ALNY · Alnylam Pharmaceuticals, Inc.
Stock Universe · mercado bursátil globalStatistical Analysis of ALNY Quantitative Strategy: High-Selectivity Trend Capture and Long-Horizon Position Holding in Equity Automation
This quantitative research study provides an exhaustive evaluation of the top-ranked algorithmic trading strategy for ALNY on a 15-minute execution timeframe. Engineered for automated trading bots and algorithmic trading agents seeking robust capital preservation alongside substantial structural gains, the model achieves a DevioLab score of 86.69, holding the number one rank for the symbol. Across a 6.96-year historical evaluation window encompassing 55 closed trades from September 2019 to September 2026, the strategy generated an 83.64% win rate and a profit factor of 7.50, constrained by a historical maximum drawdown of 17.35%. Featuring an average holding duration of 680.18 hours (approximately 28.3 days) and an average interval between trade exits of 44.41 days, the strategy demonstrates a patient, low-frequency posture designed to capture sustained multi-week equity moves while filtering out short-term market noise.
Análisis al 15.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
1. Strategy profile
The evaluated algorithmic model, designated as ALNY 0 +15798.86% 1TRAD-TPG6, represents the primary core strategy for ALNY equity asset research within the DevioLab quantitative ecosystem. Achieving a score of 86.69 and securing the rank of number one for the ticker, this model was selected under the core balanced optimal selection criteria. Though built upon a 15-minute price bar interval, the strategy operates as a low-frequency, macro-oriented algorithmic trend-following framework rather than a high-frequency trading system. Over a historical backtest span of approximately 6.96 years—stretching from September 16, 2019, through September 1, 2026—the strategy logged a total of 55 completed trades. It is designed to serve as the structural logic for automated trading bots, trading robots, and quantitative agents operating in stock market environments, delivering systematic trade generation, precise risk budgeting, and emotion-free execution parameters without requiring intraday manual intervention.
2. Trading rhythm and position duration
A defining characteristic of this strategy is its deliberate, highly selective execution cadence. Over the nearly seven-year backtest window, the algorithm executed an average of only 7.90 trades per year, which translates to roughly 1.28 trades per active trading month. The temporal spacing between position liquidations is substantial, averaging 44.41 days between exits, with a median exit interval of 35.50 days. Position holding durations reflect a distinct multi-week horizon: the average position remains open for 680.18 hours (approximately 28.3 calendar days), while the median holding duration stands at 384.00 hours (16 calendar days). This structural divergence between average and median holding times indicates that while routine trades settle within approximately two to three weeks, exceptional trending conditions lead the algorithm to hold positions open for extended periods to extract maximum value. By maintaining market exposure only during specific high-probability structural setups, the trading robot avoids over-trading and minimizes exposure during low-conviction, choppy market regimes.
3. Quality of historical results
The strategy demonstrates exceptional return asymmetry and hit-rate consistency across its historical record. Out of 55 completed trade cycles, 46 resulted in positive closed returns while only 9 ended in losses, yielding a high historical win rate of 83.64%. The overall profit factor reaches 7.50, demonstrating that gross historical profits exceeded gross historical losses by seven and a half times. The mathematical balance between typical returns and tail performance is notably healthy: the average trade generated +10.88%, while the median trade yielded +6.80%. The system's best historical trade reached +66.83%, whereas the single worst trade registered a drawdown exit of -17.35%. Crucially, the system is not heavily reliant on a tiny handful of anomalous spikes; the three largest winning trades account for 27.16% of total gross profit. This indicates that while outlier winning trades do enhance cumulative performance, the bulk of gains is broadly distributed across the 46 winning transactions.
4. Risk, drawdown and losing behavior
Risk containment within the model appears tightly structured relative to cumulative upside performance. The maximum recorded historical portfolio drawdown stands at 17.35%, which precisely matches the loss severity of the strategy's worst single trade (-17.35%). This alignment implies that historical drawdowns were primarily driven by individual trade stopouts or sharp mean-reversions rather than cascading, multi-trade compounding losses. The strategy's serial loss characteristics reinforce this resilience: the longest historical losing streak consisted of just 1 single losing trade, meaning the algorithm never experienced back-to-back losing positions throughout the entire 55-trade sample. Conversely, the strategy achieved a maximum consecutive winning streak of 16 trades. However, because position durations span several weeks, a drawndown state caused by a single adverse exit may require several months of holding time to fully recover, representing a psychological test of patience for operators running automated trading agents.
