CRL · Charles River Laboratories International, Inc.
Stock Universe · global stock marketCRL · CRL Algorithmic Trading Strategy Analysis: High Win Rate Dynamics, Multi-Week Holding Structure, and Long-Term Performance Profile
A comprehensive quantitative analysis of the algorithmic trading strategy for CRL across a 7.06-year backtest dataset spanning September 2019 to October 2026. Designed for implementation across automated trading bots, trading robots, and quantitative trading agents, the strategy operates on a 15-minute execution grid while maintaining multi-day to multi-week exposure. Demonstrating an impressive 77.88% win rate across 113 completed trades, a maximum winning streak of 21 consecutive trades, and positive aggregate returns in every single evaluated calendar year, the strategy showcases exceptional directional accuracy. However, its overall profit factor of 1.25 highlights a structural payoff asymmetry where drawdown risk (-25.27% peak-to-trough) and individual loss magnitudes absorb a substantial portion of gross profits. This analysis evaluates the strategy's statistical architecture, holding duration, return dispersion, and recent multi-year performance.
Analysis as of 11.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy under evaluation targets CRL equity data over a 7.06-year historical backtesting window from September 16, 2019, to October 8, 2026. Positioned as the second-ranked strategy for CRL on DevioLab with a DevioLab score of 75.83, the quantitative architecture is structured on a 15-minute candle interval. Over the full test horizon, the strategy generated 113 completed trade cycles, achieving a aggregate closed trade profit sum of 7222.32% and an annualized figure of 549.31%. Designed to serve as underlying execution logic for automated trading bots, trading robots, and algorithmic trading agents, the strategy uses high-resolution price data to make deliberate, low-frequency position entries. Rather than engaging in hyperactive churn, the logic filters market noise to capture larger directional swings, balancing trade frequency against structural market trends.
Trading rhythm and position duration
Despite utilizing a 15-minute execution grid, the strategy exhibits a distinctly patient exposure cadence. Over the 7.06-year historical sample, the system completed 113 trades, translating to an average of approximately 16 trades per year or 1.55 trades per active month. Position holding times reflect a multi-day to multi-week swing trading profile rather than intraday scalping. The average holding duration stands at 347.06 hours (roughly 14.46 days), while the median holding duration is 192.25 hours (approximately 8.01 days). The gap between the median and average holding times indicates that while half of the positions resolve within eight days, a subset of trades remains open for extended periods to capture longer-term price moves. Furthermore, the average time between trade exits is 21.71 days, with a median spacing of 18.99 days. This temporal spacing confirms that automated trading agents deploying this strategy operate with low turnover, allowing individual trades sufficient time to mature without exposing the portfolio to excessive transaction frequency.
Quality of historical results
The statistical profile of the strategy's return distribution is characterized by an exceptionally high win rate offset by asymmetrical loss magnitudes. Out of 113 total completed trades, 88 ended in profit while 25 resulted in losses, yielding a win rate of 77.88%. The average trade return across all positions reached +4.49%, closely mirroring the median trade return of +3.92%. This tight alignment between average and median returns indicates that positive performance was generated consistently across the trade sample rather than being artificially skewed by a handful of extreme statistical outliers. Indeed, the best individual trade achieved a gain of +36.98%, and the top three winning trades collectively accounted for only 14.89% of gross profits. This low profit concentration demonstrates that the strategy's profitability relies on a broad base of consistent wins rather than rare windfall trades. However, despite the 77.88% hit rate, the system produced a modest profit factor of 1.25. This ratio reveals that while winning trades occur frequently, losing trades carry larger average return impacts, absorbing a substantial share of total gross gains.
Risk, drawdown and losing behavior
An examination of risk metrics reveals how losing trades affect the strategy's equity curve. The maximum historical drawdown reached -25.27%, which corresponds exactly to the worst individual closed trade performance of -25.2685%. This alignment indicates that peak equity declines were primarily driven by isolated, significant trade losses rather than protracted sequences of consecutive losing trades. In fact, the strategy's longest losing streak was limited to just 2 trades, whereas its longest winning streak spanned an extraordinary 21 consecutive trades. The contrast between an impressive 21-trade winning run and a maximum losing streak of 2 trades underscores the model's high directional precision. Nevertheless, the presence of a -25.27% single-trade loss illustrates that when market movement invalidates a trade thesis, the resulting downside can be severe relative to the +3.92% median gain. Quantitative trading bots executing this logic must be engineered to withstand periodic sharp drawdowns even while enjoying long stretches of uninterrupted winning trades.
