DLTR · Dollar Tree Inc.
Stock Universe · global stock marketDLTR · DLTR 15-Minute Algorithmic Strategy Analysis: Quantitative Profile of a High-Precision, Low-Frequency Model
This analytical report evaluates the historical backtest performance of the top-ranked DLTR 15-minute quantitative strategy. Across a seven-year historical dataset comprising 83 completed trade events, the strategy achieved an 87.95% win rate, a profit factor of 8.75, and a maximum equity drawdown of 11.51%. By pairing high-granularity 15-minute market evaluation with an extended holding duration averaging 360.08 hours, the system captures substantial multi-day trends while maintaining exceptional profit distribution, where the top three winning trades account for only 15.56% of gross historical gains.
Analysis as of 20.10.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The quantitative model designated as DLTR 0 +13819.77% 1TRAD-SCA5 represents the primary core strategy for DLTR stock on a 15-minute execution interval. Designed to serve as the decision engine for automated trading bots and algorithmic trading agents, the strategy processes intraday bar data to identify high-probability entry windows, while managing trade exits across longer multi-day horizons. Over a seven-year evaluation window spanning from September 16, 2019, to September 14, 2026, the strategy recorded 83 completed trades, establishing a DevioLab performance score of 90.80 and earning the rank of number one among evaluated models for this asset. The historical sample reflects an overall win rate of 87.95%, supported by 73 winning trades against 10 losing trades. This exceptional hit rate yields a total historical profit sum of 13,819.77% across closed trade results. The system exhibits an asymmetric payoff structure characterized by a profit factor of 8.75, indicating that gross historical gains outweighed gross historical losses by nearly nine to one. Rather than generating high-frequency turnover, the model acts as a selective quantitative filter, waiting for specific structural conditions before committing capital.
2. Trading rhythm and position duration
Despite operating on a granular 15-minute chart timeframe, the strategy exhibits the holding characteristics of a patient swing-trading model rather than a rapid intraday scalper. Across the 83 completed positions, the average holding time reached 360.08 hours (approximately 15 calendar days), while the median holding duration stood at 258.75 hours (roughly 10.8 days). This substantial duration demonstrates that 15-minute price bars are utilized primarily for entry and exit precision rather than brief exposure windows. On average, the strategy completed an exit every 30.93 days, with a median spacing between exits of 23.07 days. On an annualized basis, trade activity averaged 11.86 trades per year, which translates to approximately 1.43 completed trades per active calendar month. For automated trading robots executing this logic, this creates a distinct operational profile: long periods of market observation punctuated by selective position deployment. The extended holding periods allow individual trades to capture broader underlying momentum in DLTR, ensuring that capital is not over-traded or exposed to excessive turnover risk.
3. Quality of historical results
The statistical distribution of trade returns reveals remarkable internal balance across the strategy's history. The average trade gain across all 83 executed positions stands at +6.58%, closely anchored by a median trade return of +5.07%. The proximity of the mean to the median indicates that overall historical performance was not artificially distorted by extreme statistical anomalies. The best single trade achieved a return of +44.02%, whereas the worst individual trade resulted in a loss of -11.51%. A crucial metric regarding return quality is the gross profit concentration: the strategy's three largest winning trades generated only 15.56% of total gross historical profit. In quantitative research, a low top-three concentration percentage confirms that performance is broad-based and repeatable, rather than dependent on a few isolated windfall trades. Combined with a profit factor of 8.75, the data demonstrate that the model consistently captures positive return drift while effectively capping the magnitude of non-performing trades.
4. Risk, drawdown and losing behavior
Risk management within the strategy is highlighted by a maximum historical equity drawdown of 11.51%. Remarkably, this maximum portfolio drawdown figure matches the exact percentage loss of the worst single trade (-11.51%), illustrating that equity declines were primarily bounded by single-trade stop dynamics rather than compounding series of catastrophic losses. Over the entire 83-trade sample, the strategy encountered a maximum losing streak of just 2 consecutive trades, compared to a maximum winning streak of 22 consecutive positive trades. With 73 total winning trades and only 10 losses, losing occurrences were isolated statistical exceptions rather than persistent structural cluster events. This low drawdown footprint relative to cumulative historical gain creates a favorable return-to-risk ratio. However, because position holding times average 15 days, automated trading agents implementing this system must accommodate multi-week price volatility while waiting for terminal exit triggers to resolve.
