CDW · CDW Corporation
Stock Universe · 全球股票市场Quantitative Analysis of the CDW · CDW 15-Minute Algorithmic Strategy: Low-Frequency Position Trading for Automated Execution
An analytical evaluation of the core 15-minute algorithmic strategy for CDW reveals a disciplined, selective trading profile characterized by high hit rates and extended position holding times. Over a historical evaluation window spanning more than seven years from September 2019 through September 2026, the strategy recorded 104 closed trades with an overall win rate of 78.85% and a profit factor of 3.75. Designed as automated decision logic suitable for execution by trading bots and trading robots, the algorithm operates at a low frequency of approximately 14.78 trades per year, holding positions for an average of 335.66 hours (over 14 days). Rather than relying on speculative high-frequency scalping, the strategy captures substantial multi-day price swings while maintaining a remarkably balanced profit distribution where the top three winning trades account for only 16.04% of gross historical gains. Despite experiencing a brief performance contraction in 2024, the strategy demonstrated historical resilience, logging 35 trades totaling +92.88% in cumulative gains since June 2024.
分析日期： 29.09.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The CDW 15-minute algorithmic trading strategy represents a quantitative decision framework engineered specifically for the stock market asset CDW. Evaluated across a 7.04-year historical sample from September 16, 2019 to September 29, 2026, the strategy completed 104 trades, achieving an overall win rate of 78.85% through 82 winning transactions against 22 losses. The strategy achieved a cumulative closed-trade return sum of +2489.84%, translating to an annualized return metric of 121.63% and a profit factor of 3.75. Recognized as the rank-1 core strategy for CDW on DevioLab with an analytical score of 79.50, the algorithm utilizes 15-minute price action structure not for rapid day trading, but to pinpoint entry and exit parameters within multi-day trends. The decision rules are optimized to serve as the core logic for automated trading bots and algorithmic agents, providing a programmatic systematic approach to equity swing trading.
Trading rhythm and position duration
Despite relying on 15-minute candlestick data for signal generation, the strategy demonstrates a patient trading rhythm. Across the seven-year sample, trade frequency averaged 14.78 completed trades per year, or approximately 1.63 trades per active month. The time between trade exits reinforces this measured pace, with an average interval of 24.93 days and a median interval of 19.82 days between completed trades. Position duration statistics confirm an intermediate-term holding profile: the average holding duration reached 335.66 hours (approximately 14 days), while the median holding duration stood at 220.75 hours (around 9.2 days). This dual structure reveals that the automated trading robot uses micro-structure timeframe data to evaluate setups, but allows positions to unfold over several weeks. Consequently, the strategy avoids hyperactive churn and operational stress, focusing capital on persistent directional swings.
Quality of historical results
Evaluating the distribution of historical returns highlights high statistical stability across trade outcomes. The average trade yield of +3.60% aligns closely with the median trade yield of +3.50%, indicating that historical performance was driven by repeatable statistical edge rather than skewed by extreme single-event outliers. The single best trade achieved a gain of +29.23%, whereas the worst trade registered a loss of -12.76%. Furthermore, the top three winning trades contributed only 16.04% of total gross historical profits. This low concentration metric confirms that strategy gains were broadly distributed across dozens of individual transactions throughout the seven-year timeline. A profit factor of 3.75 further underscores payoff quality, indicating that gross historical wins exceeded gross historical losses by nearly four times.
Risk, drawdown and losing behavior
Risk metrics across the sample present a controlled drawdown profile relative to cumulative return generation. The maximum peak-to-trough drawdown recorded across the 7.04-year history was 20.50%. The strategy demonstrated strong streak resilience, achieving a maximum consecutive winning sequence of 13 trades, compared to a maximum consecutive losing streak of 4 trades. With a total of 22 losses out of 104 trades, losing phases remained constrained in frequency. The maximum trade loss of -12.76% demonstrates that downside events, while material when they occur, were contained within boundaries that preserved overall capital structure. The interaction between a 78.85% hit rate and a 20.50% maximum drawdown suggests that losing series were typically absorbed and recovered without causing systemic equity compounding failure.
Behavior through time and yearly stability
A year-by-year examination reveals structural performance consistency with localized periods of market adaptation. The strategy generated positive aggregated returns in seven out of eight calendar years evaluated. In 2019, 5 trades yielded +23.53% with a 100% win rate. In 2020, performance expanded to 14 trades yielding +101.58% (85.71% win rate). The year 2021 delivered +52.01% across 13 trades (92.31% win rate), followed by 2022 with +36.09% across 16 trades (68.75% win rate) and 2023 with +63.12% across 15 trades (86.67% win rate). A temporary contraction occurred in 2024, where 13 trades resulted in a net return sum of -6.75% as the win rate dipped to 61.54%. However, historical performance rebounded in 2025 (+26.49% sum across 13 trades, 69.23% win rate) and accelerated in 2026 (+78.17% sum across 15 trades, 80.00% win rate). Annual trade counts remained consistent between 13 and 16 trades per full calendar year, proving consistent signal output across varying market regimes.
Recent period since 2024-06-01 versus full history
Isolating the recent window beginning June 1, 2024 provides crucial insight into recent algorithm performance. During this period, the strategy executed 35 completed trades, generating a cumulative return sum of +92.88%. Because this recent window accounts for over one-third of all 104 historical trades, it offers a robust evidence sample. The strong positive performance since mid-2024 demonstrates that the algorithm successfully navigated beyond its 2024 annual drawdown phase (-6.75%), re-establishing alignment with broader historical norms. Comparing recent trade density with full-history baseline values shows that signal frequency remained active while maintaining the core statistical traits seen across the complete 2019–2026 testing horizon.
Strengths and limitations
The primary structural strengths of this CDW strategy include its high win rate (78.85%), strong profit factor (3.75), low reliance on profit concentration (16.04% top-three profit share), and high symmetry between average (+3.60%) and median (+3.50%) trade outcomes. Its low operational trade frequency (~14.78 trades per year) makes it highly manageable for implementation in automated trading agent setups. Conversely, limitations include multi-week position holding durations (average 335.66 hours) which require persistent capital commitment and exposure to overnight market risk. Additionally, the strategy experienced a 20.50% historical maximum drawdown and a negative annual performance phase in 2024. Finally, statistical simulation models do not account for real-world execution factors such as order slip, variable broker fees, or market liquidity fluctuations.
DevioLab analytical conclusion
Achieving the top rank for CDW on DevioLab with a composite score of 79.50, this core 15-minute strategy delivers a balanced quantitative model for stock trading automation. The combination of high hit probability, low outlier reliance, and multi-year trade stability highlights a robust underlying edge. While multi-week holding times demand disciplined capital allocation and risk management during market drawdowns, the statistical evidence confirms that the strategy's decision rules provide a structurally sound foundation for quantitative research and algorithmic trading robot integration.
Data scope and methodology
This analysis is based on historical backtested data for asset CDW on a 15-minute timeframe, covering the period from September 16, 2019 to September 29, 2026 (7.04 years). All figures reflect closed simulated trades executed according to the strategy's programmatic logic. Statistical results are calculated without deducting trading commissions, slippage, borrowing fees, or tax liabilities. Historical performance metrics do not guarantee future performance outcomes. This quantitative report is provided strictly for educational and analytical purposes and does not constitute financial or investment advice.