Behavior through time and yearly stability
A multi-year breakdown from 2020 through 2026 demonstrates consistent calendar-year profitability across varying market conditions. In 2020, the strategy completed 13 trades with an 84.62% win rate, accumulating a +117.71% trade return sum. Activity increased in 2021 to 19 trades, delivering an 89.47% win rate and a +97.02% return sum. The strategy experienced its most challenging environment in 2022, where the win rate dropped to 57.14% across 14 trades, but it nonetheless maintained positive overall territory with a +7.82% return sum during a broad market downturn. Performance rebounded in 2023 with 16 trades, an 81.25% win rate, and +41.62% accumulated return. In 2024, the system logged 18 trades with an 83.33% win rate (+73.13% sum), followed by 2025 with 17 trades, a 64.71% win rate, and +106.35% return sum. Finally, through the partial data of 2026, 16 trades yielded an 81.25% win rate and +63.88% sum. Across seven distinct calendar years, the strategy achieved positive trade sums in every single year, proving remarkable temporal stability.
Recent period since 2024-06-01 versus full history
Analyzing performance in the recent period starting June 1, 2024, provides vital context regarding the strategy's ongoing relevance. Within this recent evidence window, the strategy completed 44 closed trades, generating a cumulative trade return sum of +215.71%. This sample represents nearly 39% of the total historical trade volume (44 of 113 trades), reflecting sustained trade execution in recent market regimes. The completion of 44 trades over this approximately two-year sub-window aligns closely with the long-term historical rate of 16 trades per year, confirming that the strategy's signal generation mechanism has remained steady and active. Furthermore, the strong positive sum of +215.71% demonstrates that the model's structural edge has persisted into current market dynamics without suffering from performance degradation or structural breakdown.
Strengths and limitations
The primary strength of this quantitative strategy lies in its exceptional win rate (77.88%) and winning streak capability (up to 21 consecutive winning trades), supported by an unbroken seven-year streak of positive annual performance. The broad distribution of gains—evidenced by the top three winners generating only 14.89% of gross profits—ensures that performance is not fragile or dependent on anomalous price spikes. Conversely, the core limitation is the structural payoff asymmetry reflected in the 1.25 profit factor and the -25.27% worst trade loss. Because individual losses can be substantially larger than individual wins (+4.49% average gain vs -25.27% worst loss), equity growth experiences occasional sharp steps downward. Additionally, the multi-week holding time requires automated trading robots and portfolio managers to accommodate overnight market exposure and prolonged holding periods.
DevioLab analytical conclusion
With a DevioLab score of 75.83 and a #2 rank for CRL, this algorithmic trading strategy offers a distinct statistical profile optimized for high directional hit rates and steady capital accumulation. Its strength lies in high win consistency, multi-year stability across diverse market phases, and low dependency on profit outliers. However, quantitative analysts and algorithmic operators must account for the profit factor constraint of 1.25 and manage the psychological and risk implications of occasional deep trade drawdowns. When deployed via systematic trading agents, the strategy serves as a reliable swing-oriented model that trades low execution frequency and high win rates against asymmetrical loss risks.
Data scope and methodology
This analysis is based entirely on historical backtested results for CRL operating on a 15-minute chart interval between September 16, 2019, and October 8, 2026. All cited metrics—including the 113 completed trades, 77.88% win rate, 1.25 profit factor, and annual breakdown figures—represent simulated closed-trade statistics derived from quantitative backtesting. Percentage returns reflect closed trade outputs and do not represent live account performance, guaranteed future returns, or real-time trading agent execution. Slippage, exchange fees, and margin costs were not factored into raw return figures unless specified. This content is provided strictly for historical research and educational purposes and does not constitute investment advice.