AMCR · Amcor plc
Stock Universe · marché actions mondialQuantitative Strategy Analysis: AMCR 15-Minute Algorithmic Model Demonstrates High Win-Rate Stability and Broadly Distributed Payoffs Over Multi-Day Holding Horizons
This empirical evaluation analyzes the historical backtested performance of the quantitative trading strategy designed for AMCR in the equity market using a 15-minute price interval dataset spanning from January 8, 2020, to September 15, 2026. Over this 6.69-year evaluation window, the strategy generated 129 completed trades, achieving an overall cumulative trade return sum of 893.91% and an annualized return metric of 105.40%. The strategy exhibits a remarkably high win rate of 81.40% (105 winning trades against 24 losing trades) alongside a profit factor of 2.50. Notably, the strategy displays exceptional profit distribution efficiency, with its top three winning trades accounting for only 11.73% of total gross profits, indicating that cumulative returns are built upon broad-based consistency rather than isolated outlier gains. However, a key structural tension exists between the high win rate and tail-risk management: the strategy maximum drawdown of 28.17% coincides almost exactly with its single worst trade loss of 28.17%. With an average position holding duration of 233 hours (approximately 9.7 days) and a trade frequency of 19.29 trades per year, the quantitative evidence characterizes this model as a selective, multi-day swing-trading framework that leverages 15-minute resolution data for refined timing. Currently ranked second for the AMCR ticker on DevioLab with a score of 75.10, the strategy provides robust decision logic suitable for execution within automated trading bots and trading agents, provided that position-level downside exposure is actively managed.
Analyse au 15.09.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
Strategy profile
The algorithmic trading strategy under evaluation targets AMCR within the stock market, operating on a 15-minute price evaluation interval. Across a continuous dataset covering 6.69 years from January 8, 2020, to September 15, 2026, the strategy generated a total of 129 completed trade cycles. Within the DevioLab quantitative evaluation framework, this strategy achieves an overall score of 75.10, placing it at rank 2 among analyzed models for the AMCR asset. The structural architecture of the strategy is designed to process intraday price action at 15-minute granularity while deploying trade logic calibrated for multi-day position dynamics. In modern quantitative implementation, such decision models serve as the underlying logic for automated trading bots, trading robots, and algorithmic trading agents designed to execute rule-based systematic setups without subjective intervention. Over the full backtest horizon, the model accumulated a combined trade return sum of 893.91%, translating to an annualized return metric of 105.40%. This performance was realized through a highly asymmetric win-loss ratio, comprising 105 winning trades and 24 losing trades. The core analytical objective of this report is to deconstruct how these primary metrics interlock with trade duration, historical consistency, drawdown structure, and payoff concentration to determine the strategy true quantitative footprint.
Trading rhythm and position duration
Although the strategy samples market data on a 15-minute timeframe, its operational rhythm is fundamentally distinct from high-frequency intraday trading or scalping. The statistical evidence demonstrates a measured pace of execution, with an average trade frequency of 19.29 completed trades per year, which translates to approximately 1.77 trades per active month. Positions are held over extended horizons: the average holding duration per trade is 233 hours (approximately 9.7 days), while the median holding duration stands at 173.5 hours (approximately 7.2 days). This divergence between the 15-minute sampling frequency and multi-day position tenure indicates that the algorithm utilizes intraday data to identify granular entry and exit signals while capturing multi-day price swings. The spacing between trade exits further highlights the deliberate cadence of the system. The strategy records an average interval of 18.81 days between trade exits, with a median exit spacing of 16.04 days. This statistical profile reveals that the model does not continuously hold open market exposure, nor does it churn capital rapidly. Instead, it operates through periodic, highly filtered position entries followed by multi-day holding windows, allowing underlying price moves in AMCR to mature before triggering an exit signal. For automated trading robots and execution algorithms, this low transaction density reduces active market exposure while maintaining a persistent structural approach to mid-term price movement.
Quality of historical results
The qualitative profile of the strategy historical returns is defined by a exceptionally high win rate combined with consistent trade-level profitability. Across 129 completed trades, the strategy achieved a win rate of 81.40%. The average return per trade stands at 2.12%, while the median trade return is slightly higher at 2.50%. The proximity of the median trade to the average trade indicates a symmetrical trade distribution without heavy skewness caused by extreme positive anomalies. The best individual trade achieved a gain of 17.39%, whereas the overall profit factor settled at a robust 2.50, demonstrating that total gross gains substantially exceeded total gross losses by a two-and-a-half to one ratio. A critical highlight of the strategy payoff quality is found in its profit concentration metric. The top three winning trades combined account for just 11.73% of the total gross profit generated across the entire 6.69-year backtest. In quantitative finance, strategies with low concentration ratios are statistically superior because their total profitability does not depend on a few fortunate, black-swan tail events. Instead, the AMCR model demonstrates broad-based profitability, where the cumulative gain of 893.91% is distributed organically across dozens of individual winning trades averaging 2.12% to 2.50% in return. This confirms that the historical edge is embedded in repeatable market dynamics rather than outlier trades.
Risk, drawdown and losing behavior
While the strategy displays exceptional directional precision, an analytical breakdown of its risk metrics reveals a clear structural trade-off. The maximum historical drawdown experienced by the strategy is 28.17%. Crucially, this maximum drawdown figure matches almost exactly the magnitude of the single worst trade in the dataset, which recorded a loss of 28.1746%. This mathematical alignment indicates that the primary source of strategy drawdown was not a compounding sequence of multiple consecutive losses, but rather a single severe tail-risk loss event where price moved sharply against an open position. This vulnerability is further contextualized by analyzing the strategy streak characteristics. The algorithm exhibits extraordinary winning resilience, achieving a maximum consecutive winning streak of 12 trades. Conversely, its maximum consecutive losing streak is restricted to just 2 trades. Because losing streaks are exceptionally short, portfolio equity declines are rarely driven by persistent loss clusters. However, the presence of a single 28.17% loss event highlights that the strategy trade logic allows individual positions to absorb significant adverse price movement before realizing a stop exit. For quantitative traders deploying trading agents based on this logic, mitigating single-trade tail risk through refined stop-loss protocols represents the primary operational challenge.
