CPAY · Corpay Inc.
Stock Universe · global stock marketCPAY · CPAY Quantitative Strategy Analysis: High-Precision 94.74% Win Rate Driven by Multi-Week Position Duration
This analytical report evaluates the top-ranked algorithmic trading strategy for CPAY (CPAY) across a 2.39-year evaluation window from March 2024 to August 2026. Despite operating on a 15-minute chart resolution, the logic acts as a patient positional framework, generating only 19 closed trades with an average holding time of nearly 20 days (479.54 hours). The strategy delivers an extraordinary 94.74% win rate (18 wins out of 19 trades), a profit factor of 4.38, and a maximum drawdown of just 0.74%. The top three winning trades account for 36.02% of gross profit, demonstrating that historical gains are distributed across multiple trades rather than heavily skewed by a single outlier. This research provides a thorough statistical assessment of the strategy's quality, drawdown profile, temporal stability, and sample-size constraints for quantitative traders evaluating automated trading bots and execution models.
Analysis as of 09.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy designated for CPAY operates on a 15-minute timeframe within the stock market context. Over an historical backtest period spanning 2.39 years—from March 25, 2024, to August 13, 2026—the model executed 19 completed trades. Out of these 19 trades, 18 resulted in positive returns, yielding an exceptional historical win rate of 94.74%. The cumulative gain across the entire test history reached 523.07%, earning the strategy a DevioLab Score of 86.40 and the rank of number one among evaluated strategies for the CPAY ticker. Designed as potential decision logic for automated trading bots, trading robots, or quantitative trading agents, the strategy exhibits a highly selective entry mechanism that trades infrequently while seeking high-probability trend capture. Across the full 2.39-year record, the system registered only a single losing trade, establishing an exceptionally clean historical performance curve characterized by minimal equity volatility and steady capital accumulation.
Trading rhythm and position duration
Although the decision rules utilize 15-minute intraday price bars to monitor market structural developments, the trading rhythm is distinctly patient and macroeconomic in duration. The strategy averages only 7.96 trades per year, which translates to roughly 1.19 trades per active month. Once a trade is executed, positions remain open for extended durations: the average holding period is 479.54 hours (approximately 19.98 days), while the median holding period stands at 358.75 hours (approximately 14.95 days). Furthermore, the strategy displays an average interval between exits of 39.40 days and a median exit interval of 29.59 days. This wide spacing between trade closures reveals that the algorithm is far from an intraday scalper or high-frequency strategy. Instead, it uses 15-minute bar granularity to fine-tune entries and exits while riding broader swing or multi-week positional trends. The structural gap between intraday bar signals and multi-week hold times allows the strategy to filter out short-term market noise while avoiding unnecessary transaction churn.
Quality of historical results
The distribution of individual trade returns highlights a robust balance between win frequency and return magnitude. The strategy achieved an average trade return of 10.60% and a median trade return of 7.99%, indicating that positive performance is anchored by consistently strong single-trade outcomes rather than marginal fractional gains. The best individual trade recorded a gain of 29.01%, whereas the single losing trade experienced a limited decline of -0.74%. This favorable asymmetry underpins a profit factor of 4.38. Analyzing gross profit concentration reveals that the top three winning trades generated 36.02% of total historical gross profits. In many trend-following models, three trades can often account for 60% or more of cumulative profits; here, the lower concentration ratio confirms that profitability was spread naturally across the 18 winning trades. Consequently, the quality of results is not the byproduct of an isolated statistical fluke, but rather a reflection of repeatable performance across multiple distinct market cycles.
Risk, drawdown and losing behavior
Risk metrics across the backtested dataset demonstrate disciplined capital preservation. The peak-to-trough maximum drawdown observed throughout the 2.39-year window was strictly bounded at 0.74%. Remarkably, this maximum drawdown figure matches the strategy's single worst trade of -0.74%, confirming that the equity curve suffered negligible open intra-trade equity erosion or cascading closed losses. The longest winning streak reached 16 consecutive trades, while the longest losing streak was exactly 1 trade. With 18 out of 19 closed trades generating positive returns, the historical risk of prolonged drawdowns remained exceptionally low. However, quantitative analysts must evaluate this drawdown profile alongside position exposure: because the model opens positions infrequently (averaging under 8 trades per year), a significant portion of time is spent in cash or unallocated states, naturally insulating the strategy from broad market downturns during inactive phases.
