DD · DuPont de Nemours, Inc.
Stock Universe · mercado global de açõesQuantitative Evaluation of DD Algorithmic Trading Strategy: High Win Rate and Balanced Payoff Architecture in Stock Market Execution
This analytical report evaluates the quantitative performance profile of the DD 0 +2718.95% 1TRAD-QOB3 algorithmic trading strategy operating on 15-minute chart intervals for the stock asset DD. Spanning a 7.06-year test horizon from September 2019 to October 2026, the strategy recorded 147 completed trades, achieving a high win rate of 78.91% alongside a profit factor of 2.50 and a cumulative closed-trade return sum of +2718.95%. Designed to serve as the decision logic for automated trading bots, trading robots, and quantitative trading agents, the system exhibits a multi-day swing trading rhythm with an average holding time of 215.44 hours and an average interval between exits of 17.61 days. A key structural finding is the extraordinary dispersion of gross profit: the top three winning trades account for only 12.94% of total gross gains, indicating that overall returns are derived from a broad base of consistent positive outcomes rather than isolated luck. Peak drawdown reached 24.51%, while losing streaks were strictly capped at 3 consecutive trades. Recent activity since June 1, 2024, accounts for 50 completed trades yielding a sum of +108.84%, demonstrating sustained operational activity in contemporary market conditions. Achieving a DevioLab score of 73.08 and ranking second for the asset DD, the strategy combines robust hit rates with low profit concentration, though its low overall frequency requires operational discipline.
Análise em 16.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The algorithmic trading model designated as DD 0 +2718.95% 1TRAD-QOB3 is a specialized core model designed for execution on 15-minute timeframe data for the equity asset DD. Built to function as the core decision-making logic for automated trading bots and algorithmic trading agents, the strategy aims to systematically filter intraday market noise to capture medium-term swing movements. Over an extensive evaluation period of 7.06 years—stretching from September 16, 2019, through October 8, 2026—the quantitative engine completed 147 trade cycles. The backtested dataset demonstrates a aggregate return sum of +2718.95% across closed trades, translating into an annualized trade return metric of 153.34%. Within the DevioLab quantitative rating system, this performance profile yields an overall DevioLab score of 73.08, placing the strategy at rank 2 among evaluated quantitative models for the symbol DD. The core architectural signature relies on high precision in trade execution, generating 116 winning positions against 31 losing positions, which establishes a baseline win rate of 78.91%. This high hit rate serves as the primary engine for equity growth, minimizing equity variance and establishing a stable foundation for automated trade deployment.
Trading rhythm and position duration
Despite operating on a 15-minute candlestick chart, the strategy is explicitly a swing-trading algorithm rather than an intraday scalping framework. The statistical record indicates an average holding duration per position of 215.44 hours (approximately 8.98 days), with a median holding period of 147.50 hours (approximately 6.15 days). The positive divergence between the average and median duration demonstrates that while the typical position resolves within roughly six days, selected trades remain active for multiple weeks to allow longer price trends to fully develop. In terms of execution frequency, the strategy averages 20.81 completed trades per year, which corresponds to approximately 1.86 trades per active month. The spacing between trade exits averages 17.61 days, with a median exit interval of 13.99 days. For traders utilizing trading robots or automated execution bots, this cadence implies long inactive periods interspersed with structured, multi-day open exposure. The strategy does not overtrade or generate excessive turnover, making it suitable for systematic frameworks where position management and trend persistence are prioritized over high-frequency turnover.
Quality of historical results
An examination of payoff metrics reveals a remarkably balanced trade distribution. The average trade yield across all 147 closed positions stands at +2.63%, while the median trade yield is +2.51%. The proximity of the median to the average indicates that historical trade returns are symmetrically distributed without extreme skewness caused by anomalous single trades. The profit factor of 2.50 reflects a strong ratio of gross mathematical gains relative to gross mathematical losses. The strategy's maximum single trade gain reached +30.11%, whereas its worst single trade loss was limited to -16.01%. Crucially, an analysis of profit concentration reveals that the top three winning trades collectively generated only 12.94% of the strategy's total gross profit. In quantitative research, a low top-three profit concentration indicates that strategy profitability is structural and broad-based, rather than dependent on a tiny handful of outlier gains. The consistent positive expectancy across 116 winning trades confirms that the system achieves its net performance through repeated statistical edge across changing market cycles.
