CSCO · Cisco Systems Inc.
Stock Universe · global stock marketQuantitative Analysis of CSCO 15-Minute Strategy: High-Precision Multi-Day Holding Profile with 84.62% Win Rate
This empirical evaluation analyzes the core algorithmic trading strategy for CSCO on the 15-minute chart, covering 7.06 years of historical backtested performance from September 2019 through October 2026. Demonstrating a DevioLab Score of 87.79 and holding the top rank for this asset, the model completes 104 trades with an extraordinary win rate of 84.62% and a profit factor of 7.50. Despite evaluating price data on a 15-minute timeframe, the strategy functions as a patient multi-day swing system with an average holding time of 331.20 hours (13.8 days) and an average exit pacing of 24.93 days. Gross profitability is exceptionally well-distributed, with the top three winning trades contributing just 13.46% of total gains. With a peak cumulative drawdown restricted to 15.72% across a 22-trade winning streak, this algorithmic profile serves as a robust automated trading logic framework for CSCO stock.
Analysis as of 12.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy designated as CSCO 0 +2746.31% 1TRAD-FQP8 represents the primary reference model for CSCO equities within the DevioLab framework, achieving a rank of 1 for the asset with an overall DevioLab score of 87.79. Evaluated across a 7.06-year historical window spanning September 16, 2019, to October 6, 2026, the strategy processes price information on a 15-minute bar interval (15m). Over this entire period, the strategy recorded 104 completed trades, yielding an aggregate cumulative return sum of 2,746.31% and an annualized performance metric of 308.05%. The foundational profile is defined by an exceptionally high hit rate, winning 88 of its 104 positions to achieve an 84.62% win rate alongside a remarkable profit factor of 7.50. Designed to serve as the decision logic for automated trading bots, trading robots, or quantitative trading agents, the system prioritizes selective execution over high trading frequency. It captures clean intermediate structural moves in CSCO stock without overtrading during noisy market consolidation.
Trading rhythm and position duration
Although the decision logic operates on a granular 15-minute chart timeframe, its operational rhythm is decidedly intermediate and multi-day rather than intraday or high-frequency. The strategy averages 14.74 completed trades per year, which translates to approximately 1.37 trades per active month. Position duration reveals a patient holding period: the average position duration stands at 331.20 hours (approximately 13.8 days), while the median holding period is 240.13 hours (10.0 days). This structural difference between average and median duration demonstrates that while half of all positions close within ten days, select trades remain open for several weeks to capture extended trend expansions. The spacing between trade exits is similarly measured, displaying an average of 24.93 days between completed trades and a median interval of 21.0 days. For deployment in automated trading agents or algorithmic trading bots, this cadence requires low operational turnover, reducing the exposure to market noise while allowing positions sufficient breathing room across regular market sessions.
Quality of historical results
The statistical quality of the strategy's returns is highlighted by a tight alignment between average performance metrics and individual trade distributions. The overall average trade yield is 3.60%, which closely tracks the median trade yield of 3.15%. This narrow divergence confirms that historical profitability is driven by consistent, repeatable trade outcomes rather than skewed by extreme positive outliers. The strategy's maximum upside outlier reached 23.61%, while its single worst trade resulted in a loss of 11.85%. Further evidence of structural balance is found in the gross profit concentration metric: the top three winning trades account for only 13.46% of total gross historical profit. In many trend-following architectures, a major share of return is concentrated in two or three fortunate trades; here, profit accumulation is distributed smoothly across dozens of successful positions. With 88 winning trades against 16 losses, the payout structure generates a steady equity curve, underpinned by an overall profit factor of 7.50.
Risk, drawdown and losing behavior
Risk characteristics across the 7.06-year evaluation window reflect controlled downside exposure and strong loss containment. The maximum cumulative peak-to-trough drawdown recorded by the strategy was limited to 15.72%, a notable figure given the total cumulative return sum of 2,746.31%. Downside severity on individual trades is capped effectively, as evidenced by the worst historical trade loss of 11.85%. The strategy's sequence risk is equally restrained: the longest consecutive losing streak encountered across 104 trades was just 3 trades. Conversely, the algorithm experienced an extraordinary maximum winning streak of 22 consecutive profitable trades. The combination of an 84.62% win rate, a maximum drawdown of 15.72%, and a 3-trade maximum losing streak indicates that historical equity pullbacks were temporary and shallow, allowing trading robots to navigate market regime shifts without suffering prolonged capital impairment.
