ADM · Archer-Daniels-Midland Company
Stock Universe · global stock marketADM Quantitative Strategy Analysis: High Win-Rate Multi-Week Position Logic on a 15-Minute Execution Grid
This analytical report evaluates the historical performance of the top-ranked quantitative strategy for ADM stock, registered under the designation ADM 0 +1561.43% 1TRAD-PBZ5. Operating on a 15-minute candle evaluation frequency, the strategy exhibits an uncommon statistical profile that pairs low trade frequency with multi-week position holding times. Over a dataset spanning nearly seven years from September 2019 to September 2026, the algorithm completed 53 trades, achieving a net cumulative cumulative return of +1561.43% with an 84.91% win rate and a Profit Factor of 7.5. Risk parameters remain exceptionally tight, reflected in a maximum historical drawdown of 12.07% and a maximum losing streak of just one trade. The strategy ranks first among evaluated models for ADM on DevioLab with a composite score of 86.40. This article breaks down the mathematical relationships between holding duration, return distribution, drawdown structure, and annual stability to explain how this automated trading logic achieves its results.
Analysis as of 12.09.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The algorithmic trading strategy designated as ADM 0 +1561.43% 1TRAD-PBZ5 represents the core quantitative model for ADM stock within the DevioLab framework, earning a composite DevioLab score of 86.40 and holding the number one rank for this asset. Designed to function as the underlying decision engine for automated trading bots and trading agents, the strategy utilizes a 15-minute price bar interval to evaluate price action and execute position changes. Across a historical backtesting window of 6.96 years, starting on September 16, 2019, and running through September 2, 2026, the strategy generated 53 completed trade cycles. Out of these 53 trades, 45 resulted in positive returns while 8 ended in losses, establishing an overall win rate of 84.91%. Total historical profit across the entire sample reached +1561.43%, corresponding to a high profit factor of 7.5. The structural design of the strategy classifies it as a core strategy model, meaning it serves as an independent benchmark for long-term algorithmic engagement in ADM stock.
2. Trading rhythm and position duration
A critical structural feature of this strategy is the contrast between its execution interval and its holding duration. Although the system monitors market data on a granular 15-minute timeframe, it is distinctly not a high-frequency system or scalping model. Instead, it operates as a patient, position-style trend or momentum capture model. The strategy generates an average of only 7.61 trades per year, which translates to roughly 1.18 trades per active trading month. The average duration for a closed position is 733.83 hours, or approximately 30.5 calendar days, while the median holding time stands at 575.50 hours, or roughly 24 calendar days. Spacing between position exits is similarly extended, averaging 44.98 days with a median of 36.00 days. This indicates that automated trading robots executing this logic spend substantial periods waiting in positions or remaining flat, leveraging the 15-minute timeframe primarily for entry precision rather than rapid position turnover.
3. Quality of historical results
The quality of returns generated by the strategy is characterized by consistency across trades and minimal reliance on extreme outliers. The average return per completed trade is +5.86%, while the median trade return stands at +5.74%. The close proximity between the mean and median indicates a highly symmetrical return distribution without heavy positive skewing. The single best trade over the 6.96-year history yielded +20.42%, whereas the single worst trade produced a loss of -12.07%. Furthermore, the top three winning trades combined account for only 17.48% of the gross cumulative profit. In many trading strategies, a small handful of oversized winners drives the vast majority of net returns, creating vulnerability if market regimes shift. Here, the low profit concentration confirms that historical gain generation is broadly distributed across the 45 winning positions, reinforcing the statistical robustness of the 84.91% hit rate and 7.5 profit factor.
4. Risk, drawdown and losing behavior
Risk containment within the strategy displays exceptional discipline throughout the historical testing period. The maximum historical drawdown experienced by the portfolio model is 12.07%. Remarkably, this peak-to-trough decline matches the exact percentage of the strategy's worst single losing trade (-12.07%). This alignment suggests that drawdowns have historically been driven by isolated single-trade stop events rather than compounding strings of consecutive losses. Indeed, the strategy's longest losing streak over 53 completed trades is exactly 1 trade, meaning the algorithm never experienced back-to-back losing positions in the analyzed sample. Conversely, the longest winning streak reached 10 consecutive profitable trades. For trading agents deployed on capital allocation platforms, a maximum drawdown of 12.07% combined with an absence of consecutive loss sequences provides a highly controlled equity curve profile, albeit with the standard caveat that past drawdown behavior does not guarantee future loss limits.