Behavior through time and yearly stability
Examining the annual breakdown shows consistent profitability across each calendar year in the test period, alongside an increase in sample size over time. In 2024 (starting from March 25), the strategy closed 2 trades, both of which were winners (100% win rate), accumulating a combined closed trade return sum of 38.36%. In 2025, trade frequency increased to 8 completed trades, maintaining a 100% win rate and delivering a combined return sum of 58.54%. In 2026 (through August 13), the algorithm generated 9 closed trades—8 wins and 1 loss—yielding an 88.89% win rate and a combined return sum of 104.50%. The steady uptick in trade occurrences from 2 in 2024 to 8 in 2025 and 9 in mid-2026 demonstrates sustained activity without degradation in accuracy. The algorithm managed to deliver its highest annual return sum in 2026 despite encountering its first historical loss, underscoring the dominant weight of its winning trades relative to its minimal loss size.
Recent period since 2024-06-01 versus full history
A critical phase of evaluation focuses on performance generated since June 1, 2024. Within this recent window, all 19 closed trades recorded in the overall history occurred, totaling a cumulative sum of closed trade percentage returns of 201.40%. Comparing this uncompounded closed trade return sum of 201.40% to the full history compounded total return of 523.07% illustrates the impact of compounding individual trade percentages over time. Because all 19 historical trades were completed after June 1, 2024, the strategy's recent performance window is virtually synonymous with its primary operational record. This confirms that the model's high win rate of 94.74% and low maximum drawdown of 0.74% are direct reflections of recent market dynamics rather than obsolete historical regimes. The consistency between recent observations and full-history metrics indicates that the underlying logic remained aligned with CPAY's trading environment throughout 2024, 2025, and 2026.
Strengths and limitations
The primary strength of this CPAY strategy lies in its outstanding trade precision, evidenced by an 18-to-1 win-loss ratio, a 94.74% hit rate, and a strong profit factor of 4.38. Furthermore, its maximum drawdown of 0.74% represents exceptional historical capital protection, while a moderate profit concentration of 36.02% across the top three winners highlights balanced contribution across positions. However, a key limitation stems from the small overall sample size of 19 trades across 2.39 years. In quantitative backtesting, small sample sizes carry higher statistical error margins, meaning future performance may experience variance or regression toward the mean. Additionally, the long holding times (averaging nearly 20 days per position) require patient execution and expose capital to overnight and weekend market gaps, which may lead to execution slippage or wider realized losses in live trading environments than those observed in backtested historical models.
DevioLab analytical conclusion
With a DevioLab Score of 86.40 and the rank of number one for CPAY, this algorithmic framework represents a highly efficient swing-to-position trading model. The strategy successfully combines 15-minute chart inputs with multi-week holding times to capture substantial price moves while ignoring minor intraday fluctuations. Its combination of a 94.74% historical win rate, a 10.60% average gain per trade, and a restricted 0.74% maximum drawdown makes it an attractive candidate for deployment as automated decision logic in trading bots or autonomous trading agents. Nevertheless, quantitative risk managers must account for the limited sample of 19 historical trades. While the statistical shape is remarkably strong, ongoing monitoring is essential to ensure that future performance maintains this high degree of precision as new market regimes unfold.
Data scope and methodology
This analysis is based strictly on historical backtested data for the CPAY token/stock covering the period from March 25, 2024, to August 13, 2026 (2.39 years). All reported metrics, including the 523.07% total return, 94.74% win rate, 4.38 profit factor, and 0.74% maximum drawdown, were calculated from 19 completed trades executed on 15-minute candle intervals. The methodology measures closed trade outcomes and does not account for live order book depth, execution slippage, broker commissions, or borrow costs. Historical performance generated through backtesting is purely illustrative and does not guarantee future operational returns or shield against market risk. This document is intended solely for educational and quantitative research purposes and does not constitute financial advice.