ADSK · Autodesk Inc.
Stock Universe · global stock marketADSK Algorithmic Trading Strategy Analysis: High Win-Rate Structure and Long-Duration Holding Patterns in 15-Minute Data
This quantitative evaluation analyzes a top-ranked algorithmic trading strategy applied to ADSK stock across a 6.99-year historical testing window from September 2019 to September 2026. Evaluated on a 15-minute chart interval, the strategy achieved a 1st-rank position for ADSK on DevioLab with a proprietary score of 85.18. The backtest exhibits a cumulative trade-sum return of 2996.48% across 43 completed trades, driven by an exceptional win rate of 88.37% (38 wins and 5 losses) and a profit factor of 6.25. Despite operating on a granular 15-minute timeframe, the strategy exhibits an extended holding horizon, averaging 695.49 hours (approximately 29 days) per position and generating an average of 6.15 trades per year. Risk parameters remained tightly controlled throughout the sample, with a maximum drawdown of 11.54% that aligns directly with the worst single losing trade of -11.54%. This research provides systematic insight into how infrequent, long-duration position management can form the algorithmic foundation for automated trading bots, trading robots, and quantitative trading agents seeking selective exposure in equity markets.
Analysis as of 12.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The evaluated algorithmic strategy, designated in research records as ADSK 0 +2996.48% 1TRAD-NMF2, represents the primary quantitative model for ADSK stock within the DevioLab database, earning a rank of 1 for the ticker and an overall DevioLab score of 85.18. Operating on a 15-minute candle interval across a full sample period of 6.99 years between September 16, 2019, and September 12, 2026, the strategy recorded 43 completed trade cycles. Cumulative historical performance across all closed trades reached a total trade-sum return of 2996.48%, translating to an annualized model calculation of 201.42%. The model demonstrates remarkable selectivity, generating only 6.15 completed transactions per year or approximately 1.16 trades per active trading month. Designed to serve as executable decision logic for automated trading bots, trading robots, and algorithmic trading agents, the strategy emphasizes high statistical edge per trade rather than high execution frequency.
Trading rhythm and position duration
Although the underlying data structure relies on a 15-minute candlestick chart, the strategy's operational rhythm reflects a patient, multi-week holding style rather than intraday noise capturing. The average holding duration per trade stands at 695.49 hours (roughly 28.98 calendar days), while the median holding time is 383.75 hours (approximately 15.99 calendar days). This substantial gap between average and median holding hours suggests that while half of the positions resolve within two to three weeks, a subset of trades remains open for extended periods to capture prolonged directional trends. Furthermore, the spacing between trade exits averages 57.69 days, with a median of 43.58 days between consecutive transaction closures. With only 6.15 trades per year, automated trading bots executing this script spend long intervals waiting for high-conviction setup conditions. The combination of fine-grained intraday price monitoring with multi-week position holding allows trading agents to pinpoint structural entries without overtrading.
Quality of historical results
The statistical quality of the strategy's trades is defined by high directional accuracy paired with solid payoff symmetry. Out of 43 completed trades, 38 were profitable and 5 resulted in losses, establishing an 88.37% win rate. The strategy generated a profit factor of 6.25, indicating that total gross gains exceeded total gross losses by more than six times. Across all executed trades, the average gain per trade was 9.03%, while the median trade yield was 7.52%. The closeness of the average and median returns indicates that positive performance was distributed fairly evenly throughout the trade set rather than being skewed by isolated anomalies. The largest single winning trade achieved a return of 39.46%, while the top three winning transactions collectively accounted for 27.41% of total gross profits. This moderate concentration level demonstrates that while individual tail-event winners added meaningfully to total returns, the strategy did not depend single-handedly on one or two outlier trades to establish its historical profitability.
