APD · Air Products and Chemicals Inc.
Stock Universe · globaler AktienmarktQuantitative Analysis of the APD Algorithmic Swing Strategy: High Win Rate Dynamics and Multi-Day Holding Structure
Detailed analysis of the APD algorithmic trading strategy across a 7-year historical test window. Generating a cumulative closed-trade return sum of +1336.22% and an annualized metric of 164.79%, the model demonstrates an 80.63% historical win rate across 160 completed trades. Operating on a 15-minute timeframe while maintaining an average position duration of 187.54 hours, the strategy functions as a medium-term swing model suitable for automated trading bots and trading agents seeking disciplined execution. The strategy maintains a maximum historical drawdown of 14.83% and exhibits a low gross profit concentration, with its top three winning trades contributing only 8.40% of total gross gains. Recent performance since June 1, 2024, reflects continued statistical strength with 57 closed trades delivering a cumulative +113.21%. This evaluation examines the interaction between hit rate, payoff symmetry, yearly stability, and risk characteristics for quantitative traders evaluating automated execution models.
Analyse vom 17.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The quantitative trading strategy designated for APD operates as a top-ranked core decision model within DevioLab's stock analysis framework, holding a DevioLab score of 82.12 and securing the primary rank for this asset. Designed to evaluate price action on a 15-minute timeframe, the algorithm is structured to function as the core decision logic for automated trading bots, trading robots, and autonomous trading agents executing equity strategies. Over a comprehensive sample window spanning September 16, 2019, through September 17, 2026—representing approximately 7.01 years of closed market data—the strategy completed 160 trades. Across this period, the model accumulated a total closed-trade percentage sum of +1336.22%, translating to an annualized return metric of 164.79%. Of the 160 historical execution cycles, 129 concluded in profitable exits while 31 generated losses, establishing an overall win rate of 80.63%. The primary statistical profile reveals a high-frequency signal detection system coupled with selective execution, generating a steady progression of trades while maintaining explicit control over market exposure.
Trading rhythm and position duration
Although the algorithm utilizes 15-minute candlestick data to monitor price structure and evaluate potential signal triggers, its execution profile is distinctly characteristic of multi-day swing trading rather than short-term intra-day scalping. The strategy logs an average holding duration of 187.54 hours (approximately 7.8 days) and a median holding duration of 156.75 hours (roughly 6.5 days). Exits occur with moderate spacing, averaging 15.97 days between completed trades, with a median exit interval of 13.98 days. On an annualized basis, the model produces 22.84 completed trades per year, which corresponds to approximately 2.05 trades per active month. This sparse trade frequency indicates that automated trading bots deploying this strategy will experience extended periods of market observation punctuated by selective trade engagements. The multi-day position duration allows the algorithm to capture meaningful directional swings in APD while filtering out noise inherent to lower timeframes.
Quality of historical results
The strategy demonstrates robust payoff distribution, highlighted by a strong alignment between average and median trade metrics. The average closed trade yield stands at +1.95%, while the median trade yield reaches +2.20%. The fact that the median trade return exceeds the average trade return indicates that individual gains are consistently positive across the trade sample rather than being artificially skewed upward by a small number of massive outliers. Further proof of broad-based performance lies in profit concentration: the strategy's top three winning trades accounted for just 8.40% of total gross gains. The single best trade generated a return of +13.38%, whereas the worst individual loss recorded was -11.17%. The profit factor is established at 1.25. While an 80.63% win rate represents an impressive hit frequency, the profit factor of 1.25 reflects the quantitative trade-off inherent in the model: winning trades occur with high regularity, but individual losing trades absorb a proportional segment of gross profits.
Risk, drawdown and losing behavior
Risk characteristics across the 7-year evaluation period reflect a well-controlled equity curve. The strategy experienced a maximum peak-to-trough historical drawdown of 14.83%. In the context of an overall cumulative return sum of +1336.22%, this peak drawdown represents a controlled magnitude of downside volatility. The model's loss containment is further illustrated by its sequence patterns: the longest winning streak extended to 20 consecutive trades, whereas the longest losing streak was contained to just 3 consecutive losses. Containing losing runs to three trades prevents prolonged drawdowns and helps preserve trading capital during adverse market conditions. The worst historical trade loss of -11.17% remains within reasonable proximity of the maximum strategy drawdown of 14.83%, indicating that drawdowns were primarily driven by isolated trade losses or minor clusters rather than compound structural failures. For quantitative trading agents executing this logic, these risk parameters offer predictable historical bounds envelope.
