DASH · DoorDash, Inc.
Stock Universe · 全球股票市场DASH 15m Algorithmic Trading Strategy Analysis: High Win-Rate Structure and Low Profit Concentration
This quantitative evaluation analyzes an algorithmic trading strategy for DASH operating on the 15-minute timeframe over a 5.79-year evaluation window from December 2020 through October 2026. Demonstrating an impressive win rate of 84.71% across 85 completed trades and a profit factor of 6.25, the strategy achieves a top rank for DASH within the DevioLab evaluation framework with a score of 85.39. A structural examination reveals that performance is not dependent on extreme positive outliers, as the top three winning trades account for only 13.16% of total gross profits. Position holding times average 343.83 hours (approximately 14.3 days), positioning the system as a multi-week swing model rather than an intraday scalper. With a maximum drawdown of 20.35% matching its single worst historical trade (-20.35%) and a maximum consecutive losing streak of only one trade, the strategy exhibits notable historical consistency across diverse yearly market regimes.
分析日期： 15.10.2026. 策略数据单独更新。阅读完整分析
1. Strategy profile
The strategy designated as DASH 0 +15393.47% 1TRAD-QAF4 represents an automated quantitative logic engineered for asset symbol DASH on a 15-minute chart interval. Within the DevioLab framework, this model holds the number one rank for DASH with a DevioLab Score of 85.39, categorizing it as a core strategy within its asset pool. The underlying dataset spans from December 30, 2020, to October 15, 2026, comprising a total history of 5.79 years. During this backtested history, the strategy generated 85 completed closed trades. Designed to function as the decision engine for automated trading bots, trading robots, or algorithmic trading agents, the strategy operates by processing high-frequency 15-minute price bars to identify multi-day exposure opportunities. Over the complete sample period, the strategy accumulated a total closed trade return sum of +15,393.47%, reflecting a highly skewed distribution toward winning outcomes where 72 out of 85 trades closed with positive returns.
2. Trading rhythm and position duration
Although the strategy processes market signals on a 15-minute price bar interval, its operational execution profile differs substantially from high-frequency intraday trading. Over the 5.79 years of evaluated history, the strategy recorded an average trade frequency of 14.67 trades per year, which translates to approximately 1.42 trades per active month. The temporal spacing between trade exits averages 24.68 days, with a median interval of 20.98 days between trade completions. Position holding durations reinforce this low-frequency, swing-oriented profile: the average holding time across all 85 closed trades is 343.83 hours (approximately 14.3 days), while the median holding duration stands at 264.00 hours (11.0 days). This disparity between signal frequency (15-minute bars) and holding duration (multi-week trades) indicates that the 15-minute timeframe is utilized for refined entry and exit execution rather than generating continuous trade turnover. For automated trading agents or trading bots executing this logic, transaction frequency remains minimal, reducing exposure to execution-level friction while maintaining extended market exposure during defined trade cycles.
3. Quality of historical results
The performance metrics of this DASH strategy demonstrate exceptional payoff efficiency and consistency. The overall historical win rate stands at 84.71%, derived from 72 winning trades against 13 losing trades. This high win rate is coupled with a profit factor of 6.25, indicating that total gross winning trade percentages exceed total gross losing trade percentages by more than sixfold. The average closed trade result across the entire dataset is +6.77%, while the median trade result closely aligns at +6.04%. The proximity between average and median trade performance underscores a uniform distribution of results across trades, rather than an equity curve inflated by rare, outsized gains. Furthermore, an analysis of profit concentration reveals that the strategy's top three winning trades combined account for only 13.16% of total gross profit. The best single historical trade achieved a return of +37.56%. Because gross gains are broadly distributed among its 72 winning executions, the system demonstrates high structural stability and resilience against outlier reliance.
4. Risk, drawdown and losing behavior
Risk characteristics for the strategy are defined by tight cluster control over losing streaks paired with an absolute maximum drawdown of 20.35%. Notably, the worst individual trade in the 85-trade sample recorded a loss of -20.35%, which mirrors the maximum historical portfolio drawdown figure. This alignment reveals that the maximum drawdown was driven by a single severe trade exit rather than a prolonged sequence of compounding losses. This interpretation is directly reinforced by the strategy's streak metrics: the longest consecutive losing streak recorded across the entire 5.79-year history is exactly 1 trade. At no point in the backtest did the strategy experience back-to-back losing trades. Conversely, the longest consecutive winning streak reached 21 trades. While single-trade downside risk can reach approximately 20%, the complete absence of consecutive losses historically limited multi-trade drawdowns, allowing equity recovery following isolated losing events.