Risk, drawdown and losing behavior
The historical risk envelope of the strategy highlights both controlled downside exposure and moderate path volatility. Over the full 7.06-year test horizon, the maximum peak-to-trough drawdown recorded was 24.51%. When evaluated alongside the strategy's cumulative trade return sum of +2718.95% and a profit factor of 2.50, a 24.51% maximum drawdown represents a reasonable downside tolerance for an unleveraged equity growth algorithm. Furthermore, the strategy exhibits remarkable resilience regarding losing streaks: the maximum consecutive loss sequence was strictly capped at 3 trades, whereas the longest winning streak reached 11 consecutive profitable trades. The worst single trade loss of -16.01% demonstrates that while individual tail losses can occur during adverse market regime shifts, the algorithm's exit mechanism successfully prevents catastrophic drawdowns. The combination of a high win rate (78.91%) and brief losing streaks helps limit extended emotional or financial drawdowns, facilitating smoother capital curves during live execution via trading bots.
Behavior through time and yearly stability
The yearly performance breakdown illustrates sustained profitability across diverse stock market environments, though annual net return totals reflect shifting market volatility. In the initial short dataset of 2019 (starting September 16), the strategy logged 4 trades (75.00% win rate) yielding a sum of +0.92%. Performance expanded substantially in 2020, producing 24 trades, an 83.33% win rate, and a annual trade sum of +113.82%. In 2021, the system executed 21 trades with a 71.43% win rate and a +53.25% sum, followed by 2022 where it completed 24 trades with a 70.83% win rate and a +69.97% sum, demonstrating resilience during broader market contractions. The year 2023 yielded 18 trades, an 83.33% win rate, and a +29.36% sum. In 2024, performance rebounded strongly with 15 trades, a 93.33% win rate, and a +42.29% return sum. The calendar year 2025 marked a minor temporary consolidation, recording 16 trades, a 62.50% win rate, and a +14.69% sum. In 2026 (through October 8), trading volume expanded to 25 trades with an 88.00% win rate and a +62.60% return sum. Every single calendar year recorded net positive cumulative closed-trade returns, proving consistent adaptation across varying market conditions.
Recent period since 2024-06-01 versus full history
A detailed audit of recent performance statistics calculated from trades closing on or after June 1, 2024, provides clear evidence of ongoing signal validity. In this recent window, the strategy completed 50 closed trades, generating a cumulative trade return sum of +108.84%. This sample of 50 recent trades represents roughly one-third of the total historical trade sample (147 trades) accumulated over 7.06 years, indicating a noticeable increase in trade execution frequency during recent market regimes. The average yield per trade in the recent window remains highly competitive, contributing over 100 percentage points of total closed-trade performance in less than two years. The seamless alignment between long-term historical metrics (78.91% win rate, +2.63% average trade) and recent trade generation confirms that the underlying quantitative edge has remained fully intact and active through contemporary trading conditions.
Strengths and limitations
The primary structural strength of the DD strategy lies in its exceptional statistical reliability. Key advantages include a high win rate of 78.91%, a solid profit factor of 2.50, a low profit concentration (top 3 trades generating just 12.94% of gross profit), and a capped maximum losing streak of 3 trades. ADDitionally, the algorithm has achieved net positive closed-trade returns in every single calendar year from 2019 through 2026. Conversely, structural limitations must be considered. With an average of only 20.81 trades per year (1.86 per active month) and an average holding duration of 215.44 hours, the strategy generates low signal frequency, requiring traders to exercise patience. A maximum drawdown of 24.51% and a worst trade loss of -16.01% also mean that users of automated trading agents must be prepared for periodic price pullbacks. Finally, as a simulated backtest study, actual live market results will naturally be impacted by order execution efficiency, bid-ask spreads, and slippage.
DevioLab analytical conclusion
The DD 0 +2718.95% 1TRAD-QOB3 trading strategy represents a robust, high-precision swing trading framework for the stock asset DD on 15-minute price charts. Earning a DevioLab score of 73.08 and holding the rank 2 position for its asset, the algorithm demonstrates exceptional statistical integrity. Its primary quantitative highlight is the rare combination of a high win rate (78.91%) and exceptionally well-distributed gains, evidenced by a top-three winner concentration of only 12.94%. Rather than relying on lucky market spikes, the strategy consistently captures medium-term trends across multi-day holding periods averaging 8.98 days. With 50 trades completed since June 2024 yielding +108.84%, recent performance reinforces the system's ongoing relevance. For quantitative traders seeking reliable logic to power automated trading robots or trading agents, this strategy offers a disciplined, statistically validated blueprint.
Data scope and methodology
All figures, metrics, and analytical observations presented in this report are calculated directly from historical backtested market data for the asset symbol DD on a 15-minute time interval between September 16, 2019, and October 8, 2026. Performance metrics, including cumulative closed-trade returns (+2718.95%), average trade yields (+2.63%), drawdowns (24.51%), and holding durations, reflect simulated executions on historical closed trades. These performance statistics are designed to evaluate algorithmic signal quality and do not account for external transaction costs, broker commissions, or execution slippage. Historical simulated backtest results are intended purely for quantitative research and algorithmic logic evaluation; they do not constitute financial advice or guarantee future investment performance.