CTSH · Cognizant Technology Solutions Corp.
Stock Universe · mercado global de açõesAlgorithmic Strategy Analysis: CTSH 15-Minute Model Delivers 81.08% Win Rate and Structural Payoff Balance
This analytical report provides an exhaustive quantitative evaluation of the top-ranked algorithmic trading strategy for CTSH on the 15-minute timeframe. Spanning a 7.04-year evaluation window encompassing 111 completed trades, the strategy demonstrates an impressive 81.08% historical win rate alongside a cumulative closed-trade gain of +1772.67%. Designed as decision logic for automated trading bots and quantitative agents, the model operates on a low-frequency, multi-day swing framework with an average position duration of 211.58 hours. Despite its exceptional hit rate and robust gain dispersion—where the top three winning trades account for only 12% of gross profit—a profit factor of 1.25 reveals an underlying structural asymmetry between loss severity and average gains. This study details the strategy's temporal consistency, drawdown behavior, and recent historical performance.
Análise em 13.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The algorithmic strategy designated for CTSH operates on a 15-minute chart resolution within stock market equities, functioning as systematic decision logic engineered for deployment in automated trading bots, trading robots, and quantitative trading agents. Carrying a DevioLab Score of 79.63, the strategy holds the number one rank among evaluated models for the CTSH asset ticker. The strategy's primary backtested dataset spans 7.04 years, starting on September 16, 2019, and running through October 1, 2026. Over this multi-year observation window, the model completed 111 closed trades, accumulating a combined gross trade return sum of +1772.67%, which converts to an annualized backtested figure of 163.02%. The core operational profile reflects a highly selective swing-trading engine that prioritizes trade accuracy over trade quantity, seeking to filter out intraday noise on the 15-minute price series in favor of broader multi-day moves.
Trading rhythm and position duration
Despite utilizing a 15-minute chart interval for signal generation, the strategy's execution pattern diverges significantly from high-frequency intraday trading. Over the full 7.04-year sample, the model executed an average of 15.76 trades per year, which translates to approximately 1.61 trades per active month. This sparse trade volume illustrates that the automated decision engine acts with substantial filter constraint, remaining idle for extended periods until specific statistical parameters are satisfied. Position holding times further reinforce this swing-trading profile. The average position holding time is 211.58 hours (roughly 8.8 calendar days), while the median holding time stands at 144.00 hours (6.0 calendar days). Spacing between exit events is similarly stretched, exhibiting an average of 23.21 days between exits and a median of 17.49 days. The variance between median and average holding metrics indicates that while most trades conclude within a single calendar week, a subset of trades remains open for multiple weeks to capture prolonged price trends. Quantitative trading bots deploying this strategy must therefore be calibrated for patience, as trade executions occur infrequently relative to the underlying intraday bar frequency.
Quality of historical results
The quality of historical returns generated by this strategy is characterized by exceptional accuracy paired with a moderate profit factor. Out of 111 total closed trades, 90 yielded positive returns while 21 resulted in losses, establishing a backtested win rate of 81.08%. The statistical expectations of individual trades remain positive across both central tendencies, with an average trade return of +3.00% and a median trade return of +3.33%. A crucial strength of the distribution is its profit concentration metric: the top three winning trades generated only 12% of total gross profit. This low concentration confirms that historical performance was driven by consistent repeat wins across the sample rather than an artificial reliance on a small cluster of extreme upside outliers. However, the overall payoff structure exhibits a notable asymmetry between upside targets and downside exits. The single best trade achieved a gain of +19.68%, while the single worst trade experienced a loss of -20.37%. Because the magnitude of adverse trades substantially exceeds the average winning trade return, the overall profit factor sits at 1.25. This metric demonstrates that while win frequency is exceptionally high, losing trades extract a sizeable proportion of cumulative gross gains.
Risk, drawdown and losing behavior
Analyzing risk characteristics requires evaluating how trade losses cluster and impact account equity. The historical maximum drawdown recorded for this strategy is 20.37%, a figure that matches almost exactly with the worst single trade loss of -20.37%. This close alignment reveals a critical structural detail: historical drawdowns were largely driven by individual severe trade exits rather than compounding series of consecutive losses. Indeed, the strategy demonstrates remarkable loss containment in terms of sequence, recording a longest losing streak of just 2 consecutive trades across the entire 7.04-year sample. In contrast, the longest winning streak reached 17 consecutive trades, highlighting the algorithm's ability to navigate prevailing market conditions without encountering prolonged regimes of failing signals. However, because single-trade downside exposure can reach -20.37%, trading agents operating this strategy experience risk concentration at the individual position level rather than through multi-trade decay. Managing position sizing and understanding individual trade variance are therefore essential operational considerations when analyzing this risk profile.
