BALL · Ball Corporation
Stock Universe · mercado bursátil globalBALL · BALL Algorithmic Strategy Analysis: High Win-Rate, Low-Frequency Model Driven by Macro Outlier Gains
An exhaustive statistical evaluation of the 15-minute algorithmic strategy for BALL reveals a highly unusual performance profile characterized by an explosive cumulative historical return of 8503.32% across a 4.15-year backtest window from June 2022 to July 2026. This performance was generated through extreme selectiveness, completing only 15 trades in total with an exceptional win rate of 93.33% and a maximum drawdown restricted to 7.63%. However, deeper quantitative scrutiny demonstrates a profound positive skew in return distribution: a single parabolic trade in 2023 yielding 1808.00%, alongside the top three winning positions, accounts for 95.05% of total gross profit. Consequently, while the average trade return stands at 132.15%, the median trade return is a far more modest 6.36%. Designed as decision logic for automated trading bots, trading robots, or trading agents operating on 15-minute bar intervals, the strategy trades infrequently—averaging 3.61 trades per year—and maintains long position durations averaging nearly 49 days. Recent historical evidence since June 2024 reinforces steady baseline accumulation, recording 10 completed trades with a aggregate return of 98.86% without relying on secondary extreme outliers.
Análisis al 23.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The algorithmic strategy designated for BALL operates on a 15-minute price series within the equity market. Over a total historical evaluation window of 4.15 years spanning from June 1, 2022, through July 27, 2026, the quantitative logic generated 15 completed trade cycles. Within the DevioLab evaluation framework, this strategy achieves a score of 72.5 and holds the rank of 3 for the BALL ticker. Over the entire backtest dataset, the strategy accumulated a total historical return sum of 8503.32%, translating to a annualized performance figure of 139.36%. The core design philosophy represents a ultra-selective macro-trend harvesting architecture intended for implementation in automated trading bots, trading robots, or autonomous trading agents. Rather than seeking high transaction velocity or micro-alpha, the underlying algorithm utilizes the 15-minute intraday bar resolution to fine-tune entry and exit execution while maintaining macro position holding horizons. Across the 15 total historical trades, 14 resulted in positive outcomes while only 1 resulted in a loss, establishing an exceptionally high hit rate of 93.33%. This structural baseline provides automated execution systems with a low turnover operational template, minimizing exposure to choppy range-bound intraday noise.
Trading rhythm and position duration
The temporal characteristics of this strategy present an interesting structural dynamic between bar evaluation frequency and trade execution cadence. Although the decision logic evaluates pricing metrics on a 15-minute candlestick chart, the actual trading cadence is remarkably low. The model executed an average of 3.61 trades per year, which equates to roughly 1 trade per active trading month across the 4.15-year historical span. This structural disconnect between intraday data sampling and low transaction frequency indicates that the strategy relies on strict higher-level filters to bypass routine market oscillations. Once a position is opened, holding times extend significantly beyond typical intraday or swing paradigms. The average position duration is 1178.58 hours (approximately 49.1 days), while the median holding duration stands at 891.75 hours (approximately 37.2 days). Furthermore, the temporal spacing between trade exits reflects long inactive intervals, exhibiting an average of 106.70 days and a median of 85.41 days between exit events. For automated trading agents and execution bots, this operational rhythm implies extended periods of zero positioning interspersed with multi-week trend-following holdings, requiring patience and stable margin parameters from automated infrastructure.
Quality of historical results
A quantitative examination of trade quality reveals both remarkable efficiency and heavy statistical concentration. The strategy achieved a high profit factor of 7.5, supported by 14 winning trades against a single losing trade. The headline average trade return of 132.15% appears extraordinarily lucrative on initial inspection; however, comparing this figure against the median trade return of 6.36% unveils extreme positive skewness within the sample distribution. The strategy's performance was overwhelmingly shaped by its best historical trade, which yielded a single gain of 1808.00%. The mathematical concentration of gross profit highlights this dependency: the top three winning trades generated 95.05% of total gross historical gains. This means that while 11 of the remaining 14 winning trades yielded consistent, positive returns around the 6.36% median mark, the strategy's massive compounding advantage was primarily driven by capturing a singular, massive trending wave. The profit factor of 7.5 reflects this structural asymmetry, demonstrating that when the algorithm successfully aligns with a major multi-month trend, it captures substantial upside while maintaining tight distribution bounds on routine winning positions.
