AMD · Advanced Micro Devices Inc.
Stock Universe · globaler AktienmarktQuantitative Analysis of AMD 1TRAD-RTV3: Multi-Week Holding Horizons and Asymmetric Payoffs in Equities Strategy
This empirical evaluation dissects the performance characteristics of the algorithmic trading strategy AMD 0 +43922.55% 1TRAD-RTV3 across a simulated testing window from September 2019 through September 2026. Designed for Advanced Micro Devices (AMD) stock on a 15-minute timeframe, the strategy demonstrates a high historical win rate of 75.63% alongside a profit factor of 3.75 across 119 completed trades. Despite executing entry and exit signals derived from granular 15-minute price data, the algorithm maintains a multi-week position duration, averaging 364.74 hours per trade. The strategy achieved a cumulative return of +43,922.55%, supported by a well-distributed gross profit structure where the top three winning trades contribute only 16.78% of gross gains. However, historical risk metrics reveal a maximum peak-to-trough drawdown of 38.22% and a maximum losing streak of 7 consecutive trades, underscoring the structural equity fluctuations inherent to trend-following logic during adverse market regimes such as calendar year 2022.
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1. Strategy profile
The algorithmic trading strategy designated as AMD 0 +43922.55% 1TRAD-RTV3 represents an automated decision framework specifically tailored for the stock market asset AMD (Advanced Micro Devices). Operating on a 15-minute chart interval, the strategy evaluates high-frequency price data to identify positional entry and exit thresholds. Over a dataset spanning nearly seven years from September 16, 2019, to September 8, 2026, the strategy recorded 119 completed trades. Within DevioLab's comparative scoring system, this strategy achieves a DevioLab score of 66.82, ranking 3rd among evaluated strategies for the AMD symbol. The overall historical backtest metrics reflect a cumulative net return of +43,922.55% with a profit factor of 3.75. This performance is grounded in 90 winning trades against 29 losing trades, yielding a win rate of 75.63%. The strategy's design is engineered for deployment within automated trading bots, trading robots, or quantitative trading agents where programmatic execution eliminates emotional discretion. Rather than targeting micro-fluctuations, the quantitative logic leverages granular 15-minute candlestick data to construct macro-directional exposure, establishing a distinctive balance between tactical timing and extended position holding.
2. Trading rhythm and position duration
A critical structural feature of this strategy is the divergence between its execution interval and its temporal holding horizon. Although configured on a 15-minute chart, the strategy exhibits an average holding duration of 364.74 hours (approximately 15.2 days) and a median holding duration of 216.00 hours (9.0 days). This duration profile classifies the model as a multi-week swing or position-trading strategy rather than a high-frequency or day-trading algorithm. The low trade frequency—averaging 17.05 trades per year and 1.86 trades per active month—further confirms that position entries are highly selective. Position exits occur infrequently, with an average interval between trade exits of 20.17 days and a median of 15.00 days. This spacing demonstrates that the strategy routinely remains inactive or holds existing exposure across multiple weeks without generating churn. By maintaining open positions across several daily sessions, the strategy captures extended trends in AMD stock. Simultaneously, this multi-day exposure subjects the portfolio to overnight gaps, earnings announcements, and broader market sentiment shifts between 15-minute evaluation periods.
3. Quality of historical results
Analyzing the payoff structure reveals favorable trade expectancy supported by strong hit-rate statistics. The strategy achieved an average trade profit of +6.05% against a median trade profit of +5.12%. The close alignment between the mean and median trade returns indicates a relatively symmetrical distribution of positive trade outcomes, suggesting that historical returns were not driven by an isolated cluster of extreme statistical anomalies. The historical spectrum of trade outcomes ranges from a maximum gain of +76.68% on its best trade to a maximum loss of -19.59% on its worst trade. A vital indicator of structural robustness is the concentration of profit: the top three winning trades account for 16.78% of the strategy's total gross profit. Because less than a fifth of aggregate gains depend on the three largest winners, the strategy's outperformance displays broad-based participation across its 90 winning trades. Combined with a profit factor of 3.75, the statistical profile indicates that gross realized profits exceeded gross realized losses by nearly four-to-one over the full evaluation window.
4. Risk, drawdown and losing behavior
While the strategy exhibits robust profitability metrics, its historical equity curve demonstrates substantial drawdown risks that warrant careful risk management. The maximum observed drawdown reached 38.22% over the historical history. This capital contraction reflects the vulnerability of multi-week trend logic during prolonged market corrections or choppy consolidation phases where trailing stops or exit rules trigger late relative to price peaks. The strategy recorded a maximum losing streak of 7 consecutive trades, contrasted against a maximum winning streak of 19 consecutive trades. The occurrence of a 7-trade losing sequence in a system with a 75.63% overall win rate highlights the presence of regime dependency; losing trades tend to cluster tightly during unfavorable equity market environments. Furthermore, with the worst individual trade incurring a -19.59% loss, automated trading agents deploying this logic must withstand pronounced adverse trade excursions before position exit signals are triggered.