Behavior through time and yearly stability
An examination of the yearly breakdown shows consistent positive trade performance across all evaluated calendar years. In 2019, during the initial partial period of 3 trades, the model registered 1 win and 2 losses (+1.27% sum, 33.33% win rate). Operational performance normalized rapidly in 2020, delivering 21 completed trades with 18 wins and 3 losses (+90.39% sum, 85.71% win rate). Subsequent years demonstrated ongoing stability: 2021 generated 13 trades with 11 wins (+31.48% sum, 84.62% win rate); 2022 generated 16 trades with 13 wins (+47.11% sum, 81.25% win rate); and 2023 yielded a flawless 100% win rate across 10 completed trades (+44.83% sum). In 2024 and 2025, trade activity remained steady at 14 completed trades per year, each producing 10 wins and 4 losses (71.43% win rate) with net trade sums of +33.33% and +28.42% respectively. The model accelerated trade output in 2026, logging 20 trades with 17 wins (+56.20% sum, 85.00% win rate). Across seven consecutive calendar periods, the strategy consistently produced positive gross trade sums, proving that its quantitative edge was active across changing market regimes rather than isolated to a single favorable year.
Recent period since 2024-06-01 versus full history
Evaluating recent performance since June 1, 2024, provides vital context regarding the strategy's current behavioral trends. In this recent window, the strategy completed 43 closed trades, generating a cumulative trade return sum of +113.90%. Comparing this window against the full history highlights a notable increase in trade realization velocity. While the historical baseline average across 7.04 years sits at 15.76 trades per year, the post-June 2024 period recorded 43 completed trades in approximately two years, indicating a higher frequency of signal trigger conditions during recent market cycles. Importantly, the recent sub-sample contributed substantial total gains without showing signs of structural decay. Generating +113.90% across 43 trades yields an average trade gain consistent with the long-term historical baseline of +3.00%. This alignment suggests that the algorithm's decision rules have maintained statistical continuity under modern price action on CTSH.
Strengths and limitations
The primary strength of this CTSH trading strategy lies in its outstanding win rate of 81.08% combined with exceptional profit distribution stability, where no small set of trades dominates total performance. The strategy's ability to maintain an unbroken sequence of positive calendar-year net results over seven years, alongside a maximum losing streak of only two trades, reflects strong directional reliability. For automated execution environments, low trade frequency (15.76 trades per year) inherently reduces operational trade activity. Conversely, the strategy's primary limitation is its payoff ratio asymmetry. With a worst single trade of -20.37% and an average trade return of +3.00%, a single outlier loss can offset multiple typical winning trades, capping the historical profit factor at 1.25. Additionally, holding positions for an average of 211.58 hours exposes capital to overnight gap risk and multi-day asset volatility, requiring robust risk tolerance despite the high win rate.
DevioLab analytical conclusion
With a DevioLab Score of 79.63 and a rank of 1 for the CTSH ticker, this 15-minute algorithmic strategy presents a compelling quantitative profile for swing-oriented automated trading systems. The model effectively balances high precision—evidenced by an 81.08% historical win rate and a maximum consecutive loss streak of 2 trades—with consistent multi-year return delivery. Its gain generation is well-distributed, requiring no individual outlier trade to sustain performance. However, quantitative analysts and system operators must account for the structural tension between trade accuracy and tail risk: the profit factor of 1.25 reflects the statistical reality that occasional losing trades can be relatively severe. Provided that automated trading robots and risk frameworks are configured to accommodate multi-day position durations and single-trade adverse excursions, the strategy represents a highly disciplined, historically validated approach to systematic trading in CTSH.
Data scope and methodology
All figures presented in this analysis are calculated directly from backtested closed-trade simulation records for CTSH on the 15-minute timeframe between September 16, 2019, and October 1, 2026. Cumulative percentage outputs represent the unadjusted arithmetic summation of individual trade percentage returns. The data scope excludes live broker execution variables, market impact, order routing latencies, trading fees, commissions, and slippage. Recent window statistics reflect completed trades whose exit timestamp occurred on or after June 1, 2024, UTC. Historical simulated results serve strictly for analytical evaluation of strategy logic and structural mechanics; past backtested performance does not guarantee future results or live account profitability.