ALGN · Align Technology Inc.
Stock Universe · global stock marketALGN Quantitative Strategy Analysis: Evaluating a 76.58% Win-Rate 15-Minute Swing Trading Logic for Align Technology
This empirical report provides a comprehensive quantitative examination of the ALGN algorithmic trading strategy, identified internally as ALGN 0 +5656.70% 1TRAD-TPG7. Operating on a 15-minute price evaluation frame over a seven-year dataset spanning September 2019 through September 2026, the strategy achieved a 76.58% win rate, a profit factor of 5.00, and a total cumulative gain of 5,656.70% across 111 completed trades. Holding positions for an average of 336.23 hours (median 217.00 hours), the algorithm operates as a selective, multi-week swing trading model suitable as the decision logic for automated trading bots, trading robots, and quantitative trading agents. With a DevioLab Score of 73.08, the system ranks second overall for ALGN stock. This study analyzes the strategy's structural strengths, including low winner concentration and unbroken multi-year profitability, alongside key risk parameters such as a maximum historical drawdown of 43.78%.
Analysis as of 14.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The subject of this quantitative evaluation is an algorithmic trading strategy developed for ALGN stock (Align Technology, Inc.) on a 15-minute bar timeframe (15m). Covering a testing history from September 16, 2019 to September 14, 2026, the strategy encompasses exactly seven years of backtested execution data. Over this multi-year evaluation span, the model completed 111 trades, generating a total cumulative profit of 5,656.70% and an annualized performance metric of 234.57%. On DevioLab.com, the strategy holds an official DevioLab Score of 73.08, positioning it as the second-ranked quantitative strategy for ALGN under the core independent selection criteria. Designed to function as the core decision logic for automated trading bots, algorithmic trading robots, and execution agents, the model evaluates market data at 15-minute intervals to identify entry and exit signals. Despite evaluating intraday candle data, the algorithm does not engage in high-frequency trading or intraday scalping. Instead, it systematicallyfilters short-term noise to establish directional positions in ALGN stock. The long-term historical sample provides a robust statistical foundation for analyzing how the algorithm handles varying equity regimes, shift in volatility, and market cycles.
Trading rhythm and position duration
Analyzing the time distribution of closed trades reveals that the strategy operates with a moderate-frequency, long-duration swing trading profile. While signals are generated on a 15-minute candlestick chart, positions are held over extended time horizons. The strategy records an average holding duration of 336.23 hours (approximately 14.0 calendar days) and a median holding duration of 217.00 hours (roughly 9.0 calendar days). The right-skewed gap between median and average holding times indicates that while most trades resolve within one to two weeks, a subset of trades remains open for several weeks to capture sustained trends in ALGN. This long-duration position structure results in a controlled trade cadence. The strategy produces an average of 15.86 completed trades per year, which translates to approximately 1.61 trades per active month. Furthermore, the average spacing between consecutive trade exits is 23.19 days, with a median spacing of 19.01 days. This low-frequency output means that automated trading agents executing this strategy experience prolonged periods of position maintenance punctuated by periodic signal rebalancing. The strategy prioritizes trade selectivity over transaction volume, minimizing operational activity while maintaining continuous exposure to medium-term price trends.
Quality of historical results
The historical performance quality of the strategy is characterized by exceptionally strong win-rate consistency and structural efficiency. Across 111 completed trades, 85 closed in profit while 26 closed at a loss, yielding a win rate of 76.58%. The profit factor stands at 5.00, meaning that total gross gains generated by winning trades exceeded total gross losses from losing trades by a five-to-one ratio. A granular inspection of return distributions highlights the broad-based nature of these gains. The average trade return across all 111 closed positions is 4.34%, which aligns closely with the median trade return of 4.42%. This near-perfect alignment between average and median expectations confirms that strategy returns are not artificially inflated by a handful of extreme positive outliers. Furthermore, the top three winning trades combined account for only 12.44% of total gross profit. This low winner concentration demonstrates that profitability is evenly distributed across the 85 winning trades rather than dependent on lucky single events. While the best single trade achieved a gain of 42.30%, the algorithm consistently accumulates positive expectancy through modest, repeatable trade outcomes.
Risk, drawdown and losing behavior
While the strategy demonstrates high historical return metrics, a rigorous examination of risk factors reveals meaningful drawdown exposure. The maximum historical drawdown recorded across the seven-year dataset reached 43.78%. Notably, the strategy worst individual trade loss was -42.80%. The close correspondence between the worst single trade loss (-42.80%) and the maximum overall drawdown (43.78%) indicates that historical equity pullbacks were largely driven by isolated, severe adverse trade events rather than compounding sequences of consecutive losses. This structural risk behavior is further reflected in the strategy streak metrics. The algorithm maximum consecutive losing streak was limited to just 3 trades, whereas its longest winning streak reached 10 consecutive profitable trades. The low losing streak count underscores that the high win rate (76.58%) acts as a dynamic cushion against multi-trade compounding losses. However, the presence of a -42.80% worst trade demonstrates that when losing trades do occur, open equity can experience significant retracement prior to trade closure. Automated trading robots deploying this logic must be engineered to withstand substantial peak-to-trough mark-to-market fluctuations during adverse position developments.
