AMGN · Amgen Inc.
Stock Universe · mercado global de açõesAMGN · AMGN Algorithmic Strategy Analysis: High Hit-Rate Swing Dynamics Across a 7-Year History
This quantitative evaluation analyzes a top-ranked algorithmic trading strategy designed for AMGN on a 15-minute execution timeframe. Over a simulated seven-year dataset spanning 2019 through 2026, the strategy generated a cumulative closed-trade gain of +1225.22% across 96 completed positions. Characterized by an exceptionally high historical win rate of 76.04%, a controlled maximum drawdown of 16.62%, and an average position holding duration of 392.67 hours, the algorithm demonstrates a disciplined, low-frequency swing profile. With a DevioLab Score of 79.85—ranking first among evaluated models for this ticker—the strategy presents a compelling case study in structural hit-rate consistency and decentralized return distribution across multiple market regimes.
Análise em 16.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The quantitative model designated as AMGN 0 +1225.22% 1TRAD-IQJ7 operates on a 15-minute price series for AMGN, serving as a specialized decision engine engineered for systematic execution. Despite utilizing a granular 15-minute intraday bar for signal evaluation, the algorithm does not engage in rapid intraday scalping; instead, it uses the fine resolution to pin down entry and exit timing for intermediate multi-day swing positions. Across a seven-year evaluation horizon extending from September 16, 2019, to September 16, 2026, the strategy completed 96 trades, achieving an overall win rate of 76.04% based on 73 winning transactions against 23 losing trades. The system achieved a cumulative return sum of +1225.22% across all closed positions, corresponding to an annualized metric of 210.81%. Holding core status within the DevioLab database, the model earned a DevioLab Score of 79.85, securing the rank of number one among strategies evaluated for AMGN. This performance highlights an algorithmic framework optimized for capturing sustained directional trends while maintaining selective exposure to market fluctuations.
Trading rhythm and position duration
The historical trade log reveals a low-frequency, patient trading rhythm structured around medium-term holding periods. On average, the strategy completes 13.71 trades per year, which translates to approximately 1.43 closed trades per active month. The temporal spacing between position exits averages 26.72 days, with a median interval of 21.01 days, confirming that trade completions occur roughly once or twice a month rather than in rapid sequence. Once deployed, positions remain active for substantial durations. The average holding duration stands at 392.67 hours (approximately 16.36 calendar days), whereas the median holding duration is 251.00 hours (roughly 10.46 calendar days). The right-skewed gap between average and median holding times indicates that while most trades resolve within one to two weeks, select positions are maintained across several weeks to capture extended trend expansions. For deployment within automated trading bots, trading robots, or quantitative trading agents, this spatial rhythm minimizes execution turnover, operating well within liquidity boundaries while insulating the portfolio from short-term market noise.
Quality of historical results
Evaluating trade performance metrics reveals a return profile built on high accuracy and remarkably balanced profit distribution. The strategy recorded an average trade return of +3.08%, which aligns closely with its median trade return of +3.37%. The close proximity of the median to the average demonstrates that historical profitability is not skewed by isolated extreme windfalls, but is instead driven by consistent, repeatable wins. This structural balance is further reinforced by the concentration metric: the three largest winning trades accounted for just 12.20% of total gross profit. In many trend-following algorithms, top winners frequently absorb 30% to 50% of total gains, creating fragility; here, profitability is broadly distributed across the 73 winning trades. The strategy achieved a best single trade return of +16.51% against a worst single trade loss of -16.62%. Despite an outstanding win rate of 76.04%, the overall profit factor stands at 1.25. This profit factor reflects a moderate average payoff ratio, meaning individual losing trades are roughly equal in magnitude to individual winning trades, making high trade accuracy the central engine of the strategy's net performance.
Risk, drawdown and losing behavior
Risk management metrics across the seven-year backtest highlight controlled equity declines and tightly constrained streak vulnerabilities. The strategy experienced a maximum drawdown of 16.62%, matching the magnitude of its worst individual trade loss (-16.62%). This alignment suggests that peak drawdown events were primarily driven by single adverse position resolutions rather than compounding multi-trade compounding cascades. The maximum consecutive losing streak was restricted to 3 trades, whereas the longest winning streak reached 7 consecutive trades. Over 96 historical completed trades, encountering only 23 total losses with a maximum sequence of 3 consecutive drawdowns indicates that equity recovery cycles were historically prompt. The high win rate of 76.04% creates a statistical cushion against prolonged equity stagnation, provided the risk-per-trade profile remains disciplined. For automated trading agents managing capital, a maximum historical drawdown of 16.62% against a cumulative trade return sum exceeding 1200% represents an advantageous ratio of historical gain relative to equity pain.
