciplined market entry with high outcome precision, converting 85.05% of its closed positions into positive trades while maintaining a strict upper limit on historical drawdown. Its structural metrics—including a median trade return of +2.02%, a profit factor of 5.0, and positive net gains across every single calendar year from 2019 through 2026—highlight a robust quantitative design. The high gain contribution during the post-June 2024 window (+99.25% across 36 trades) further confirms ongoing operational relevance. For traders seeking logic suitable for automated trading bots, the strategy offers an attractive blend of steady growth, low trade churn, and strict downside containment.
Data scope and methodology
This analysis is derived exclusively from the backtested closed-trade performance dataset for CB stock under the CB 0 +1384.57% 1TRAD-ZBU2 strategy model across the 15-minute timeframe. The historical dataset covers completed trades between September 16, 2019, and September 14, 2026. All reported percentage gains, drawdowns, holding durations, win rates, and annual summaries reflect closed-trade backtest outputs generated without slippage, commission, or execution cost adjustments unless explicitly noted. History start dates represent the beginning of the strategy evaluation window and do not correspond to asset issuance or listing dates. These historical findings serve analytical and educational research purposes and do not constitute financial advice, market forecasts, or trade recommendations.