CHRW · C.H. Robinson Worldwide Inc.
Stock Universe · global stock marketCHRW Algorithmic Trading Strategy Analysis: High-Win-Rate Positional Logic in 15-Minute Market Data
This empirical evaluation analyzes the CHRW 0 +3359.71% 1TRAD-BGA2 quantitative strategy, an automated decision model designed for CHRW stock using 15-minute price data. Spanning a historical period of nearly seven years from September 2019 to August 2026, the strategy achieves a DevioLab score of 81.94, ranking first for this equity ticker. The strategy demonstrates an exceptional win rate of 81.58% across 76 completed trades, combined with a contained maximum drawdown of 13.93%. By maintaining long average holding durations of nearly 17 days despite its intraday evaluation interval, the system functions as a low-frequency, swing-to-positional trading engine. This study examines the structural mechanics, distribution of returns, drawdown profile, and stability across full historical and recent timeframes.
Analysis as of 01.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The CHRW 0 +3359.71% 1TRAD-BGA2 strategy represents a specialized quantitative decision engine developed for CHRW equity trading. Operating on a 15-minute price interval, the strategy acts as the systematic core for automated trading bots, algorithmic execution agents, and trading robots that process intraday price inputs to capture multi-day trends. Over a total historical evaluation window of 6.94 years ranging from September 16, 2019, through August 26, 2026, the strategy completed 76 closed trades. With 62 profitable exits and only 14 losing trades, the system establishes a strong empirical win rate of 81.58%. It achieves an aggregate cumulative output of +3359.71% across the full backtested period, yielding a DevioLab score of 81.94 and securing the top overall ranking for the CHRW symbol within the DevioLab framework. Rather than engaging in hyperactive intraday churning, the strategy utilizes the 15-minute candle structure to pinpoint patient, higher-conviction position entries and exits.
Trading rhythm and position duration
A defining characteristic of this strategy is the contrast between its 15-minute sampling resolution and its extended position holding times. While intraday data feeds power the signal processing, the historical behavior reflects a distinctly swing-to-positional execution rhythm. The strategy averages approximately 10.94 completed trades per year, which translates to a calm pace of 1.33 trades per active month. The mean holding duration stands at 409.80 hours, or roughly 17.08 days, while the median holding duration is 216.25 hours, or approximately 9.01 days. The divergence between mean and median holding times indicates that while many trades conclude within a week or two, select position holds extend across several weeks to capture larger structural price swings. Exits occur infrequently, with a mean spacing of 33.32 days and a median spacing of 30.00 days between completed trades. For developers building automated trading agents or deploying systematic trading bots, this cadence means low portfolio turnover, reduced operational execution stress, and long holding windows where market exposure remains active over multiple macroeconomic announcements and earnings cycles.
Quality of historical results
The statistical distribution of trade returns reveals noteworthy structural qualities. The average completed trade generated a positive return of +5.16%, while the median trade registered at +4.25%. The close alignment between the average and median return values demonstrates that performance is driven by consistent, baseline wins rather than being distorted by isolated statistical anomalies. The single best historical trade achieved a gain of +24.52%, whereas the single worst trade produced a loss of -13.93%. Furthermore, the top three winning trades collectively account for only 16.43% of total gross historical profit. This relatively low concentration ratio confirms that strategy returns are broadly distributed across its 62 winning trades, rather than relying heavily on a handful of lucky tail events. The historical profit factor stands at 1.25. In the context of an 81.58% win rate, a profit factor of 1.25 indicates that while winning trades are frequent, individual losing trades tend to be larger in absolute percentage terms than the typical winning trade. The system relies on its high hit rate to overcome these periodic downside drawdowns.
Risk, drawdown and losing behavior
Risk management metrics indicate controlled historical equity curve fluctuations relative to overall accumulated gains. The strategy recorded a maximum drawdown of 13.93% over its 6.94-year history. This maximum drawdown figure aligns almost exactly with the worst individual closed trade (-13.93%), highlighting that equity drawdowns were largely driven by single adverse trade realizations rather than compounding sequences of multiple losses. The longest winning streak reached 9 consecutive profitable trades, whereas the longest losing streak was capped at just 2 consecutive trades. This short losing streak length highlights the strategy's historical ability to prevent sustained performance slumps. However, because the profit factor is 1.25 despite an 81.58% win rate, risk evaluation must acknowledge that occasional losing trades can erode a noticeable portion of recently accumulated small gains. Traders utilizing this strategy within trading robots must account for the duration of trades, recognizing that holding losing positions over multi-week periods requires disciplined adherence to automated exit parameters.
