CPRT · Copart, Inc.
Stock Universe · 全球股票市场CPRT · CPRT Quantitative Strategy Analysis: Low-Frequency Position Architecture and Controlled Drawdown Dynamics in Automated Stock Trading
An exhaustive statistical evaluation of the primary algorithmic trading model for CPRT · CPRT demonstrates an exceptional historical win rate combined with asymmetric payoff distribution and minimal equity drawdowns across a 7.07-year evaluation window. Operating on a 15-minute timeframe, the strategy generated a cumulative return of +1030.93% (+90.73% annualized) across 26 completed trades between September 2019 and October 2026. Characterized by long holding durations averaging 966.20 hours (approximately 40.26 days) and a low trading frequency of 3.68 trades per year, the model achieved a win rate of 88.46% and a Profit Factor of 7.50. Maximum drawdown remained strictly constrained at 4.13%, reflecting disciplined trade management. While the top three winning trades account for 44.66% of gross historical gains, positive performance spans every backtested year, demonstrating structural consistency suitable for implementation in automated trading bots, trading robots, and quantitative trading agents.
分析日期： 09.10.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The quantitative model evaluated here represents the primary algorithmic decision logic for CPRT · CPRT on a 15-minute chart interval. Over an extended backtested history of 7.07 years spanning from September 16, 2019, to October 9, 2026, the strategy generated a cumulative return of +1030.93%, equivalent to an annualized gain of 90.73%. Out of 26 total completed trades, the model recorded 23 winning positions and 3 losing positions, establishing an exceptional win rate of 88.46%. This statistical efficiency earns the model a DevioLab score of 88.29, ranking it number 1 among evaluated strategies for the CPRT ticker. Designed to capture macro trends while utilizing 15-minute granularity for precision execution, the strategy serves as a potential logic core for automated trading bots, trading robots, and algorithmic trading agents executing equity strategies.
Trading rhythm and position duration
Despite operating on a 15-minute bar interval, the strategy exhibits the execution rhythm of a multi-week position trading system rather than an intraday framework. The historical data reflect an average trading frequency of just 3.68 trades per year, or 1.13 trades per active month. Position holding periods are notably extended, with an average holding duration of 966.20 hours (approximately 40.26 days) and a median holding duration of 397.38 hours (roughly 16.56 days). The divergence between the average and median holding times indicates that while many trades resolve within a few weeks, a subset of positions remains open significantly longer to extract extended trend movement. Inter-trade spacing is similarly deliberate, averaging 96.45 days between exits with a median of 70.18 days. This low-velocity trading rhythm means automated trading agents executing this logic experience substantial inactive periods, prioritizing signal quality and patience over high turnover.
Quality of historical results
The overall profitability of the strategy is characterized by a favorable balance between win rate and average gain per trade. The average return per completed trade stands at +10.86%, significantly surpassing the median trade return of +5.45%. This relationship highlights a right-skewed return distribution where regular moderate gains are complemented by occasional substantial winners. The single best trade produced a gain of +71.92%, whereas the worst trade resulted in a loss of -4.13%. The overall Profit Factor reaches 7.50, demonstrating that historical gross profits outweighed gross losses by a 7.5-to-1 ratio. An examination of profit concentration reveals that the three largest winning trades account for 44.66% of total gross historical profit. While this indicates a meaningful reliance on top-tier winning events, the underlying baseline of 23 total winning trades confirms that profitability is not solely an artifact of a single lucky outlier.
Risk, drawdown and losing behavior
Historical risk metrics reveal a exceptionally controlled drawdown profile relative to the strategy's total return. The maximum observed equity drawdown across the entire 7.07-year period was held to 4.13%, matching the magnitude of the single worst trade of -4.13%. This alignment suggests that drawdowns were primarily driven by isolated individual trade exits rather than compounding sequences of severe losses. The model experienced a maximum losing streak of only 2 consecutive trades, contrasted with a peak winning streak of 18 consecutive successful trades. The combination of an 88.46% hit rate and minimal drawdown behavior indicates that historical open equity exposure was effectively insulated from major tail-risk events. For quantitative trading bots operating in equity markets, this statistical structure points to low historical capital stress, though small sample sizes always require prudent risk allocation.
Behavior through time and yearly stability
A detailed review of yearly performance demonstrates remarkable return stability across varying market regimes from 2020 through 2026. Every calendar year produced positive net cumulative returns. In 2020, the strategy completed 3 trades with a 100% win rate, yielding +103.05%. Subsequent years maintained perfect hit rates alongside moderate trade counts: 2021 generated +38.95% (2 trades, 100% win rate), 2022 achieved +37.19% (4 trades, 100% win rate), 2023 recorded +27.69% (2 trades, 100% win rate), and 2024 produced +25.82% (4 trades, 100% win rate). The first losing trades appeared in 2025, where the model recorded 5 trades with a 60% win rate and a combined gain of +23.24%. In 2026, performance recovered to 6 trades, an 83.33% win rate, and +26.35% net return. The consistency of positive annual outcomes underscores structural resilience across changing volatility conditions.
Recent period since 2024-06-01 versus full history
Examining performance in the recent sample window starting June 1, 2024, provides vital context on how the strategy has operated in current market conditions. During this recent period, 15 completed trades were recorded, producing a cumulative return sum of +75.42%. This represents a substantial portion of the total historical trade count (15 out of 26 completed trades) and highlights an increase in trade execution frequency during the final two years of the dataset compared to the sparser trading observed from 2020 to 2023. Importantly, despite the uptick in activity and the occurrence of all 3 historical losing trades within the post-2024 window (across 2025 and 2026), the recent period maintained strong overall profitability, confirming that the algorithmic logic remained effective and financially viable through recent market regimes.
Strengths and limitations
The primary analytical strength of this strategy lies in its outstanding risk-adjusted return profile, anchored by an 88.46% win rate, a Profit Factor of 7.50, and a tightly limited maximum drawdown of 4.13%. The multi-week holding duration effectively buffers trading agents from high turnover friction and noise on smaller timeframes. Conversely, the model's main analytical limitation stems from its sparse execution sample: completing 26 total trades over 7.07 years yields a small statistical sample size. Additionally, the concentration of 44.66% of gross profit in the top three trades indicates that long-term outperformance depends on remaining fully invested through infrequent, high-magnitude trend expansions. Automated execution must account for prolonged inactive intervals between signals.
DevioLab analytical conclusion
The core quantitative strategy for CPRT · CPRT stands as a high-conviction, low-turnover trading model with superior historical performance metrics. Its ranking as the number 1 strategy for CPRT on DevioLab with a score of 88.29 reflects its ability to deliver a +1030.93% cumulative historical gain while keeping drawdowns capped at 4.13%. The statistical dynamic—combining long average holding periods of 966.20 hours with long winning streaks—makes it an appealing candidate for automated trading logic requiring disciplined position management. While backtested consistency across every calendar year builds analytical confidence, practitioners deploying trading robots based on this logic should respect the small historical trade sample and maintain strict position sizing controls.
Data scope and methodology
All figures presented in this report are derived directly from backtested historical trade simulations for CPRT · CPRT on a 15-minute candle evaluation frequency between September 16, 2019, and October 9, 2026. The dataset covers 26 completed trade cycles. Recent period metrics reflect trades with exit timestamps on or after June 1, 2024. Statistical metrics assume systematic execution without manual intervention. Slippage, exchange fee structures, borrow costs, and liquidity constraints are not modeled in these raw performance figures. Historical performance metrics serve strictly as analytical measurements of past simulated strategy behavior and do not constitute investment advice or guarantees of future live trading performance.