CDNS · Cadence Design Systems, Inc.
Stock Universe · 全球股票市场CDNS · CDNS Algorithmic Strategy Analysis: High Win Rate and Right-Skewed Payoff Profile on a 15-Minute Framework
This analytical evaluation examines the core algorithmic trading strategy for CDNS stock, evaluated across a 7.03-year backtesting history from September 2019 to September 2026. Operating on a 15-minute bar frequency, the strategy achieves a DevioLab score of 84.14, earning the top rank for the ticker. Over a total sample size of 30 completed trades, the strategy delivers a cumulative return of +2393.03 percent with an exceptional 90 percent win rate (27 winning trades against 3 losing trades) and a high profit factor of 6.88. Maximum historical drawdown remains contained at 9.70 percent. However, quantitative examination reveals a highly skewed payoff structure: the average trade return of +14.75 percent contrasts sharply with a median trade return of +5.38 percent, driven by a best trade of +208.10 percent and a concentration where the top three winning trades generate 61.39 percent of gross cumulative profit. Additionally, 25 of the 30 completed trades executed after June 1, 2024, demonstrating that historical trade completion has been heavily concentrated in recent market cycles.
分析日期： 29.09.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The CDNS quantitative trading model represents a core algorithmic strategy engineered for equity trading in Cadence Design Systems (CDNS). Holding the rank of number one for the symbol with a DevioLab score of 84.14, the strategy operates using a 15-minute intraday bar interval to monitor price dynamics while executing an ultra-selective multi-week swing or position holding framework. Over the complete sample period spanning September 16, 2019, through September 25, 2026—a duration of approximately 7.03 years—the model generated a cumulative closed-trade return of +2393.03 percent, translating to an annualized figure of 394.54 percent under compounding conditions. The logic is structured to serve as the algorithmic core for automated trading bots, trading robots, or trading agents requiring strict signal filters and minimal trade turnover. A total of 30 completed trades were logged over the entire dataset, reflecting a highly conservative trigger rate designed to capture extended macro trends while ignoring minor intraday market noise.
Trading rhythm and position duration
Despite utilizing a 15-minute execution interval, the strategy operates as a low-frequency, long-duration position model rather than a high-frequency or scalping algorithm. Across the full 7.03-year observation window, the strategy recorded only 30 completed trades, which equates to an average trade frequency of 4.27 trades per year or 1.30 trades per active month. Position durations exhibit a wide dynamic range between typical trades and extreme trend-following outliers. The average holding duration stands at 1563.19 hours, or approximately 65.1 days, whereas the median holding duration is substantially lower at 348.0 hours, or exactly 14.5 days. This structural gap indicates that while typical trades settle within two to three weeks, a subset of winning trades is permitted to run uninterrupted for several months. The temporal distance between trade exits averages 45.48 days, with a median spacing of 30.99 days. For deployment within automated trading agents, this spatial cadence implies long periods of passive position holding interspersed with occasional tactical adjustments.
Quality of historical results
The strategy demonstrates robust statistical quality driven by a high win rate combined with a strong profit factor of 6.88. Out of 30 historical trades, 27 closed with positive returns, yielding a 90.0 percent hit rate against only 3 losing trades. However, a deeper examination of the payoff curve reveals a asymmetric distribution. The average trade performance of +14.75 percent diverges significantly from the median trade performance of +5.38 percent. This disparity is explained by the strategy's peak winner, which achieved a single-trade gain of +208.10 percent. Furthermore, the three largest winning trades collectively account for 61.39 percent of the strategy's total gross profits. This concentration indicates that while the strategy reliably harvests modest baseline gains on standard trades, a majority of its net total performance relies on capturing rare, runaway trend expansions. The profit factor of 6.88 reflects that historical gross profits outweighed gross losses by nearly seven to one, supported by strict loss containment on failed entries.
