CASY · Casey's General Stores, Inc.
Stock Universe · 全球股票市场Quantitative Evaluation of CASY · CASY Algorithmic Strategy: High Win-Rate, Position Duration, and Drawdown Control
This quantitative research report evaluates the historical backtested behavior of an algorithmic trading strategy for CASY (Casey's General Stores). Operating on a 15-minute candle evaluation frequency across a 7-year sample from September 2019 to September 2026, the strategy generated 39 completed trade events with a 92.31% win rate, a profit factor of 5.0, and a maximum equity drawdown of 2.77%. Despite the granular 15-minute chart resolution, position holding times average over 1,000 hours, revealing a multi-week position-holding profile rather than high-frequency intraday trading. This analysis explores how rare entry selection, low loss magnitude, and stable year-over-year distribution combine to create the strategy's statistical profile, offering vital insights for quantitative traders and automated trading bot deployments.
分析日期： 26.09.2026. 策略数据单独更新。阅读完整分析
1. Strategy profile
The strategy under evaluation is a systematic, single-asset algorithmic decision logic built for CASY (Casey's General Stores) on a 15-minute price bar resolution. Over a historical backtest span of 7.03 years—stretching from September 16, 2019, through September 26, 2026—the model recorded 39 fully completed trades. Across this multi-year evaluation dataset, the strategy accumulated a total closed-trade sum percentage return of 2290.42%, translating to an annualized return metric of 313.64%. On the DevioLab quantitative evaluation framework, the strategy achieves a composite score of 72.33, placing it in 3rd rank among evaluated strategies for this specific ticker. Rather than engaging in active intraday churn, the system operates as a selective position-entry model designed to capture sustained price expansions while enforcing strict exit criteria. The strategy is structured as algorithmic logic suitable for implementation within automated trading bots, trading robots, or trading agents executing orders via programmatic brokerage interfaces.
2. Trading rhythm and position duration
A defining characteristic of this strategy is the structural contrast between its evaluation timeframe and its actual execution cadence. While the underlying engine evaluates market structure every 15 minutes, it acts with extreme restraint, averaging just 5.55 completed trades per year, or approximately 1.15 trades per active trading month. The average interval between trade exits is 66.36 days, with a median spacing of 47.96 days. Position holding durations reflect a genuine multi-week to multi-month commitment: the average holding time spans 1036.08 hours (approximately 43.17 calendar days), while the median holding duration sits at 647.50 hours (around 26.98 days). The rightward skew between median and average holding times indicates that while most trades settle within three to four weeks, select trends are permitted to run for considerably longer periods. This demonstrates that the 15-minute candle resolution is used primarily for precise signal generation and exit execution rather than for rapid scalping or intraday turnover.
3. Quality of historical results
The statistical quality of the strategy's return distribution is characterized by remarkable consistency and asymmetric payoff parameters. Out of 39 closed trades, 36 closed in profit, yielding a win rate of 92.31%. The average trade profit stands at +8.98%, closely tracking the median trade return of +7.10%. This proximity between mean and median trade outcomes indicates that the strategy's return profile is not artificially inflated by a single massive outlier, but rather driven by repeatable, mid-sized gains. The single best trade reached +36.12%, while the concentration of profit in the top three winning trades accounts for only 25.71% of total gross gains. This lower concentration metric confirms that gross profitability is broadly distributed across the winning trade population rather than reliant on a lucky minority of trade entries.
4. Risk, drawdown and losing behavior
Risk management metrics across the historical dataset reveal exceptionally contained equity pullbacks. The maximum observed historical drawdown was limited to 2.77%, a remarkably shallow equity peak-to-trough decline over a seven-year trading horizon. This tight risk boundary is reinforced by the strategy's worst historical trade loss, which was restricted to just -1.65%. Across all 39 trades, only 3 resulted in losses, producing an overall profit factor of 5.0. The strategy achieved a maximum winning streak of 21 consecutive successful trades, whereas its longest losing streak was limited to 2 consecutive trades. The statistical combination of a tight maximum trade loss (-1.65%) and a shallow maximum drawdown (2.77%) demonstrates that adverse price movement was systematically controlled during backtested execution, preventing catastrophic compound equity loss.
5. Behavior through time and yearly stability
An examination of the yearly breakdown shows consistent performance across varying market cycles from 2019 through 2026. In 2019, during the initial partial year, the strategy logged 1 winning trade (+4.51%). In 2020, 5 trades yielded 5 wins (100% win rate) with a cumulative gain of +80.12%. In 2021, 6 trades delivered 6 wins (+28.39%). In 2022, 3 trades produced 3 wins (+37.29%). The year 2023 saw the first losing trade, generating 3 wins and 1 loss (75% win rate) for a net gain of +17.70%. In 2024, performance accelerated with 7 wins out of 7 trades (+54.97%). In 2025, 3 trades yielded 3 wins (+59.58%). Finally, in 2026, activity increased to 10 completed trades with 8 wins and 2 losses (80% win rate), accumulating +67.52%. Every calendar year posted positive net cumulative returns, showing robust consistency over extended temporal regimes.
6. Recent period since 2024-06-01 versus full history
Evaluating recent performance from June 1, 2024, through September 26, 2026, provides a window into the strategy's contemporary market responsiveness. During this recent window, the strategy completed 17 trades, representing nearly 43.6% of its total historical trade volume. These 17 recent trades accumulated a combined closed-trade profit of +156.87%. Compared to the earlier historical baseline from 2019 to mid-2024, the strategy experienced an increase in trade frequency during this recent phase. Despite this higher execution tempo, return generation remained robust, contributing significantly to the full-history performance profile. The recent window demonstrates that the decision logic remained fully functional in recent market conditions without suffering from structural breakdown or performance decay.
7. Strengths and limitations
The primary statistical strengths of this algorithm lie in its exceptionally high win rate (92.31%), its disciplined drawdown containment (2.77% maximum drawdown), and its low concentration risk, with the top three trades accounting for under 26% of gross profits. Furthermore, its overall profit factor of 5.0 highlights strong profit generation relative to realized losses. Conversely, the strategy's principal analytical limitation is its small total sample size of 39 trades over 7.03 years. Because trade entries occur infrequently (5.55 trades per year), statistical confidence intervals surrounding performance parameters are wider than those of high-frequency models. Automated trading robots implementing this strategy must be prepared for long idle periods between signal occurrences and multi-week holding periods, requiring patience during extended market phases.
8. DevioLab analytical conclusion
In summary, the CASY strategy evaluated here exhibits a distinctive quantitative footprint: a low-frequency, high-precision swing trading framework operating on a 15-minute evaluation bar. Achieving a DevioLab score of 72.33 and ranking 3rd for CASY, the strategy balances a 92.31% hit rate with strict downside protection, as evidenced by its -1.65% worst trade and 2.77% peak drawdown. The strategy's performance is well-distributed across time rather than concentrated in isolated market spikes. For quantitative developers configuring automated trading agents, the strategy represents an appealing logic template focused on position quality and capital preservation, provided the operator's infrastructure can accommodate multi-week trade durations and sparse signal frequency.
9. Data scope and methodology
All metrics and statistics presented in this analytical report are derived directly from backtested historical performance data recorded between September 16, 2019, and September 26, 2026, for ticker CASY on a 15-minute timeframe. Return percentages represent historical closed-trade sum and compound metrics generated within simulated backtesting environments. These statistics do not include slippage modeling, order routing latency, broker commission schedules, or real-time execution friction. Historical performance records serve strictly as quantitative analytical research and do not guarantee future returns or live execution results. This article is published for educational and analytical purposes only and does not constitute financial advice.