COST · Costco Wholesale Corporation
Stock Universe · globaler AktienmarktCOST · COSTco Wholesale Strategy Analysis: Quantitative Assessment of a Low-Frequency, Long-Duration Model Achieving 868.23% Historical Return
This analytical evaluation examines a top-ranked algorithmic trading strategy developed for COST (COSTco Wholesale Corporation) evaluated on a 15-minute execution interval across a 7.06-year historical dataset from September 2019 to October 2026. Generating a cumulative simulated return of 868.23% across just 34 completed trades, the strategy exhibits an exceptional profit factor of 5.00, a high win rate of 76.47%, and a remarkably constrained maximum drawdown of 8.49%. Despite operating on a 15-minute timeframe for precise timing, the model demonstrates long position holding durations—averaging 1,589.24 hours—and low annual trade frequency (4.81 trades per year). A notable feature of the strategy's statistical distribution is the concentration of recent trade activity, with 22 of its 34 lifetime trades occurring since June 2024. This report provides a comprehensive breakdown of the strategy's structural rhythm, profit concentration, risk parameters, and temporal consistency for quantitative traders and developers evaluating automated decision logic.
Analyse vom 09.10.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
1. Strategy profile
The strategy COST 0 +868.23% 1TRAD-ZKT2 represents the top-performing quantitative model for COSTco Wholesale Corporation (COST) in the DevioLab library, holding the number one ranking for this symbol with a DevioLab score of 79.44. Constructed to evaluate price action on a 15-minute bar interval, the algorithm spans a backtested historical observation window of approximately 7.06 years, running from September 16, 2019, through October 9, 2026. Over this multi-year evaluation, the model completed 34 trades, generating a total cumulative gain of 868.23% and an annualized performance metric of 111.28%. Designed as decision logic suitable for deployment in automated trading bots, algorithmic trading robots, and autonomous execution agents, the system prioritizes selective market engagement over high transaction volume. The core profile of this strategy combines a broad historical dataset with selective execution triggers, establishing a baseline characterized by infrequent trade entries and substantial gain capture per closed cycle.
2. Trading rhythm and position duration
Although the strategy processes market data on a 15-minute price interval, its operational rhythm is fundamentally distinct from high-frequency or short-term intraday trading systems. Across 7.06 years of historical testing, the algorithm generated only 34 completed trade cycles, corresponding to an average of 4.81 trades per year and 1.42 trades per active month. Position holding times reveal a clear macro swing orientation: the average holding duration is 1,589.24 hours (approximately 66.2 days), while the median holding duration stands at 674.50 hours (approximately 28.1 days). The divergence between average and median holding times indicates a right-skewed distribution where certain positions are held across multi-month trends to maximize gain capture. The temporal spacing between trade exits further reinforces this deliberate pacing, showing an average interval between closed trades of 72.75 days and a median exit spacing of 48.76 days. For automated trading agents and deployment engines, this statistical rhythm implies low portfolio turnover, minimal transaction frequency, and extended exposure windows per trade.
3. Quality of historical results
The strategy's return profile demonstrates strong asymmetric execution quality, backed by a overall win rate of 76.47% (26 winning trades against 8 losing trades) and an overall profit factor of 5.00. The average closed trade yield is +8.07%, whereas the median trade yield is +3.79%. This gap between mean and median performance highlights the contribution of outlier winning trades to total strategy profitability. Specifically, the strategy's single best trade achieved a gain of +62.99%, compared to a worst closed trade loss of -8.30%. Further examination of gross profit distribution reveals that the top three winning trades collectively account for 48.99% of total gross historical profit. While this indicates that nearly half of the cumulative gain was driven by three exceptional market moves, the strategy's positive median return (+3.79%) confirms that profitability is not solely an artifact of a single lucky entry, but rather supported by a broader baseline of positive closed trades.