Behavior through time and yearly stability
Examining performance across individual calendar years demonstrates remarkable multi-year consistency. In 2019, across 4 trades, the system achieved a 75.00% win rate and a combined return of +11.66%. Performance accelerated in 2020, delivering 13 trades with an 84.62% win rate and +67.25% sum return. In 2021, 10 trades produced an 80.00% win rate and +45.54%. The strong performance continued through 2022, yielding 11 trades with an 81.82% win rate and +55.27%, followed by 2023 with 15 trades, an 80.00% win rate, and +44.91%. In 2024, 9 trades achieved an 88.89% win rate and +59.36%. The strategy recorded 7 trades in 2025 with a 71.43% win rate and +59.03%, and in 2026 recorded 7 trades with an 85.71% win rate and +49.46%. Throughout every single full or partial calendar year in the sample, annual win rates remained solidly above 70%, and aggregate annual trade returns remained consistently positive. Trade frequency stayed stable within a narrow range of 7 to 15 trades per full year, showing that signal generation remained steady across varying equity market volatility regimes.
Recent period since 2024-06-01 versus full history
Evaluating performance in the recent period starting June 1, 2024, provides crucial insights into modern strategy efficacy. Out of the 76 total historical trades, 20 closed trades fall within this recent evaluation window. These 20 recent trades generated a cumulative return sum of +158.30%, representing a substantial share of total performance over a window of approximately two years. This recent sample represents more than a quarter of the entire historical trade population, ensuring that contemporary observations carry statistical weight. The presence of 20 completed trades with robust cumulative returns demonstrates that the underlying trade logic has maintained its efficacy into recent market environments rather than decaying over time. For quantitative traders evaluating automated trading bots for CHRW, this strong recent sample confirms ongoing responsiveness to recent equity price dynamics.
Strengths and limitations
The strategy displays several core quantitative strengths. Primary among them is its high historical win rate of 81.58%, paired with an exceptionally low maximum drawdown of 13.93% over nearly seven years. The top three winners contribute only 16.43% of gross profits, proving that performance is broadly anchored across many successful trades. Annual consistency is excellent, with every year generating high hit rates and positive total returns. On the limitation side, the total sample size consists of 76 completed trades, which, while spanning 6.94 years, represents a relatively small absolute trade count due to the strategy's low execution frequency. Additionally, the profit factor of 1.25 reflects an asymmetrical payoff profile where losing trades are larger on average than winning trades. Finally, the long median holding time of 216.25 hours requires tolerance for market exposure during major overnight gaps and news events.
DevioLab analytical conclusion
The CHRW 0 +3359.71% 1TRAD-BGA2 strategy earns its top DevioLab ranking for CHRW through a combination of high statistical consistency, well-distributed profit sources, and controlled historical drawdown. Its decision framework successfully translates 15-minute price signals into structured, multi-week position holdings that capture significant trend moves while maintaining an 81.58% win rate. The strategy is well suited for deployment as decision logic in automated trading agents and trading bots where low trade frequency and disciplined risk boundaries are preferred over high-turnover scalping. While users must respect the risk profile created by occasional larger losing trades and the patience required for 17-day average position holds, the empirical dataset presents a highly robust profile across both long-term and recent market windows.
Data scope and methodology
This analysis is based strictly on historical backtested performance data compiled by DevioLab for the CHRW asset ticker operating on a 15-minute timeframe between September 16, 2019, and August 26, 2026. All statistics, including win rates, holding durations, profit factors, drawdowns, and yearly performance breakdowns, reflect closed simulated trade events. Trade exits occurring on or after June 1, 2024, are categorized under the recent window. The results do not account for live order execution variables such as slippage, exchange fee schedules, or order routing delays. Historical performance figures are hypothetical backtests and do not constitute financial advice or guarantees of future investment performance.