5. Behavior through time and yearly stability
An examination of yearly performance metrics reveals uninterrupted annual consistency throughout the test period from 2021 through 2026. In 2021, the strategy logged 16 trades with an 81.25% win rate and a cumulative return sum of +65.69%. In 2022, trade volume rose to 17 trades with a 76.47% win rate and a sum of +119.67%. The year 2023 saw lower trading volume with 7 trades, but achieved a 100.00% win rate and a cumulative return sum of +123.20%. In 2024, performance remained robust with 12 trades, an 83.33% win rate, and a sum of +99.21%. During 2025, the strategy produced 15 trades with a 93.33% win rate and a +83.01% sum, followed by 2026 which recorded 18 trades, an 83.33% win rate, and a return sum of +84.99%. Across all six individual calendar years, the strategy maintained win rates above 76% and delivered positive cumulative performance, demonstrating that its signal logic operated successfully across diverse macro conditions without relying on a single isolated period of hyper-profitability.
6. Recent period since 2024-06-01 versus full history
Evaluating recent strategy behavior since June 1, 2024, provides insight into current model efficacy. Out of the 85 total historical trades, 40 completed trades fell within this recent window (defined by sell timestamps on or after June 1, 2024 UTC). During this recent timeframe, the strategy generated a cumulative return sum of +240.27%. This represents nearly half of all completed historical trades occurring within the last two and a half years of the evaluation period. The average trade performance during this recent window remained consistent with long-term metrics, averaging approximately +6.01% per trade (+240.27% across 40 trades). Rather than experiencing decay or parameter degradation, the strategy maintained active signal generation and stable trade delivery, confirming that recent asset dynamics for DASH continue to align with the underlying quantitative capture rules.
7. Strengths and limitations
The primary structural strength of this DASH algorithmic strategy lies in its high accuracy and balanced profit distribution. With an 84.71% historical win rate, a 6.25 profit factor, and a top-three winner concentration of just 13.16%, performance is exceptionally broad-based. The complete historical absence of consecutive losing streaks (maximum losing streak of 1) provides predictable recovery patterns following adverse trades. Conversely, the strategy's primary limitation is its modest total sample size of 85 completed trades over nearly six years, yielding roughly 14.67 trades per year. Because average holding times span 343.83 hours (over two weeks), automated trading bots deploying this logic must maintain positions through multi-day price fluctuations and overnight market risk. Additionally, the presence of a -20.35% worst single trade indicates that while losing events are rare, individual stop or exit executions can result in notable drawdowns that require disciplined risk management.
8. DevioLab analytical conclusion
Achieving a DevioLab Score of 85.39 and ranking first among evaluated strategies for DASH, this algorithmic logic demonstrates a superior risk-adjusted historical return profile. Its combination of an 84.71% win rate, strong payoff symmetry, and minimal profit concentration distinguishes it from strategies that rely on rare outlier rallies to maintain positive expectancy. The alignment between average trade (+6.77%) and median trade (+6.04%) reinforces the mechanical consistency of its multi-day swing capture mechanism. For developers and quantitative operators configuring trading agents or trading robots, the strategy offers a low-turnover, high-win-rate decision framework suitable for capturing structural moves in DASH on a 15-minute chart basis, provided that execution parameters account for multi-week holding durations and single-trade downside potential.
9. Data scope and methodology
This analysis is based strictly on historical backtested performance data provided by DevioLab for asset symbol DASH across a 15-minute candle interval from December 30, 2020, to October 15, 2026. All statistics, including percentage returns, win rates, holding periods, and drawdown figures, represent simulated closed trades calculated on historical price data. This research does not incorporate real-time order book depth, execution slippage, broker commissions, or variable exchange financing fees unless specifically noted. Historical results do not guarantee future performance, and this material is provided solely for quantitative research and analytical purposes, not as financial or investment advice.