AMP · Ameriprise Financial Inc.
Stock Universe · marché actions mondialQuantitative Analysis of AMP Algorithmic Strategy: Multi-Year Performance, Structural Drawdown Control, and Low-Frequency Swing Dynamics
This quantitative evaluation inspects a top-ranked algorithmic trading strategy designed for AMP stock equity, evaluated across a seven-year historical backtest window from September 2019 to September 2026. Utilizing a 15-minute sAMPling timeframe to inform execution decisions, the strategy delivers a cumulative historical return of +5669.34% across 120 completed trades, achieving an overall win rate of 79.17% and an exceptional Profit Factor of 6.25. Rather than relying on rapid intra-day turnover or scalping mechanics, the decision logic operates as a medium-term swing framework, exhibiting an average position holding duration of 330.74 hours (approximately 13.78 days) and a maximum peak-to-trough drawdown limited to 14.43%. With the top three individual winning trades accounting for only 17.11% of gross profits, performance displays balanced distribution across market cycles. Holding the rank of #1 for AMP on DevioLab with a quantitative score of 85.77, the strategy provides robust decision logic suitable for automated trading bots and algorithmic trading agents navigating equity market volatility.
Analyse au 16.09.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
Strategy profile
The algorithmic trading strategy designated as AMP 0 +5669.34% 1TRAD-WFX3 represents a core quantitative model optimized for AMP stock equity. Evaluated across a full seven-year testing horizon spanning September 16, 2019, through September 16, 2026, the model stands as the rank #1 strategy for this asset on DevioLab, earning a DevioLab quantitative score of 85.77. Over the historical sAMPle, the strategy completed 120 closed trades, generating an all-history cumulative return of +5669.34% and an annualized performance metric of 168.71%. The statistical architecture relies on a 15-minute timeframe data feed for signal evaluation, serving as precise decision logic suitable for implementation within automated trading bots, trading robots, or automated trading agents seeking rules-based exposure. Out of the 120 completed trades, 95 resulted in positive returns while 25 closed in losses, establishing a baseline win rate of 79.17%. The overall statistical profile demonstrates a high-conviction, low-frequency paradigm where market participation is selective, minimizing exposure to noise while capturing structural trend movements.
Trading rhythm and position duration
Although the underlying data engine operates on a 15-minute bar interval, the trading rhythm of this algorithmic strategy is distinctly oriented toward multi-day position holding rather than high-frequency execution or scalping. The strategy registers an average holding duration of 330.74 hours (approximately 13.78 days) and a median holding duration of 210.88 hours (approximately 8.79 days). This disparity between mean and median holding times indicates that while most positions exit within roughly nine days, selective market conditions prompt the model to remain in trades for several weeks to fully capture extended moves. The pacing of market exits further highlights this low-frequency structure: the strategy records an average of 20.34 days between exits and a median of 16.18 days between exits. On an annualized basis, the model completes approximately 17.14 trades per year, which translates to a modest 1.69 trades per active month. Consequently, an automated trading agent running this logic would experience extended periods of market inactivity interspersed with targeted position entries, ensuring that capital is committed primarily when historical statistical parameters align.
Quality of historical results
The quality of the historical performance is characterized by high payoff efficiency and a well-distributed return profile across trades. The strategy achieved a Profit Factor of 6.25, indicating that total gross gains substantially outweighed cumulative gross losses over the 120-trade sAMPle. The average return per trade stands at +3.86%, while the median trade return is +2.80%. The positive alignment between the mean and median trade metrics confirms that profitable performance is a consistent characteristic across the sAMPle rather than an artifact skewed by extreme positive outliers. The single best trade achieved a gain of +32.00%, whereas the worst trade incurred a loss of -12.57%. Furthermore, an examination of profit concentration reveals that the top three largest winning trades account for exactly 17.11% of total gross profits. In quantitative strategy design, a low profit concentration figure demonstrates structural resilience, proving that the cumulative gain of +5669.34% was accumulated through a broad distribution of successful trade events rather than reliance on a handful of uncharacteristic market spikes.
Risk, drawdown and losing behavior
Risk management metrics within the historical dataset highlight controlled downside exposure relative to total performance output. The maximum peak-to-trough drawdown recorded over the seven-year period was 14.43%. When evaluated alongside the full history profit of +5669.34% and the Profit Factor of 6.25, the modest drawdown highlights favorable historical risk-adjusted return characteristics. The losing behavior of the strategy is characterized by brief and contained drawdown periods. The longest historical losing streak was limited to just 3 consecutive trades, whereas the longest winning streak reached 29 consecutive trades. With a total of 25 losing trades out of 120 completed positions, loss clusters remained small, preventing severe equity curve deterioration. The worst single trade loss of -12.57% represents a defined risk boundary within the backtest, indicating that losing trades were systematically contained before impacting systemic equity balance. This statistical stability provides a reassuring structural framework for deployment within quantitative risk budgets.
