AMD · Advanced Micro Devices Inc.
Stock Universe · mercado global de açõesAMD 15-Minute Quantitative Strategy Evaluation: Analyzing High-Win-Rate Multi-Week Trend Dynamics and 7.5 Profit Factor
This comprehensive quantitative analysis evaluates the AMD 0 +34927.64% 1TRAD-ELF4 algorithmic trading strategy designed for Advanced Micro Devices (AMD) stock across a nearly seven-year backtested timeline from September 2019 to September 2026. Despite evaluating data captured at a 15-minute bar interval, the quantitative model exhibits a distinctly selective, multi-week holding profile, executing just 72 total trades across 6.98 years. Key historical achievements include an extraordinary 80.56% win rate, a robust profit factor of 7.5, and a DevioLab score of 75.92, ranking it first among tested strategies for this ticker. Gross profit distribution remains structural rather than accidental, with the three top-performing trades representing only 25.16% of total gains. However, historical performance reveals a significant drawdown phase of 34.02% during the 2022 market contraction, emphasizing that even high-payoff automated trading logic faces macro equity friction. This study provides an objective statistical decomposition of the strategy's trading rhythm, payoff symmetry, yearly behavior, and recent performance post-June 2024 for quantitative traders evaluating automated trading bots, trading robots, and algorithmic agents.
Análise em 16.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The strategy designated as AMD 0 +34927.64% 1TRAD-ELF4 represents a specialized stock-market algorithm engineered for Advanced Micro Devices (AMD). Operating on a 15-minute sampling interval, the strategy utilizes granular price data to identify specific structural entry and exit signals. Across a historical dataset spanning from September 16, 2019, to September 8, 2026—a period of 6.98 years—the quantitative model recorded 72 completed trades. With a DevioLab score of 75.92, it currently holds the rank of 1 for the AMD ticker within the DevioLab framework. The overall performance profile generated an aggregate historical return sum of +34,927.64%, reflecting an annualized rate of 2,034.59%. This quantitative system is designed to function as the decision logic for automated trading bots, trading robots, and systematic trading agents seeking systematic exposure to equity swings without requiring high-frequency execution.
Trading rhythm and position duration
Although the underlying data structure relies on a 15-minute bar frequency, the strategy's operational profile is marked by extreme patience rather than rapid scalping or intra-day churn. Over the 6.98-year sample, the algorithm executed an average of 10.31 trades per year, or approximately 1.44 trades per active month. The duration statistics reveal a multi-week positional hold: the average holding time across all completed positions stands at 644.99 hours (approximately 26.87 days), while the median holding duration is 347.88 hours (around 14.49 days). The disparity between average and median holding times indicates that while many trades resolve within two to three weeks, selected trend-following entries remain active for well over a month. Similarly, the temporal distance between trade exits averages 32.93 days, with a median of 20.98 days. The 15-minute timeframe therefore serves as a precise execution timing mechanism rather than a driver of transaction volume, allowing automated trading agents to filter intraday market noise before engaging in longer-horizon moves.
Quality of historical results
The statistical distribution of trade returns demonstrates exceptional historical payoff efficiency. Out of 72 completed trades, 58 closed in profit while 14 resulted in losses, establishing an 80.56% win rate. The overall profit factor reached 7.5, indicating that total gains exceeded total gross losses by seven and a half times. The average return per trade across the entire sample is +9.88%, comfortably exceeding the median trade return of +6.36%. The single best trade achieved a remarkable gain of +92.63%, while the worst trade incurred a loss of -20.80%. Crucially, the top three winning trades accounted for 25.16% of total gross profit. This concentration metric proves that the strategy's high performance is not a statistical artifact driven by one or two isolated windfalls, but rather the cumulative result of consistent positive expectancy across broad historical conditions.
