ADI · Analog Devices Inc.
Stock Universe · global stock marketADI Quantitative Analysis: A High-Win-Rate Multi-Day Algorithmic Strategy on Analog Devices
This quantitative research report evaluates the top-ranked algorithmic trADIng model for Analog Devices (ADI) evaluated across a nearly seven-year backtest dataset spanning September 2019 to September 2026. Designed for implementation via automated trADIng bots and execution agents operating on a 15-minute price series, the strategy demonstrates a distinctive structural profile characterized by low trADIng frequency, multi-day holding periods, and exceptional payoff consistency. Over 129 completed trades, the strategy achieved an 81.40 percent win rate, a profit factor of 3.75, and a maximum drawdown limited to 10.39 percent, culminating in a cumulative closed-trade gain of 10245.80 percent. Notably, gross profits show almost no dependency on statistical outliers, with the three largest winning trades contributing just 9.11 percent of total gross gains. This analysis examines the strategy's temporal distribution, risk parameters, and recent performance persistence to assess its structural viability.
Analysis as of 12.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The strategy under examination represents the leADIng quantitative model for ADI within the DevioLab framework, earning a DevioLab score of 86.35 and holding the top rank for this asset. Built upon a 15-minute price dataset for Analog Devices, the strategy translates short-term price signals into multi-day position logic, making it particularly well suited for deployment within automated trADIng bots, trADIng robots, and programmatic trADIng agents. Over an evaluation span of approximately 6.99 years, running from September 16, 2019, through September 12, 2026, the strategy completed 129 total trades. The long-term return profile displays a cumulative closed-trade sum of 10245.80 percent, translating to an annualized backtested metric of 393.78 percent. Key performance parameters include an 81.40 percent win rate across 105 winning and 24 losing trades, supported by a profit factor of 3.75 and a maximum historical drawdown of 10.39 percent. Rather than relying on constant market participation, the system operates selectively, using granular timeframe analysis to capture distinct medium-term swings in the underlying equity.
TrADIng rhythm and position duration
A critical structural characteristic of this strategy is the stark contrast between its 15-minute execution interval and its patient position holding times. While the underlying price data is sampled every 15 minutes, the strategy operates as a moderate-frequency swing system rather than an intraday scalper. Across the full dataset, the system executed an average of 18.45 trades per year, which corresponds to approximately 1.70 trades per active month. Position durations reflect a multi-day holding architecture: the average holding time stands at 258.30 hours (approximately 10.76 days), whereas the median holding duration is 168.50 hours (approximately 7.02 days). The divergence between the median and average holding times indicates that while most trades resolve within a single week, a subset of positions remains open significantly longer to absorb extended trend moves. Furthermore, the average elapsed time between trade exits is 19.74 days, with a median of 15.98 days. For trADIng agents executing this logic, this rhythm translates into prolonged periods of flat exposure interspersed with highly selective market entries, minimizing churn and keeping trade exposure targeted.
Quality of historical results
The strategy's performance profile is anchored by a combination of high hit-rate precision and well-distributed returns across its trade distribution. The system recorded 105 winning trades against 24 losing trades, establishing an 81.40 percent win rate. The average return per completed trade reached 3.98 percent, while the median trade return stood closely behind at 3.12 percent. This tight alignment between average and median expectations indicates that positive outcomes are structural across typical trades rather than skewed by rare spikes. Payoff asymmetry is also favorable: the best single historical trade generated a return of 18.37 percent, whereas the worst single trade resulted in a loss of -7.26 percent. Perhaps the most compelling quality metric is the extreme balance of gross profitability. The top three winning trades accounted for only 9.11 percent of total gross profits. In quantitative system design, low profit concentration confirms that backtested success stems from repeatable edge across dozens of trades rather than a fortunate cluster of massive market spikes.
Risk, drawdown and losing behavior
Risk containment within this strategy appears disciplined throughout the nearly seven-year backtest window. The maximum historical drawdown was restricted to 10.39 percent, a relatively modest drawdown depth given the strategy's high cumulative equity growth. This controlled equity curve is further illustrated by sequence analysis: the strategy experienced a maximum winning streak of 15 consecutive trades, compared to a maximum losing streak of just 3 trades. This short losing sequence prevented severe compounding equity contractions during adverse market phases. The overall profit factor of 3.75 reflects a healthy ratio of gross profits to gross losses, demonstrating that accumulated gains substantially outweighed downside write-offs. Because the single worst historical trade was capped at -7.26 percent, the system successfully avoided catastrophic tail losses. However, because position durations regularly extend over several days, holding positions overnight and through earnings announcements introduces gap risk that historical backtest engines cannot fully eliminate.