Risk, drawdown and losing behavior
Risk metrics across the 4.15-year dataset display exceptional downside control, closely tied to the strategy's high win rate and strict loss truncation. The maximum historical drawdown experienced by the portfolio was 7.63%, a figure that directly mirrors the worst individual trade loss of -7.63%. Because the strategy recorded 14 winning trades and only 1 losing trade, the longest winning streak extended to 7 consecutive trades, whereas the longest losing streak was limited to a single trade. In historical execution, peak-to-trough equity decline was not caused by a series of compounding losses or prolonged drawdowns, but rather by the single unaligned position recorded in the dataset. Because losses were confined to a single -7.63% event, the historical strategy equity curve remained remarkably smooth outside of its step-function upward jumps. For quantitative risk managers deploying automated trading robots, this risk profile demonstrates that historical drawdowns were tightly bounded by single-trade stop mechanics rather than structural equity erosion across multiple trades.
Behavior through time and yearly stability
Analyzing the yearly breakdown statistics reveals how historical performance evolved across different market regimes from 2022 into 2026. In 2022, the strategy executed 3 trades, all of which were profitable (100% win rate), producing a combined return sum of 24.18%. The pivotal inflection point occurred in 2023, during which the strategy completed just 1 trade; that single trade produced the best historical output of 1808.00%, single-handedly providing the vast majority of total historical performance. In 2024, activity normalized back to 3 completed trades, achieving a 100% win rate and a cumulative return sum of 61.03%. The year 2025 represented the most active calendar period with 5 completed trades, yielding 4 wins and 1 loss (80% win rate) with a combined sum of 36.62%. Finally, through the partial data period in 2026, the strategy closed 3 trades, all positive (100% win rate), adding 52.49% in cumulative returns. The yearly breakdown illustrates that while baseline profitability was maintained consistently across every single calendar year, the overall return magnitude was dominated by the extraordinary regime capture in 2023.
Recent period since 2024-06-01 versus full history
Evaluating performance in the recent period beginning June 1, 2024, provides vital context regarding the strategy's ongoing operational behavior. Out of the 15 total historical trades completed since mid-2022, 10 trades were closed on or after June 1, 2024. This recent evidence window encompasses two-thirds of all historical transactions, indicating a subtle increase in trade completion frequency in the latter half of the sample period. During this recent window, the strategy generated a cumulative return sum of 98.86%. Comparing this recent performance to full-history metrics demonstrates that even without another massive 1808.00% outlier event, the strategy maintained highly productive baseline performance. The average gain per trade across these recent 10 positions was approximately 9.89%, aligning closely with the strategy's broader baseline behavior outside the 2023 macro winner. For automated trading agents evaluating contemporary applicability, the post-June 2024 data shows strong structural consistency, proving that the underlying logic continued to generate positive net expectation across multiple trades in recent market conditions.
Strengths and limitations
The primary structural strength of this algorithmic strategy lies in its outstanding historical accuracy and strict risk containment. A win rate of 93.33%, combined with a profit factor of 7.5 and a maximum drawdown capped at 7.63%, represents a robust analytical framework. Furthermore, the low trading frequency (3.61 trades per year) insulates the strategy from over-trading risks and unnecessary market exposure. Conversely, the principal statistical limitation stems from the small total sample size of 15 completed trades over 4.15 years. From a statistical perspective, a 15-trade sample carries inherent wide confidence intervals regarding true population expectations. Additionally, the extreme concentration of profit—where the top three winning positions account for 95.05% of gross profit and the median trade (+6.36%) differs drastically from the average trade (+132.15%)—means that long-term historical performance was heavily reliant on catching rare, multi-hundred-percent trend movements. If future market regimes fail to produce extended directional trends, strategy performance may reflect the baseline median returns rather than the historical average return.
DevioLab analytical conclusion
With a DevioLab score of 72.5 and a rank of 3 for BALL, this algorithmic trading strategy demonstrates a compelling quantitative profile tailored for long-horizon position holding using intraday execution triggers. The strategy successfully combines a high hit rate with exceptionally controlled historical drawdown. However, quantitative analysts and operators of automated trading bots or trading agents must interpret the headline return of 8503.32% with proper statistical calibration. The strategy functions effectively as a dual-engine model: it generates modest, high-probability baseline gains (median return of 6.36%) while maintaining exposure to capture massive, rare macro trends (such as the 1808.00% trade in 2023). Deploying this strategy in automated environments requires an operational framework capable of maintaining long holding durations (averaging ~49 days per trade) and surviving multi-month periods of inactivity between signals without altering parameters.
Data scope and methodology
This statistical analysis is derived strictly from historical simulated backtest data for BALL on a 15-minute bar interval covering the period from June 1, 2022, through July 27, 2026 (4.15 total years). All metrics, including trade returns, holding durations, drawdown figures, and yearly breakdowns, represent closed trade outcomes calculated upon signal execution. The recent sample window beginning June 1, 2024, captures all completed trades where the exit timestamp occurred on or after that date. Statistics do not account for unstated live execution costs, potential slippage, floating overnight financing costs, or corporate action adjustments unless explicitly recorded. These findings reflect historical simulated research intended to analyze the structural properties of quantitative algorithms and do not constitute financial advice or a guarantee of future live performance.