5. Behavior through time and yearly stability
An examination of the yearly breakdown statistics reveals consistent profitability with shifting annual trade frequency and total return magnitude. In 2020, the strategy completed 9 trades, all 9 of which were profitable (100% win rate), delivering a cumulative trade sum of +227.68%. In 2021, performance remained perfect in hit rate, generating 7 wins across 7 trades (100% win rate) for a combined trade sum of +73.13%. The year 2022 saw the highest annual activity with 12 trades, resulting in 8 wins and 4 losses (66.67% win rate) and a total return sum of +80.71%. The year 2023 experienced a slower consolidation environment, yielding 6 trades (4 wins, 2 losses, 66.67% win rate) with a modest positive sum of +10.98%. Activity rebounded in 2024 with 5 trades, all winning (100% win rate), delivering +72.88%. In 2025, the model logged 8 trades (6 wins, 2 losses, 75.00% win rate) totaling +74.79%, while the partial year 2026 registered 8 trades (7 wins, 1 loss, 87.50% win rate) totaling +58.18%. Every single calendar year in the sample maintained a net positive sum of trades, highlighting strong structural adaptation across varying equity market environments.
6. Recent period since 2024-06-01 versus full history
Evaluating performance during the recent sample window from June 1, 2024, through September 1, 2026, provides insight into the strategy's contemporary market alignment. During this 27-month recent period, the strategy closed 21 trades, accumulating a combined trade sum of +205.85%. This represents a notable acceleration in trade frequency compared to its long-term baseline: approximately 38% of all historical trades occurred within this recent window. Despite the increased execution density, the return quality remained intact, averaging nearly +9.80% per closed trade during this phase. This substantial sample of 21 recent completed trades confirms that the strategy's statistical edge has remained active and operational in recent stock market conditions, rather than relying solely on historical gains achieved early in the 2019–2020 test period.
7. Strengths and limitations
The principal structural strength of this strategy lies in its combination of a high win rate (83.64%), a robust profit factor (7.50%), and a disciplined risk profile that caps historical peak-to-trough drawdown at 17.35%. Its low gross-profit concentration (top 3 winners at 27.16%) confirms broad-based profitability across trade cycles. The primary limitation stems from its sparse signal generation and extended holding periods. Averaging under 8 trades per year with holding times exceeding 28 days, automated trading bots executing this logic will experience long stretches of inactivity and multi-week exposure to overnight gap risk, earnings announcements, and broader equity market shifts. Operators must be comfortable with low execution volume and refrain from manual intervention during prolonged holding phases.
8. DevioLab analytical conclusion
With a top-tier DevioLab score of 86.69 and the number one ranking for ALNY, this algorithmic model stands out as a highly effective core framework for automated equity trading. Its statistical architecture balances conservative exposure control with powerful upside profit capture. The strategy's history shows an ability to navigate both aggressive multi-month rallies and choppy market regimes without succumbing to consecutive losing spirals. For quantitative traders and developers configuring automated trading agents, the strategy offers a compelling template for medium- to long-horizon trend capture, provided that deployment expectations are aligned with its low-frequency, multi-week holding behavior.
9. Data scope and methodology
All analytical conclusions presented in this article are derived exclusively from historical backtest performance data spanning September 16, 2019, through September 1, 2026. The dataset encompasses 55 completed simulated trade cycles generated on 15-minute price data for ALNY equity. Percentage metrics express cumulative sum-of-trade returns across closed positions and do not account for compounding account leverage, variable slippage, order routing delays, or transaction fee structures. This quantitative analysis serves purely as historical strategy research and structural logic evaluation; it does not constitute financial advice, investment recommendations, or guarantees of future live trading results.