Behavior through time and yearly stability
An examination of the strategy's yearly breakdown demonstrates high operational stability across diverse market environments from 2019 through 2026. In 2019, across a partial initial dataset, the model closed 4 trades with a 100% win rate and an 8.90% cumulative gain. In 2020, trade frequency expanded to 16 trades, generating an 80.98% return with 15 wins and 1 loss (93.75% win rate). Performance remained profitable in 2021 with 13 trades, 10 wins, and a 65.25% sum (76.92% win rate). The strategy navigated its most challenging environment in 2022, recording 13 trades, 7 wins, and 6 losses for a 53.85% win rate and a modest cumulative gain of 12.69%. The system recovered decisively in 2023, delivering 35.33% across 12 trades (75.00% win rate), followed by an impeccable 2024 campaign with 14 wins out of 14 trades (100% win rate) yielding 67.71%. This momentum persisted into 2025 (15 trades, 86.67% win rate, 45.80% gain) and 2026 (17 trades, 94.12% win rate, 57.69% gain). Annual trade counts remain stable between 12 and 17 trades per full calendar year, proving that performance is broadly distributed through time rather than clustered in a single speculative surge.
Recent period since 2024-06-01 versus full history
Evaluating recent performance from June 1, 2024, through October 6, 2026, provides insight into how the decision logic handles contemporary market dynamics. During this recent testing window, the strategy completed 42 closed trades, accumulating a combined return sum of 154.06%. This represents approximately 40.38% of the total 104 trades executed across the entire 7.06-year history. The concentration of 42 trades in this 28-month sample reflects a slightly higher completion rate than the historical long-term average, driven by clear market trends in CSCO during this period. The total cumulative gain of 154.06% during this recent timeframe confirms that the quantitative edge has remained fully intact in recent market conditions, matching or exceeding the structural baseline established during earlier backtested years.
Strengths and limitations
The primary structural strength of this algorithmic strategy lies in its high statistical reliability, characterized by an 84.62% win rate, a 7.50 profit factor, and a remarkably low concentration of profits among top winners (13.46%). Its multi-day holding structure (median 240.13 hours) shields automated trading agents from rapid intraday market noise while capturing substantive price swings. The maximum historical drawdown of 15.72% provides favorable risk-adjusted parameters for systemic execution. Conversely, a potential limitation is the low absolute trade frequency, averaging only 14.74 trades per year or 1.37 per active month. Quantitative operators seeking high trade volume or constant market participation may find the prolonged inactive periods challenging. Additionally, because individual positions remain open for an average of nearly 14 days, the strategy is exposed to overnight market gaps and earnings events inherent in equity trading.
DevioLab analytical conclusion
The CSCO 15-minute core strategy earns its rank 1 status and DevioLab score of 87.79 through a compelling balance of high hit rate, controlled drawdowns, and exceptional return dispersion. Rather than relying on fragile scalping techniques or high-leverage turnover, the strategy uses its 15-minute chart resolution to execute a selective, multi-day swing methodology. With 88 wins out of 104 trades, a modest maximum drawdown of 15.72%, and consistent annual profitability spanning from 2019 to 2026, the strategy offers a resilient decision logic framework suitable for integration into automated trading bots, trading robots, and quantitative portfolio architectures.
Data scope and methodology
The statistics analyzed in this document are derived from historical backtested simulations of closed trades for CSCO on a 15-minute interval between September 16, 2019, and October 6, 2026. All percentage figures represent closed-trade outcomes calculated strictly within the simulated strategy engine. Performance statistics do not account for variable exchange fees, execution slippage, interest rates, or market impact, nor do they reflect real-time live trading account performance. The historical start date marks the beginning of the strategy dataset and should not be interpreted as the asset's creation or listing date. Past simulated performance is provided for quantitative analysis and logical verification only and does not constitute financial advice or guarantee future trading results.