Behavior through time and yearly stability
An analysis of the yearly breakdown demonstrates consistent positive performance across varying equity market environments from 2020 through 2026. In 2020, the strategy generated its highest annual return of +202.86% across 20 trades with a 90.00% win rate (18 wins, 2 losses). The following year, 2021, maintained strong performance with 19 trades, an 84.21% win rate (16 wins, 3 losses), and a summed return of +82.74%. During the broader market headwinds of 2022, the strategy adjusted to record 17 trades, a 64.71% win rate (11 wins, 6 losses), and a positive net return of +39.65%. In 2023, trade frequency moderated to 13 trades, yielding 9 wins and 4 losses (69.23% win rate) for a cumulative gain of +18.32%. Performance accelerated in 2024 with 13 trades, a 76.92% win rate (10 wins, 3 losses), and +30.75% in total returns. In 2025, trade frequency expanded to 23 trades, producing 17 wins and 6 losses (73.91% win rate) and +32.96% return. Finally, the partial year of 2026 recorded 15 trades with 14 wins and 1 loss, reaching a 93.33% win rate and +55.36% return. Every calendar year within the backtest period recorded positive net returns and win rates above 64%, confirming multi-year consistency.
Recent period since 2024-06-01 versus full history
Evaluating the performance window from June 1, 2024, through the conclusion of the test sAMPle on September 16, 2026, provides insight into the strategy's recent operational stability. During this recent window, the model completed 45 trades, generating a cumulative return sum of +111.34%. Notably, these 45 recent trades represent 37.5% of the total 120 completed trades in the entire seven-year history. This increase in trade completions during the recent period indicates heightened market volatility or expanded opportunity recognition by the decision engine in recent cycles. Importantly, the recent cumulative return of +111.34% confirms that the algorithm maintained strong profitability without experiencing structural decay or strategy degradation. Comparing recent output against full-history norms demonstrates that the core edge remained intact, validating the continued relevance of the quantitative logic across contemporary market dynamics.
Strengths and limitations
The primary strength of this algorithmic trading strategy lies in its high win rate of 79.17% combined with an exceptional Profit Factor of 6.25 and a contained maximum drawdown of 14.43%. The broad distribution of gains—evidenced by the top three winning trades contributing only 17.11% of gross profits—ensures that performance is structurally sound. Furthermore, long winning streaks reaching up to 29 trades reflect high decision accuracy during favorable trends. Conversely, primary limitations center on low trade frequency and extended holding durations. Completing an average of only 17.14 trades per year with an average holding time of 330.74 hours requires substantial operational patience. Traders utilizing automated trading robots must tolerate extended periods without active signals or exit events. Additionally, multi-day exposure exposes open positions to overnight gap risk and broader equity market shocks that cannot be adjusted mid-holding when markets are closed.
DevioLab analytical conclusion
Holding the rank of #1 for AMP on DevioLab with a composite quantitative score of 85.77, this strategy demonstrates high-tier historical execution. The combination of a +5669.34% total return, an annualized performance metric of 168.71%, and a maximum drawdown restricted to 14.43% confirms effective risk-reward capture over a seven-year backtest horizon. The model's low trade frequency (1.69 trades per active month) and multi-day holding structure (median 210.88 hours) establish it as a patient, high-conviction swing strategy rather than a high-churn mechanism. For quantitative traders seeking reliable decision logic to power automated trading bots or algorithmic trading agents on AMP, the empirical evidence supports this core model as a robust framework for systematic equity market participation.
Data scope and methodology
This analysis is based strictly on historical backtested performance data generated for AMP stock equity on a 15-minute timeframe over the sAMPling period from September 16, 2019, to September 16, 2026. All reported metrics, including completed trade counts (120), win rates (79.17%), profit factors (6.25), drawdowns (14.43%), holding durations, and annual breakdowns are derived exclusively from closed trade records within the simulated test environment. History start dates represent the beginning of the strategy dataset and do not reflect asset listing dates or corporate events. These results represent historical simulated performance for strategy evaluation purposes and do not constitute investment advice, financial recommendations, or guarantees of future live execution profitability.