APTV · Aptiv PLC
Stock Universe · marché actions mondialAPTV Quantitative Swing Model Analysis: Evaluating the +1044.24% Historical Return and 3.75 Profit Factor Engine
This quantitative research report evaluates an algorithmic trading strategy for APTV operating on a 15-minute chart resolution across a 6.97-year historical testing dataset from September 2019 through September 2026. Generating a cumulative closed profit of +1044.24% across 102 completed trades, the strategy achieves a Profit Factor of 3.75 and a win rate of 73.53%. Structurally designed as a low-frequency swing framework with an average holding time of 179.73 hours (approximately 7.5 days), the strategy demonstrates high return distribution across its trades, with the top three winning positions generating only 13.59% of gross profits. While the peak historical drawdown reached 26.05%, this was primarily dictated by a single worst-case exit of -26.05% rather than prolonged losing sequences. Currently ranked first for APTV on DevioLab with a score of 73.07, the algorithm offers a robust quantitative profile suited for implementation within automated trading bots and algorithmic execution agents.
Analyse au 18.09.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
Strategy profile
The strategy evaluated in this report is a specialized quantitative trading algorithm designed for the stock asset APTV. Operating on a 15-minute timeframe (15m), the strategy utilizes sub-hourly price data to identify multi-day directional swing opportunities. Across a simulated testing period of approximately 6.97 years—spanning from September 16, 2019, to September 4, 2026—the algorithm completed 102 closed trade cycles, achieving a cumulative historical performance of +1044.24% with an annualized equivalent metric of 121.68%. With a DevioLab Score of 73.07, this strategy holds the rank 1 position for APTV among evaluated strategies in its class. Out of 102 completed trades, 75 closed in profit while 27 incurred losses, yielding a structural win rate of 73.53%. The resulting Profit Factor stands at 3.75, meaning gross historical profits exceeded gross historical losses by a factor of nearly three and three-quarters. Designed with precise execution parameters, the strategy serves as logic suitable for implementation in automated trading bots, trading robots, and quantitative trading agents seeking systematic equity exposure.
Trading rhythm and position duration
Despite operating on a 15-minute price aggregation interval, the strategy is firmly characterized as a medium-term swing model rather than an intraday scalping framework. The average holding duration for a trade position is 179.73 hours (approximately 7.49 calendar days), while the median holding duration sits at 144.75 hours (approximately 6.03 calendar days). This duration indicates that while entry and exit signals are refined using 15-minute candle closes, trades routinely persist across multiple daily sessions to capture broader cyclical price swings. The algorithm exhibits a disciplined, selective trading frequency. Historically, it executed an average of 14.64 trades per year, which translates to roughly 1.48 completed trades per active month. The temporal spacing between position closes reinforces this restrained pace: the average time between trade exits is 24.94 days, with a median exit interval of 20.00 days. For automated trading agents and trading robots, this low-turnover cadence reduces exposure to frequent market noise, focusing execution capital on higher-conviction setup windows.
Quality of historical results
An examination of payoff distribution reveals a structurally sound performance profile. The average trade gain across all 102 closed trades is +2.85%, while the median trade gain is slightly higher at +3.15%. In quantitative analysis, a median return that meets or exceeds the average return is a favorable indicator; it demonstrates that overall strategy performance is sustained by consistent, repeatable core trade outcomes rather than being artificially inflated by rare, extreme positive outliers. This broad distribution of profits is further confirmed by profit concentration metrics. The three largest winning trades historically accounted for just 13.59% of total gross profits. While the best single trade achieved a gain of +21.82%, the strategy did not rely on this outlier to generate its overall +1044.24% return. With 75 winning outcomes distributed across 102 trades and a Profit Factor of 3.75, the strategy's statistical edge is embedded in a reliable hit rate paired with strong payoff consistency across diverse market phases.
Risk, drawdown and losing behavior
Risk metrics highlight a distinct structural relationship between position-level loss severity and portfolio-level drawdown. The maximum historical drawdown recorded for the strategy is 26.05%. Notably, the worst single trade loss in the dataset is -26.05%, matching the maximum drawdown percentage almost exactly. This structural alignment suggests that peak equity contraction was driven by a single severe trade exit or gap event, rather than an extended accumulation of consecutive losing positions. The strategy's drawdown dynamics are mitigated by strong streak controls. The longest consecutive losing streak observed over the nearly 7-year dataset was just 3 trades, whereas the longest winning streak reached 7 consecutive trades. Because losing trades rarely cluster together, portfolio recovery after drawdown events has historically been relatively prompt. However, the presence of a -26.05% worst trade underscores that automated trading bots executing this logic must account for single-position tail risk in stock equities, particularly across overnight multi-day holds.