5. Behavior through time and yearly stability
The annual breakdown provides detailed insight into how the strategy navigated distinct macroeconomic cycles and volatility regimes in AMD stock from 2020 through 2026. Performance was overwhelmingly positive across six of the seven recorded calendar years, with 2022 standing as the sole unprofitable period. In 2020, the strategy recorded 9 trades with an 88.89% win rate (8 wins, 1 loss) producing a aggregate gain of +171.31%. In 2021, trade frequency rose to 16 trades with a 68.75% win rate, yielding +98.45%. During the equity market downturn of 2022, performance contracted: across 14 trades, the win rate dropped to its historical low of 57.14% (8 wins, 6 losses), resulting in a net calendar loss of -5.64%. Profitability recovered sharply in 2023, generating +97.62% across 20 trades with an 80.00% win rate. In 2024, 19 trades yielded +65.71% at a 78.95% win rate. The year 2025 logged 22 trades—the highest annual trade count—yielding +115.94% despite a lower win rate of 59.09% (13 wins, 9 losses), demonstrating strong payoff asymmetry in winning trades. In 2026, the strategy completed 19 trades with a perfect 100.00% win rate, delivering +176.58%.
6. Recent period since 2024-06-01 versus full history
Evaluating recent execution performance from June 1, 2024, through the end of the history dataset provides a meaningful window into contemporary strategy behavior. During this recent sub-period, the strategy completed 50 trades, representing 42.02% of all historical trades recorded since September 2019. The cumulative sum of trade returns during this window reached +331.69%. Comparing this recent window against the broader 6.98-year history indicates an acceleration in trade exits and consistent profit generation in modern market conditions. Because 50 out of 119 total trades closed after June 1, 2024, recent market volatility in AMD stock generated frequent signal triggers relative to the strategy's historical baseline. The strong total return of +331.69% over these 50 recent trades confirms that the strategy's structural edge remained functional and active throughout the recent market environment.
7. Strengths and limitations
The primary analytical strength of the AMD 1TRAD-RTV3 strategy lies in its asymmetric payoff profile and high baseline win rate (75.63%). With a profit factor of 3.75 and a low profit concentration (top 3 winners contributing 16.78%), the historical equity growth is supported by repeatable multi-trade capture rather than fortunate reliance on single outlier moves. Furthermore, an average annual trade count of 17.05 limits portfolio exposure to execution friction. Conversely, significant structural limitations exist. The average position holding duration of 364.74 hours exposes capital to gap risk, corporate earnings surprises, and overnight market shifts. Additionally, the strategy's historical maximum drawdown of 38.22% and worst trade loss of -19.59% demand substantial institutional risk tolerance. The concentration of losses during trendless or declining equity regimes—as evidenced by the 7-trade losing streak and negative return in 2022—indicates that trading robots executing this code must be prepared for extended periods of drawdown during secular equity corrections.
8. DevioLab analytical conclusion
With a DevioLab score of 66.82 and a ranking of 3rd for the AMD symbol, the AMD 0 +43922.55% 1TRAD-RTV3 strategy represents a disciplined, trend-capturing algorithm designed for equity market participation. The model successfully converts granular 15-minute price observations into intermediate-term holdings, achieving a historical total return of +43,922.55% over approximately seven years of simulated data. For quantitative practitioners evaluating this strategy for automated trading bot deployment, the key operational consideration is managing structural equity drawdowns. While the strategy exhibits robust multi-year consistency, its multi-week holding horizon and 38.22% maximum drawdown necessitate strict capital allocation policies. Portfolio managers and automated agents utilizing this model must align position sizing with the strategy's historical volatility profile to survive severe drawdown sequences like that observed in 2022 while capturing its long-term trend upside.
9. Data scope and methodology
The empirical findings detailed in this article are derived exclusively from historical simulated backtest statistics for the strategy AMD 0 +43922.55% 1TRAD-RTV3 across the sample period starting September 16, 2019, and ending September 8, 2026. The dataset encompasses 119 completed, closed trades generated on a 15-minute price series for AMD stock. All metrics, including cumulative percentage returns, profit factor, annual breakdown, holding times, and drawdown figures, reflect hypothetical backtested execution. These statistics do not represent live account trading yield, real-money execution, or guaranteed future performance. Slippage, exchange fee structures, execution latency, and unexpected corporate actions are not modeled in the underlying statistical output, and historical results serve solely for comparative quantitative analysis.