Behavior through time and yearly stability
An examination of the yearly breakdown reveals consistent profitability across every calendar year evaluated, though with notable variation in performance intensity and trade volume. In 2019, across a partial sample, the strategy logged 5 trades with a 100% win rate and a cumulative return of +10.96%. In 2020, activity expanded to 27 trades, achieving an 85.19% win rate and generating a peak annual sum of +78.39%. Performance remained strong through 2021 (18 trades, 77.78% win rate, +33.58%), 2022 (22 trades, 77.27% win rate, +37.76%), and 2023 (24 trades, 83.33% win rate, +34.20%). The year 2024 represented a transitional regime, marked by a drop in win rate to 50.00% across 16 trades, with yields totaling +7.15%. However, the strategy rebounded strongly in 2025, recording 28 trades with an 82.14% win rate and a combined return of +45.35%. In 2026, performance accelerated further, logging 20 trades, a 95.00% win rate, and +64.74% in sum returns. This multi-year distribution confirms that positive closed returns are distributed across multiple market environments rather than isolated to a single fortunate year.
Recent period since 2024-06-01 versus full history
Evaluating performance in the recent period beginning June 1, 2024, provides vital insight into the current viability of the strategy's signal logic. Across this recent window, the strategy executed 57 closed trades, accumulating a cumulative return sum of +113.21%. This recent trade volume accounts for more than one-third of the total 160 completed trades in the entire dataset, indicating heightened trade frequency in recent market conditions. The recent gain of +113.21% confirms that the strategy navigated recent volatility effectively, bouncing back decisively from the lower win-rate environment observed in early 2024. Comparing this recent window to the full 7-year track record indicates that the algorithm's core mechanics remain well-aligned with ongoing price dynamics in APD. The presence of 57 closed trades in this period ensures that the recent sample provides a statistically meaningful subset rather than a sparse anomaly.
Strengths and limitations
The primary statistical strength of this algorithm lies in its exceptional win rate of 80.63% combined with strict drawdown containment capped at 14.83%. The model benefits from exceptional trade distribution, where no single trade dominates performance, as evidenced by the low 8.40% top-three winner concentration. Furthermore, short losing streaks of no more than 3 consecutive trades support operational resilience for automated trading bots. Conversely, the principal limitation is the strategy's profit factor of 1.25. Despite winning four out of five trades, the ratio of gross gains to gross losses remains modest due to downside asymmetry where individual losses (worst trade -11.17%) can offset multiple average gains (+1.95%). Additionally, with an average of 22.84 trades per year, trading robots using this logic must maintain patience during multi-week lulls between exit signals.
DevioLab analytical conclusion
The APD 15-minute quantitative strategy presents a robust profile characterized by persistent historical accuracy, disciplined drawdown containment, and steady multi-year capital accumulation. Earning a DevioLab score of 82.12 and the top rank for APD, the strategy demonstrates how automated trading agents can leverage 15-minute price data to execute disciplined multi-day swing positions. While the profit factor of 1.25 highlights that loss management is crucial to sustaining net returns, the high hit rate of 80.63% and minimal gross profit concentration confirm that performance is built upon repeatable, broad-based statistical edges. For quant analysts and automated system builders, the strategy offers a tested model with a track record extending from 2019 through 2026.
Data scope and methodology
The findings in this report are based strictly on backtested closed-trade statistics for APD across a historical testing range from September 16, 2019, to September 17, 2026. All reported returns, win rates, drawdowns, and duration metrics represent simulated historical executions calculated on 15-minute bar data. Percentages describe closed-trade sums and statistical averages; they do not account for live broker slippage, trading commissions, borrowing costs, or execution delays, nor do they represent actual trading account equity changes. Past historical results do not guarantee future performance. This analysis is produced solely for quantitative research and software development evaluation and does not constitute financial or investment advice.