4. Risk, drawdown and losing behavior
Risk management metrics within the dataset show noteworthy discipline, particularly when evaluated against the strategy's total cumulative return. The maximum drawdown experienced across the 7.06-year test period was limited to 8.49%. This peak-to-trough decline is remarkably shallow relative to the +868.23% overall net gain, yielding a high return-to-drawdown ratio. The strategy's losing dynamics are similarly controlled: the worst individual trade recorded a loss of -8.30%, which almost perfectly mirrors the maximum strategy-level drawdown. This statistical relationship suggests that overall portfolio drawdowns were primarily caused by individual trade stop-outs rather than compound multi-trade losses during extended market contractions. Additionally, the strategy's streak analysis indicates strong persistence during favorable conditions, with a longest winning streak of 7 consecutive profitable trades compared to a maximum losing streak of just 2 consecutive losses.
5. Behavior through time and yearly stability
An examination of the yearly breakdown reveals significant variability in trade generation and return distribution over time. In 2020, the strategy logged just 1 trade (100% win rate) producing a +5.76% gain. In 2021, activity remained light with 2 trades (100% win rate) yielding an impressive combined +89.45%. The year 2022 marked an increase in transaction frequency to 8 trades with a 50.00% win rate (4 wins, 4 losses) generating +18.41%. No closed trades were recorded in 2023. Activity expanded notably in subsequent years: 2024 produced 5 trades (100% win rate) summing to +103.49%; 2025 generated 9 trades (77.78% win rate, 7 wins, 2 losses) for +31.80%; and 2026 recorded 9 trades (77.78% win rate, 7 wins, 2 losses) adding +25.62%. This timeline highlights that while yearly win rates remained consistently high (50% to 100%), the physical generation of trading opportunities is unevenly spread across calendar years.
6. Recent period since 2024-06-01 versus full history
A critical structural feature of this dataset is the concentration of completed trade events in the recent historical window. In the period starting June 1, 2024, the strategy executed 22 of its 34 total lifetime trades, generating a combined return of +99.34% during this timeframe alone. This means that approximately 64.7% of all lifetime trade completions occurred within the final ~2.3 years of the 7.06-year backtest. Comparing the recent sample to the full history shows a marked acceleration in exit events, rising from an average of under 3 trades per year prior to mid-2024 to an annualized pace exceeding 9 trades per year recently. Importantly, performance quality remained stable during this higher-density period, with the strategy maintaining strong win rates (77.78% in both 2025 and 2026) and continuing to accumulate steady positive returns without triggering elevated drawdowns.
7. Strengths and limitations
The primary historical strengths of this COST strategy include its exceptional profit factor of 5.00, a high win rate of 76.47%, controlled equity drawdown (8.49% maximum peak-to-trough drop), and strong performance in the post-June 2024 testing window. The long holding duration allows the model to ride major equity trends in COST without incurring excessive turnover. Conversely, primary limitations center on sample size and profit concentration. With only 34 total completed trades across more than seven years, the sample size is small from a strict statistical perspective. Furthermore, the top 3 winning trades account for 48.99% of all gross profits, indicating a dependence on catching high-magnitude trend outliers. Furthermore, extended periods of inactive trade generation (such as 2020, 2021, and 2023) require significant patience from operators running automated execution infrastructure.
8. DevioLab analytical conclusion
With a DevioLab score of 79.44 and a rank of #1 for COST, this 15-minute algorithmic strategy provides a robust benchmark for long-horizon position trading on large-cap equities. Its combination of a high profit factor, minimal drawdown, and long position retention makes it a compelling candidate for quantitative integration into automated trading bots, trading robots, and algorithmic portfolio agents requiring low-turnover, high-conviction decision logic. However, quantitative analysts must weigh the strategy's outstanding total return (+868.23%) against its low overall trade frequency (34 trades) and profit concentration in top winners. While historical backtested metrics confirm exceptional structural efficiency, past simulated trading performance does not guarantee future live execution results.
9. Data scope and methodology
All analytical conclusions presented in this article are derived strictly from backtested historical trade logs for COST on the 15-minute timeframe covering the period from September 16, 2019, to October 9, 2026. The metrics reflect closed-trade performance values and do not include live trading slippage, variable broker execution latency, overnight margin financing rates, or exchange fee structures unless explicitly noted. Recent window calculations (since June 1, 2024) incorporate all completed trades whose sell timestamp occurred on or after June 1, 2024, UTC. This report is produced strictly for quantitative research, algorithmic evaluation, and educational purposes, and does not constitute financial advice, investment recommendations, or endorsement of specific trading software.