5. Behavior through time and yearly stability
Analyzing annual breakdown metrics reveals strong historical consistency across diverse market regimes in DLTR. In 2019, during the initial sample period starting in September, the strategy closed 3 trades, all winning (100% win rate), for a combined sum of +8.48%. In 2020, output expanded to 11 trades with 10 wins (90.91% win rate) yielding +104.72%. The year 2021 saw 7 trades with 6 wins (85.71% win rate) and a +57.74% return sum. In 2022, 11 trades yielded 10 wins (90.91% win rate) accumulating +76.51%. Activity in 2023 generated 12 trades with 11 wins (91.67% win rate) totaling +55.00%. In 2024, the system logged 15 trades with 11 wins (73.33% win rate) producing +41.61%. Performance rebounded strongly in 2025, recording 14 completed trades with zero losses (100% win rate) and a return sum of +134.88%. Finally, in the 2026 data window, 10 trades were executed with 8 wins (80.00% win rate) adding +67.11%. Every single calendar year generated positive net trade sums and win rates exceeding 73%, demonstrating structural resilience.
6. Recent period since 2024-06-01 versus full history
The historical sample includes a substantial concentration of recent trade activity evaluated from June 1, 2024, onward. Within this recent window, the strategy completed 37 closed trades, generating a cumulative trade return sum of +231.50%. This recent period accounts for nearly 45% of all total historical trades recorded since 2019, offering a robust empirical baseline to assess modern market behavior. The frequent trade generation during this window indicates that market volatility and price structure in DLTR provided frequent trigger conditions aligned with the strategy's entry criteria. The performance during this period shows that the system's core parameters maintained strong efficacy in recent trading environments without suffering from alpha degradation or performance saturation.
7. Strengths and limitations
The primary strength of this quantitative model lies in its high statistical reliability, evidenced by an 87.95% historical win rate, a 22-trade winning streak, and a profit factor of 8.75. Furthermore, the low profit concentration of 15.56% across the top three trades proves that gains are systematically distributed across many trades. Controlled downside risk is another clear advantage, with maximum drawdown limited to 11.51%. Conversely, primary limitations center around low trade frequency and capital lockup duration. Generating an average of only 11.86 trades per year, the strategy requires patience and is unsuited for operators seeking continuous daily action. Additionally, holding positions for an average of 360 hours exposes open trades to broader market gap risks and earnings announcements, which requires disciplined risk tolerance from automated trading bots executing the logic.
8. DevioLab analytical conclusion
Achieving a DevioLab score of 90.80 and ranking first among strategies for DLTR, this model demonstrates an exceptional balance between high win probability, broad return distribution, and controlled drawdown parameters. By utilizing a 15-minute execution frame to fine-tune entries while holding positions across multi-week horizons, the system effectively captures structural swings in DLTR stock. The empirical evidence across 83 trades demonstrates that the strategy does not rely on outlier luck, but rather on a consistent edge that has functioned effectively across multiple years and recent trading windows. For quantitative practitioners deploying automated trading agents, the strategy offers a robust framework for systematic position taking, provided capital allocation accounts for low trade frequency and multi-day holding requirements.
9. Data scope and methodology
The metrics presented in this report are derived entirely from backtested historical simulation data covering closed trade events between September 16, 2019, and September 14, 2026, on 15-minute price bars for DLTR. All return statistics, including mean gains, profit factor, drawdown percentages, and annual breakdown totals, reflect closed trade results without compounding fees, slippage, or live order execution latency, unless explicitly noted. Historical performance data serve solely as a quantitative tool to evaluate historical strategy dynamics and do not constitute investment advice or guarantee future performance outcomes in live trading environments.