Behavior through time and yearly stability
An examination of the yearly performance breakdown shows steady positive returns across every single calendar year in the test period, demonstrating robustness across differing market regimes. In 2019, during the strategy's initial partial year, it logged 3 trades with a 100% win rate, yielding +14.54%. Activity expanded in 2020 to 17 trades (14 wins, 3 losses) generating a sum of +51.94% at an 82.35% win rate. Subsequent years maintained consistent profitability: 2021 recorded 9 trades (7 wins, 2 losses) for +29.24%; 2022 generated 11 trades (10 wins, 1 loss) with a 90.91% win rate yielding +38.74%; 2023 produced 14 trades (9 wins, 5 losses) summing to +16.11% at a 64.29% win rate; and 2024 recorded 15 trades (9 wins, 6 losses) returning +28.61% with a 60.00% win rate. Momentum accelerated significantly in 2025, delivering 14 trades (11 wins, 3 losses) for a annual return sum of +71.87% (78.57% win rate), while 2026 recorded 13 trades (10 wins, 3 losses) totaling +44.22% (76.92% win rate). The persistence of positive annual cumulative returns across eight calendar years confirms that the algorithm's edge is not anchored to a single speculative market phase.
Recent period since 2024-06-01 versus full history
Isolating the recent performance window from June 1, 2024, through the end of the historical sample provides critical insight into contemporary strategy dynamics. During this sub-period, the algorithm closed 36 trades, compiling a cumulative return sum of +130.25%. This represents a notable increase in trade frequency relative to the earlier backtest years: the 36 completed trades in this ~27-month window account for 37.5% of all historical trades executed over the entire seven-year dataset. Despite the heightened trade cadence, performance quality remained intact, with the post-June 2024 sample contributing over 130 percentage points of net trade return. Comparing recent trade behavior with full-history norms confirms that the strategy's market interaction logic has maintained its efficacy in recent market environments, offering active statistical evidence rather than relying solely on legacy performance from 2019–2022.
Strengths and limitations
The primary strength of this algorithmic strategy lies in its exceptionally consistent hit rate (76.04%) combined with broad profit dispersion, as evidenced by the top three winning trades contributing only 12.20% of gross profits. Perfect annual profitability across eight consecutive calendar periods confirms structural adaptiveness. Furthermore, a long average holding duration of 392.67 hours coupled with a low trade count (13.71 trades per year) renders the strategy highly resistant to transaction overhead and execution drag when deployed via automated trading bots or trading agents. Conversely, the model's primary limitation stems from its profit factor of 1.25. Because the worst historical trade (-16.62%) is virtually identical in magnitude to the best historical trade (+16.51%), the strategy relies fundamentally on sustaining its high win rate to stay profitable. A cluster of unexpected consecutive losses could impact equity performance faster than in high-payoff, low-win-rate trend models.
DevioLab analytical conclusion
Achieving a DevioLab Score of 79.85 and ranking first for AMGN, this strategy represents a refined swing-trading model built on selective entry timing and rigorous win-rate discipline. Its combination of a 76.04% win rate, a 16.62% maximum drawdown, and uniform annual profitability demonstrates robust structural alignment with AMGN's historical volatility profile. The statistical proof of non-concentrated returns—where no single trade dominates the equity curve—provides high confidence in the systemic stability of the underlying logic. For quantitative developers and traders evaluating automated execution architectures, the strategy provides a strong candidate for algorithmic trading robot implementation, provided the underlying system accounts for multi-week position holding times and maintains realistic risk controls aligned with historical drawdown boundaries.
Data scope and methodology
The analytical insights presented in this report are derived strictly from backtested historical trade logs for AMGN on a 15-minute chart resolution across the period from September 16, 2019, to September 16, 2026. All reported metrics, including cumulative return sums (+1225.22%), win rates (76.04%), holding durations, and drawdown figures, reflect closed-trade mathematical simulations. The dataset incorporates 96 completed trades, with recent-period calculations established from positions closed on or after June 1, 2024 UTC. These results represent historical quantitative research and backtested strategy performance; they do not constitute live brokerage execution records, financial advice, or future return guarantees.