Risk, drawdown and losing behavior
Risk containment represents a central feature of the historical record. Over nearly seven years of simulated history, the strategy experienced a maximum drawdown of 11.54%. Notably, this maximum drawdown figure corresponds almost exactly to the single worst trade in the dataset, which recorded a loss of -11.54%. Because the strategy recorded a maximum consecutive losing streak of only 1 trade, historical equity declines were predominantly single-event drawdowns rather than compounded multi-trade losing series. Conversely, the strategy achieved a maximum winning streak of 17 consecutive profitable trades. The tight cluster between worst trade (-11.54%) and maximum drawdown (11.54%), combined with a 6.25 profit factor, highlights a historical profile where losing trades were promptly recognized and contained before deep equity erosion could materialize. For automated trading robots, this historical behavior implies a relatively smooth equity path with brief, isolated drawdowns.
Behavior through time and yearly stability
An examination of the annual breakdown reveals consistent profitability across all individual calendar years in the test sample, though execution density and return magnitude varied. In 2020, the strategy completed 8 trades, achieving a 100% win rate and a cumulative return sum of 125.30%. Performance remained perfect in directional accuracy through 2021 (5 trades, 100% win rate, +24.55%), 2023 (6 trades, 100% win rate, +41.75%), and 2024 (6 trades, 100% win rate, +80.93%). In 2022, during broader equity market adjustments, the strategy maintained an 83.33% win rate across 6 trades (5 wins, 1 loss), accumulating +47.46%. In the final years of the sample, 2025 yielded 3 trades (2 wins, 1 loss, +35.63%, 66.67% win rate), while 2026 recorded 9 trades (6 wins, 3 losses, +32.87%, 66.67% win rate). Across all seven calendar years, the strategy posted positive annual trade-sum totals, demonstrating structural resilience across diverse market phases.
Recent period since 2024-06-01 versus full history
The evidence window covering trades closed on or after June 1, 2024, provides a robust sample to compare recent market adaptation against full historical baselines. In this recent period, the strategy completed 16 trades—representing more than 37% of its total lifetime transactions—and generated a combined return sum of 141.10%. This high concentration of recent closed trades indicates an acceleration in trade completions during 2024–2026 compared to the lower annual trade counts observed between 2021 and 2023. Despite the higher trade volume in the recent window, overall performance remained strong, contributing nearly 141 percentage points of positive return sum. This substantial recent sample confirms that the strategy's activity remained fully operational and productive in the final two years of the backtest window rather than stagnating or degrading in changing market environments.
Strengths and limitations
The key statistical strengths of this ADSK strategy include its high win rate of 88.37%, a high profit factor of 6.25, modest top-winner concentration (27.41%), and a drawdown ceiling capped at 11.54%. The long average holding period of nearly 29 days reduces trade turnover, limiting operational stress for automated trading agents. However, important quantitative limitations must be considered. The total sample size consists of 43 completed trades over 6.99 years, resulting in a low annual sample size of 6.15 trades. Statistical inference derived from 43 total events carries wider confidence intervals than strategies with hundreds of executions. Additionally, holding positions across multi-week periods exposes open trades to overnight gap risks and corporate news events that cannot be neutralized intraday. Finally, historical backtest returns do not include real-world execution friction such as spread costs or slippage.
DevioLab analytical conclusion
With a DevioLab score of 85.18 and a rank of 1 for ADSK, the strategy represents a highly refined model characterized by high selectivity, extended trade durations, and controlled downside metrics. Its reliance on long holding periods (average 695.49 hours) allows it to capture substantial swing moves while keeping trade frequency low. The alignment of an 88.37% historical win rate with a maximum drawdown of only 11.54% underscores a historically robust asymmetry between gains and losses. Quant traders evaluating this model for deployment within automated trading bots or trading robots should weigh the low total trade count (43 trades) against its consistent multi-year profitability and strong recent performance since mid-2024.
Data scope and methodology
The analytical insights in this report are derived strictly from simulated backtest data generated by DevioLab.com for ADSK stock on a 15-minute timeframe between September 16, 2019, and September 12, 2026. All reported percentage gains, trade sums, drawdowns, and duration metrics represent closed historical simulated transactions. These metrics do not represent live account trading results, guaranteed future returns, or formal financial advice. Market parameters such as execution slippage, broker commissions, borrow costs, and liquidity constraints were not factored into these raw historical calculations.