5. Behavior through time and yearly stability
An examination of the strategy's yearly breakdown demonstrates high operational consistency across diverse market environments between 2020 and 2026. Every single calendar year in the evaluation window generated a positive net cumulative percentage return. In 2020, the strategy completed 7 trades with a 85.71% win rate, yielding +43.12%. In 2021, 5 trades produced a 80.00% win rate and +39.01%. Performance accelerated in 2022 with 8 trades, a 87.50% win rate, and +53.49%. The year 2023 represented a temporary performance consolidation, where 7 trades yielded +7.78% despite maintaining a 71.43% win rate. Activity rebounded in 2024 with 8 trades yielding +37.33% at an 87.50% win rate. The strategy saw its highest annual output in 2025, completing 12 trades with an 83.33% win rate for a net return of +76.82%. In the partial dataset for 2026, 6 trades achieved a 100% win rate, contributing +53.22%. This unbroken string of positive annual sums underscores the strategy's structural adaptability across multiple market cycles.
6. Recent period since 2024-06-01 versus full history
Focusing specifically on recent history from June 1, 2024, to September 2, 2026, provides insight into the algorithm's contemporary execution dynamics. During this recent window, the strategy completed 23 trades, accumulating a net return sum of +150.29%. This represents a notable acceleration in trade frequency compared to the earlier baseline: 23 out of the total 53 historical trades—or roughly 43.4% of all historical positions—were closed during this recent period. Despite the increased execution density, performance quality remained elevated, with the recent return sum accounting for approximately 9.6% of total historical profit while delivering consistent baseline gains. The strong alignment between recent performance (+150.29% across 23 trades) and full-history metrics demonstrates that the strategy's core logic has remained effective in recent trading conditions.
7. Strengths and limitations
The primary strength of the strategy lies in its high win rate (84.91%) combined with an extraordinary Profit Factor of 7.5 and a constrained maximum drawdown of 12.07%. The broad distribution of gains—evidenced by the low 17.48% profit concentration among the top three winners—ensures that performance is not dependent on speculative tail events. Additionally, the complete absence of consecutive losing streaks provides psychological and operational stability for automated execution. However, specific limitations must be recognized. With only 53 total trades over nearly seven years, the sample size in terms of total trade occurrences is modest. Traders expecting frequent market interaction will find the average 30.5-day holding period and 45-day exit spacing restrictive. Furthermore, holding individual positions across multi-week horizons exposes capital to overnight gap risk and broader equity market structural shifts that cannot be managed intraday.
8. DevioLab analytical conclusion
The quantitative evaluation conducted by DevioLab establishes ADM 0 +1561.43% 1TRAD-PBZ5 as an exceptionally stable, low-frequency position trading model for ADM stock. By marrying 15-minute bar resolution for entry timing with multi-week position management, the algorithm achieves an optimal balance between precision and trend capture. A DevioLab score of 86.40 and a rank of 1 reflect its superior risk-adjusted return profile, defined by a 7.5 profit factor, low top-winner dependency, and minimal historical drawdown. For developers and institutional operators building deployment architectures for automated trading bots, trading robots, or quantitative trading agents, this strategy provides a robust historical blueprint for patient capital deployment in ADM stock.
9. Data scope and methodology
This analysis is based entirely on historical backtested strategy data generated for ADM stock between September 16, 2019, and September 2, 2026 (a duration of 6.96 years). Evaluation was conducted using 15-minute candle data intervals. All metrics, including total return (+1561.43%), win rate (84.91%), drawdown (12.07%), and holding durations, reflect closed simulated trades. In accordance with DevioLab methodology, statistics do not incorporate external execution costs, slippage, variable broker commission schedules, or leverage parameters. Historical backtested performance serves as a quantitative measurement tool and is not indicative of guaranteed future trading results.