Behavior through time and yearly stability
An analysis of yearly performance reveals remarkable consistency across changing market regimes from 2019 through 2026. The strategy delivered positive aggregate trade returns in every single calendar year of the backtest. In 2019, during the initial partial testing period, the system completed 3 trades with a 100.00 percent win rate and a total return of 12.90 percent. During the volatile market environment of 2020, it logged 15 trades with an 86.67 percent win rate and 82.44 percent return sum. In 2021, performance remained strong with 19 trades, an 89.47 percent win rate, and an 82.03 percent return sum. Despite macro headwinds in the semiconductor sector during 2022, the system maintained a 70.00 percent win rate across 20 trades, generating 59.92 percent in combined returns. Momentum recovered in 2023 with 21 trades, an 80.95 percent win rate, and a 67.13 percent total return. The pattern continued into 2024 (17 trades, 82.35 percent win rate, 47.66 percent total return), 2025 (18 trades, 77.78 percent win rate, 87.99 percent total return), and 2026 (16 trades, 81.25 percent win rate, 73.25 percent total return). Trade frequency remained stable at 15 to 21 trades per full calendar year, underscoring consistent signal generation.
Recent period since 2024-06-01 versus full history
Evaluating recent historical performance from June 1, 2024, through the end of the dataset on September 12, 2026, provides an updated view of strategy efficacy in contemporary market conditions. During this recent window, the strategy completed 42 trades, representing nearly one-third of its total historical trade count. These 42 recent trades generated a cumulative sum of closed-trade returns equal to 177.38 percent. The relatively high density of trades in this period (42 trades over approximately 27 months) indicates sustained signal activity without degradation of trade cadence. Comparing recent output to the full historical baseline confirms that the strategy's core dynamics remained intact throughout 2024, 2025, and 2026. Rather than experiencing alpha decay, the quantitative rules continued to identify profitable multi-day swing opportunities on ADI, producing substantial closed returns in line with long-term performance expectations.
Strengths and limitations
The strategy exhibits several distinct statistical strengths. Its primary advantage is a high win rate of 81.40 percent combined with an exceptionally low profit concentration, where the top three winning trades generate less than 10 percent of gross gains. A profit factor of 3.75 and a limited drawdown of 10.39 percent further reflect robust risk-adjusted historical performance. Additionally, the low annual trade frequency reduces operational complexity for automated execution models. Conversely, key limitations stem from sample size and holding architecture. With 129 total trades over 6.99 years, the statistical sample size is moderate, meaning individual trades carry higher weight than in high-frequency models. Furthermore, average holding durations exceeding 10 days expose positions to broader equity market gaps, overnight risk, and macro news events. Finally, backtested percentage sums represent ideal trade calculations and do not incorporate real-world execution friction such as brokerage commissions, variable bid-ask spreads, or slippage.
DevioLab analytical conclusion
With a top ranking for ADI and a DevioLab score of 86.35, this algorithmic strategy presents a compelling case study in low-frequency, multi-day swing trADIng on a 15-minute price foundation. The strategy successfully combines high win rates with minimal concentration risk, generating consistent returns across diverse annual market cycles without relying on rare extreme winners. For quantitative analysts and operators of automated trADIng bots or programmatic trADIng agents, the strategy offers a structured framework that prioritizes trade selectivity over high turnover. However, prospective implementers must account for multi-day market risk and ensure execution infrastructure can manage overnight equity exposure. As with all backtested strategy evaluations, historical metrics serve as an analytical benchmark rather than a guarantee of future live performance.
Data scope and methodology
This analysis is based entirely on historical simulated closed-trade performance statistics generated for asset ADI (Analog Devices) over the testing period from September 16, 2019, to September 12, 2026 (6.99 years). The strategy utilizes a 15-minute chart interval to generate signals. All reported percentage gains, profit factors, win rates, and drawdown figures represent closed-trade mathematical outputs derived from backtested simulation rules. Summed percentage returns describe the uncompounded aggregate of individual trade outcomes and do not reflect specific brokerage account returns, leverage, compounding, slippage, or transaction fee structures. Recent performance calculations covering trades closed on or after June 1, 2024, comprise 42 completed trades. This research paper is provided exclusively for quantitative research and educational purposes and does not constitute financial or investment advice.