Behavior through time and yearly stability
Evaluating the annual performance breakdown from 2020 through 2026 demonstrates remarkable multi-year consistency across varying equity market conditions. In 2020, the strategy recorded 17 trades with an 82.35% win rate and a cumulative return sum of 60.66%. In 2021, trade volume reached 15 trades with an 80.00% win rate and a cumulative return of 35.66%. The year 2022 marked the strategy highest annual precision, delivering a 89.47% win rate across 19 trades (17 wins and 2 losses) for a net return sum of 41.81%. In 2023, the algorithm executed 23 trades—its highest annual trade frequency—achieving a 78.26% win rate and a cumulative return of 36.22%. This structural stability persisted into the latter half of the sample. In 2024, the model executed 18 trades with a 77.78% win rate and a return sum of 30.50%. The year 2025 maintained this baseline with 22 trades, a 77.27% win rate, and a 37.47% return sum. Finally, during the partial year of 2026, the strategy generated 15 trades with an 86.67% win rate and a 30.98% return sum. Across all seven individual calendar years, annual win rates remained tightly bound between 77.27% and 89.47%, while annual return sums consistently generated between 30.50% and 60.66%. This uniformity confirms that the strategy historical performance is temporally robust rather than concentrated in a single favorable market phase.
Recent period since 2024-06-01 versus full history
A focused examination of the recent evidence window beginning June 1, 2024, provides valuable insight into the modern viability of the AMCR algorithmic strategy. Out of the 129 total trades generated over the 6.69-year backtest, 48 completed trades occurred within this recent window. These recent trades produced a cumulative trade return sum of 86.79%. This confirms that recent trade activity accounts for more than one-third of total lifetime trades and represents a significant portion of overall performance, demonstrating that the model has remained active and productive in recent market environments. Comparing the recent window to the full-history metrics shows strong alignment with historical baselines. The steady accumulation of 86.79% across 48 trades reflects an average trade return profile consistent with the full-history mean of 2.12%. Furthermore, the higher density of completed trades since mid-2024 confirms that the strategy entry conditions continue to trigger frequently without experiencing decay in execution opportunities. Rather than relying on outdated market regimes from 2020 or 2021, the algorithm maintains strong statistical continuity into current trading conditions.
Strengths and limitations
The primary quantitative strength of this trading strategy lies in its outstanding statistical win rate of 81.40% paired with a profit factor of 2.50. This win rate is backed by exceptional profit distribution, as evidenced by the top three winning trades contributing only 11.73% of total gross profits. This structural decentralization of gains ensures that historical performance is steady, reliable, and resistant to single-event dependencies. Furthermore, the strategy displays exemplary temporal stability, posting win rates above 77% and positive cumulative returns across every single calendar year from 2020 to 2026, while restricting consecutive losing streaks to a maximum of 2 trades. Conversely, the primary limitation of the strategy is its single-trade downside exposure. The strategy worst historical trade resulted in a loss of 28.17%, which directly defined its maximum drawdown of 28.17%. This indicates an unmanaged downside tail risk where single positions can suffer deep drawdowns before exiting. Additionally, with an average position holding duration of 233 hours (nearly 10 days) and only 19.29 trades per year, the strategy requires substantial patience and capital commitment. Operators seeking high-frequency turnover or tight intra-day stop losses may find the multi-day holding demands and deep worst-case loss profile challenging without secondary risk controls.
DevioLab analytical conclusion
In conclusion, the quantitative strategy for AMCR on the 15-minute timeframe represents a highly disciplined, multi-day swing framework that achieves high historical win rates and steady equity growth. DevioLab scores this model at 75.10, ranking it second among evaluated strategies for the AMCR asset. The strategy core efficacy stems from its high hit rate (81.40%), moderate trade frequency (19.29 trades per year), and exceptionally balanced profit structure, where returns are spread evenly across 105 winning trades rather than concentrated in lucky outliers. For practical application within automated trading bots, trading robots, or systematic execution agents, the strategy provides robust decision rules for timing medium-term swings in AMCR. However, systematic operators must address the asymmetric tail risk identified in the backtest data. Because the historical maximum drawdown of 28.17% was generated by a single worst trade of identical magnitude, real-world deployment would benefit from integrating strict account-level risk management or trailing stop parameters to cap individual trade losses without disrupting the underlying high-win-rate logic.
Data scope and methodology
The statistical findings presented in this report are derived strictly from simulated historical backtest data for the AMCR algorithmic trading strategy over the period starting January 8, 2020, and ending September 15, 2026. All metrics, including trade counts, win rates, holding durations, drawdowns, and annual performance breakdowns, are computed exclusively from closed trade events recorded on a 15-minute price data sampling structure. The recent observation window specifically isolates trades with completion timestamps on or after June 1, 2024. This research is intended solely for quantitative analysis and strategy evaluation purposes and does not constitute financial or investment advice. Simulated historical backtests do not incorporate real-time market frictions such as order book liquidity, bid-ask spreads, slippage, brokerage commissions, or exchange execution delays, all of which may impact live trading outcomes. Past performance recorded in historical simulations is no guarantee of future returns in live trading environments.