Behavior through time and yearly stability
Evaluating the strategy year-by-year breakdown across eight calendar years (2019 through 2026) reveals exceptional annual consistency. The strategy generated net positive return sums in every single calendar year evaluated. In 2019, across a partial sample of 3 trades, the strategy achieved a 100% win rate and a cumulative return sum of 33.45%. During 2020, trade frequency expanded to 19 trades, delivering a 73.68% win rate and a net return sum of 141.89%. Subsequent years demonstrated consistent trade frequency and win-rate stability. In 2021, the algorithm generated 15 trades with an 80.00% win rate (+64.49% sum). In 2022, a challenging period for many equity assets, the strategy executed 20 trades with a 70.00% win rate and positive net performance of 37.11%. In 2023, 17 trades yielded a 76.47% win rate (+45.54% sum). The consistency continued into 2024 (16 trades, 81.25% win rate, +61.76% sum), 2025 (12 trades, 75.00% win rate, +64.14% sum), and the partial 2026 period (9 trades, 77.78% win rate, +32.80% sum). Annual win rates remained tightly bounded between 70.00% and 81.25% across all full years, illustrating remarkable structural stability across changing market regimes.
Recent period since 2024-06-01 versus full history
A critical benchmark for evaluating algorithmic validity is performance in the recent testing window. Examining completed trades with exit timestamps on or after June 1, 2024, provides a clean out-of-sample perspective relative to earlier historical years. In this recent window, the strategy executed 30 completed trades, accumulating a net sum of returns equal to 126.13%. With 30 completed trades occurring since June 1, 2024, the recent window represents 27.03% of the total 111-trade historical dataset. Comparing the recent sample against the full dataset confirms that the algorithm performance characteristics have remained fully consistent. The trade output rate during this recent window (30 trades over roughly 2.25 years, or ~13.3 trades per year) aligns closely with the full-history average of 15.86 trades per year. The robust performance of +126.13% across 30 recent trades indicates that the strategy predictive edge in ALGN stock has not suffered from alpha decay or structural degradation in recent market conditions.
Strengths and limitations
The empirical data highlights several clear structural strengths for this ALGN quantitative model. First, a high win rate of 76.58% combined with a profit factor of 5.00 establishes exceptional historical trade efficiency. Second, the strategy demonstrates balanced profitability without reliant tail risk: the top three winning trades generate only 12.44% of gross profit, and the median trade return (4.42%) closely matches the average trade return (4.34%). Third, the strategy exhibits unbroken annual profitability across eight consecutive calendar years from 2019 to 2026. Conversely, material limitations must be recognized. The primary risk constraint is the maximum drawdown of 43.78% and a worst individual trade loss of -42.80%, indicating that unhedged open positions can suffer deep drawdowns before exit signals are triggered. Additionally, with an average holding time of 336.23 hours and only 15.86 trades per year, the strategy requires low execution frequency and extended holding periods. Operators deploying automated trading bots must exercise capital patience, as low trade volume requires extended time horizons to manifest its statistical expectancy.
DevioLab analytical conclusion
With a DevioLab Score of 73.08 and a rank of #2 for ALGN stock, strategy ALGN 0 +5656.70% 1TRAD-TPG7 stands out as a highly efficient quantitative swing trading model. The combination of a 76.58% win rate, a 5.00 profit factor, and a decentralized profit structure makes it a compelling candidate logic for automated trading robots and quantitative agents seeking medium-term directional exposure in Align Technology. However, potential operators must balance the strategy high profit factor against its 43.78% historical maximum drawdown profile. Because individual losing trades can reach magnitude (-42.80%), strict portfolio sizing and risk management guidelines are essential when integrating this logic into automated trading systems. Overall, the statistical evidence validates the strategy robustness across multiple market cycles and recent trading regimes.
Data scope and methodology
This quantitative analysis is based strictly on historical backtested closed trade performance for ALGN stock (15m timeframe) covering the sample period from September 16, 2019 to September 14, 2026. All reported statistics—including the 111 completed trades, 76.58% win rate, 5.00 profit factor, 43.78% drawdown, and yearly breakdowns—are derived from simulated execution logic. The data provided reflects closed trade performance and does not account for live broker execution variables, market slippage, exchange fees, liquidity constraints, or margin borrowing costs. Recent statistics post-2024-06-01 reflect trades whose exit timestamps occurred on or after June 1, 2024 UTC. Simulated historical results provide quantitative insight into strategy mechanics but do not guarantee future profitability in live execution environments.