Risk, drawdown and losing behavior
Risk management metrics show historical discipline across the observed dataset. The peak-to-trough maximum drawdown recorded during the 7.03-year history was capped at 9.70 percent, a remarkably low figure relative to the cumulative return of +2393.03 percent. The largest single loss across all trades was limited to -8.97 percent, demonstrating that losing trades were truncated before causing structural capital erosion. The strategy experienced a maximum consecutive winning streak of 22 trades, while its maximum losing streak was restricted to just 2 consecutive trades. This asymmetry between win sequences and loss sequences minimized prolonged equity drawdowns. The tight coupling between a maximum trade loss of -8.97 percent and a overall portfolio drawdown of 9.70 percent confirms that drawdown events were largely driven by isolated trade exits rather than compounding losses across multiple simultaneous failures.
Behavior through time and yearly stability
Analyzing annual statistical outputs highlights a pronounced temporal concentration of closed trades in the latter portion of the backtest window. In 2023, the model closed 2 trades, both of which were profitable, producing a 100 percent win rate and a combined return sum of +227.98 percent, heavily boosted by long-duration position cycles. In 2024, trade activity increased to 6 completed trades, yielding 4 wins and 2 losses (66.67 percent win rate) with a cumulative gain of +52.05 percent. In 2025, performance accelerated with 12 completed trades, achieving a 100 percent win rate and a cumulative return sum of +94.34 percent. In 2026, through September 25, the model completed 10 trades with 9 wins and 1 loss (90.0 percent win rate), generating +68.00 percent. The data show that 30 out of 30 recorded trades completed between 2023 and 2026, while the years from 2019 through 2022 generated negligible closed trade events, reflecting either open long-term positions or strict signal filtering during earlier market regimes.
Recent period since 2024-06-01 versus full history
The performance window beginning June 1, 2024, represents the primary driver of total sample trade density. Out of 30 total historical trades completed since September 2019, 25 trades closed on or after June 1, 2024. During this recent window, the strategy accumulated a combined closed-trade return of +181.20 percent. The concentration of 83.3 percent of all completed trades within this recent multi-year span demonstrates that recent price behavior on CDNS aligned frequently with the model's entry and exit criteria. Comparing the recent sample to the broader dataset shows consistent win rate stability and steady capital accrual, confirming that the strategy's core performance parameters have remained effective during recent market environments.
Strengths and limitations
The primary strength of the strategy lies in its risk-adjusted trade profile, characterized by a 90.0 percent win rate, a 6.88 profit factor, and a historical maximum drawdown restricted to 9.70 percent. The ability to limit losing streaks to two trades while generating a 22-trade win streak provides operational equity stability. Conversely, the strategy exhibits two clear analytical limitations. First, the total trade sample is sparse, consisting of only 30 completed trades over 7.03 years, which increases statistical uncertainty compared to high-frequency models. Second, gross profit generation is heavily right-skewed, with the top three winning trades contributing 61.39 percent of gross gains and a best single trade of +208.10 percent creating a substantial buffer above the median trade gain of +5.38 percent. Automated trading robots executing this logic must remain in position long enough to capture rare tail-event winners to replicate historical performance.
DevioLab analytical conclusion
With a DevioLab score of 84.14 and a top ranking for CDNS, this algorithmic strategy presents a compelling model of high-conviction, low-frequency trend extraction. By combining a 15-minute bar resolution with long average holding periods of over 65 days, the system filters out market volatility while capitalizing on sustained directionality. The high win rate of 90.0 percent and constrained maximum drawdown of 9.70 percent demonstrate effective risk management. Quantitative operators deploying this logic into automated trading agents should account for the low trade frequency of 4.27 trades per year and the strong reliance on top-tier outlier trades, recognizing that historical success has depended on patience during non-trending phases and uncompromised exposure during macro trend expansions.
Data scope and methodology
All metrics reported in this analytical review are derived strictly from historical backtesting data for the CDNS strategy across the evaluation period from September 16, 2019, to September 25, 2026. Statistics are calculated exclusively from 30 completed closed trades. The 15-minute timeframe refers to the price chart resolution used for signal calculation. Performance percentages reflect simulated gross historical returns of the strategy logic and do not incorporate external factors such as live execution slippage, broker commission schedules, order queue dynamics, or live account compounding adjustments. Historical backtest performance provides quantitative insight into model mechanics but does not guarantee future operational results.