Behavior through time and yearly stability
Evaluating the strategy across individual calendar years demonstrates consistent profitability across varying market environments. Out of eight calendar years analyzed (including partial years 2019 and 2026), seven yielded positive cumulative trade sums: - 2019: 3 trades, 100.00% win rate, +16.73% cumulative sum - 2020: 13 trades, 84.62% win rate, +51.98% cumulative sum - 2021: 17 trades, 70.59% win rate, +37.75% cumulative sum - 2022: 21 trades, 76.19% win rate, +68.01% cumulative sum - 2023: 13 trades, 69.23% win rate, +30.68% cumulative sum - 2024: 10 trades, 60.00% win rate, -4.36% cumulative sum - 2025: 12 trades, 75.00% win rate, +55.96% cumulative sum - 2026: 13 trades, 69.23% win rate, +33.99% cumulative sum The most active period occurred in 2022, where 21 completed trades produced a +68.01% return with a 76.19% win rate. The sole negative calendar year was 2024, which recorded a modest loss of -4.36% across 10 trades. The strategy returned to strong positive performance in 2025 (+55.96%) and 2026 (+33.99%), illustrating long-term resilience across multiple market regimes.
Recent period since 2024-06-01 versus full history
A detailed examination of the recent window from June 1, 2024, through September 4, 2026, provides valuable insight into contemporary strategy health. During this recent window, the strategy completed 33 trades, producing a cumulative return sum of +92.78%. This trade sample represents nearly one-third (32.35%) of all historical trade activity, indicating an increase in signal completion frequency relative to the multi-year baseline. While full calendar year 2024 closed slightly in negative territory at -4.36% (reflecting weakness in the early months of 2024), performance from June 2024 onward rebounded significantly. The +92.78% sum generated across 33 post-June 2024 trades demonstrates that the strategy's underlying swing mechanics remain effective in current market conditions, providing a strong recent sample size for traders evaluating automated deployment.
Strengths and limitations
The strategy demonstrates several core quantitative strengths: - Exceptional Win Rate & Profit Factor: A 73.53% win rate combined with a 3.75 Profit Factor provides a high statistical edge. - Non-Concentrated Returns: The top 3 winning trades contribute only 13.59% of gross profits, ensuring performance is distributed across many trades. - Positive Payoff Distribution: A median trade of +3.15% exceeding the average trade of +2.85% highlights healthy return symmetry. - High Multi-Year Consistency: Positive performance in 7 out of 8 calendar years. The strategic limitations include: - Single-Trade Tail Risk: The worst historical trade loss of -26.05% matches the maximum drawdown, highlighting vulnerability to sharp adverse moves. - Low Execution Frequency: Averaging 14.64 trades per year, the strategy requires patience and long observation periods to realize its statistical edge. - Multi-Day Exposure: An average holding period of ~7.5 days exposes open positions to overnight gap risk inherent in individual stock equities.
DevioLab analytical conclusion
The quantitative evaluation of this APTV trading strategy establishes it as a highly capable swing-trading model. By leveraging 15-minute candle structures to enter multi-day positions, the strategy captures directional market movements while maintaining a low turnover rate of ~1.48 trades per month. Its rank 1 position for APTV with a score of 73.07 is backed by solid statistical fundamentals: a 73.53% win rate, a 3.75 Profit Factor, and low winner concentration. While automated trading agents executing this logic must account for single-position tail risk—as evidenced by the -26.05% maximum trade drawdown—the algorithm's short losing streaks (maximum 3 consecutive losses) and high historical win frequency make it a compelling candidate for quantitative portfolio integration.
Data scope and methodology
This analysis is based strictly on historical backtested performance data for ticker APTV on a 15-minute (15m) chart interval covering the period from September 16, 2019, to September 4, 2026. All metrics—including total return (+1044.24%), trade counts (102 completed trades), win rates, drawdowns, and holding times—reflect simulated closed positions calculated without adjustment for live execution factors such as slippage, variable broker commissions, order queue latency, or margin borrowing costs. Historical performance is purely analytical and does not guarantee future results.