Risk, drawdown and losing behavior
Despite an 80.56% success rate, the historical equity curve was subject to periodic downside volatility. The maximum drawdown recorded throughout the backtest window reached 34.02%. This peak-to-trough decline highlights the inherent risk of holding multi-week equity positions during broad sector corrections. In terms of trade sequencing, the strategy experienced a maximum winning streak of 20 consecutive profitable trades, contrasted with a longest losing streak of 4 trades. The presence of a -20.80% worst trade demonstrates that when market trends break down, individual position stop conditions can allow significant capital erosion before exiting. For quantitative risk managers utilizing trading robots, these metrics emphasize that even algorithms with a 7.5 profit factor require adequate equity capital cushions to endure multi-week drawdowns without experiencing structural compromise.
Behavior through time and yearly stability
Examining yearly performance metrics illuminates how the algorithm navigated diverse market regimes between 2020 and 2026. In 2020, the strategy completed 5 trades with an 80.00% win rate (4 wins, 1 loss) and a cumulative sum return of +174.57%. The following year, 2021, yielded perfection across 8 trades (100% win rate, 0 losses), adding +109.91%. However, 2022 marked a challenging macroeconomic regime, generating 8 trades with a 50.00% win rate (4 wins, 4 losses) and a net negative return sum of -21.67%. Recovery was swift in 2023, recording 11 trades, an 81.82% win rate, and a +83.20% gain. In 2024, performance remained solid with 14 trades, a 78.57% win rate, and +65.51%. The 2025 window produced 11 trades, a 72.73% win rate, and +124.98%. Finally, the 2026 period showed 15 trades with a 93.33% win rate and +174.71% in cumulative returns. Except for the 2022 equity market contraction, the model demonstrated multi-year stability.
Recent period since 2024-06-01 versus full history
Analyzing the recent dataset from June 1, 2024, to September 8, 2026, provides valuable insights into the strategy's current effectiveness. During this recent window, the strategy executed 34 completed trades—representing nearly 47% of all historical trades recorded in the 6.98-year backtest. These 34 recent trades generated a cumulative return sum of +355.85%. This substantial concentration of trades in the recent period suggests an acceleration in market structure setups that trigger the algorithm's entry conditions. Comparing the recent sum return of +355.85% against the total historical trade base confirms that the strategy maintained high profitability and consistent trade execution well into the final years of the test window, demonstrating that its signal logic did not degrade in recent market environments.
Strengths and limitations
The principal quantitative strengths of this strategy lie in its exceptional 7.5 profit factor, high 80.56% win rate, and well-distributed return profile where the top three winning trades comprise only 25.16% of gross gains. The low trade frequency (10.31 trades per year) minimizes transaction exposure relative to fast-scalping models. However, structural limitations are equally evident. The sample size of 72 completed trades over nearly seven years is relatively modest, which introduces sample-size sensitivity into long-term statistical inferences. Furthermore, holding trades for an average of 644.99 hours exposes active positions to overnight gaps, quarterly earnings reports, and macro news events. The 34.02% maximum drawdown and -20.80% worst trade illustrate that open-position risk remains substantial when trend reversals occur suddenly.
DevioLab analytical conclusion
With a DevioLab score of 75.92 and a rank of 1 for AMD, the strategy stands out as a high-expectancy quantitative model optimized for long-swing equity momentum. Its structural design successfully bridges intraday 15-minute technical sampling with multi-week trend capture, achieving a favorable payoff ratio and strong historical win metrics. While the 2022 drawdown of 34.02% serves as a reminder of systemic equity risks, the system's rebound across 2023–2026 demonstrates robust recovery dynamics. For developers and institutional quantitative analysts configuring automated trading bots or systematic trading agents, the strategy offers a compelling logical foundation, provided that operational risk management accounts for prolonged holding times and deep equity swings.
Data scope and methodology
This analysis is derived entirely from historical backtested trade logs for the asset symbol AMD across the timeframe September 16, 2019, through September 8, 2026. The technical setup utilizes 15-minute candlestick data to generate discrete trading decisions. All statistical calculations, including holding durations, win rates, drawdown percentages, and yearly trade sums, reflect closed simulated transactions. Slippage, brokerage commissions, margin costs, tax considerations, and execution latency are not included in the raw calculations. Historical backtested performance does not guarantee future operational profitability, and this research is published strictly for quantitative education and strategy modeling purposes.