APA · APA Corporation
Stock Universe · 全球股票市场Quantitative Strategy Analysis: APA · APA 15-Minute Algorithmic Model Demonstrates High-Conviction Trend Capture
This quantitative research report provides a rigorous analytical breakdown of the core algorithmic trading strategy developed for APA on a 15-minute bar interval over a 7.01-year historical dataset spanning from September 2019 to September 2026. Demonstrating a historical win rate of 72.46% across 69 completed trades, the strategy balances low trade frequency (9.85 trades per year) with substantial holding durations (averaging 468.7 hours, or approximately 19.5 days per position). With a cumulative historical trade sum of +64,492.24% and an overall profit factor of 2.50, the strategy highlights the efficacy of applying granular intra-day price data to capture intermediate macro swings. This analysis examines the strategy's quality metrics, drawdown characteristics, yearly performance variations including the recovery since mid-2024, and its structural role as an automated decision engine for trading bots and execution agents.
分析日期： 17.09.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The core quantitative strategy designated for APA operates on a 15-minute candlestick chart interval, evaluating historical price data spanning 7.01 years between September 16, 2019, and September 17, 2026. Despite utilizing a fine 15-minute time resolution, the strategy is engineered to filters out short-term market noise, executing a total of 69 completed trades throughout the multi-year history. This translates to an average active trade frequency of 9.85 trades per year, or approximately 1.17 trades per active month. Evaluated within the DevioLab framework, the strategy secures a score of 70.87 and holds the 3rd rank for the APA ticker. The model serves as high-conviction decision logic suitable for integration into automated trading bots, trading robots, and quantitative trading agents designed for equities, enabling structured position management without requiring manual order intervention.
Trading rhythm and position duration
A defining characteristic of this strategy is the marked disparity between its signal generation interval and its historical trade duration. While signals are processed on 15-minute bars, positions are held for multi-week timeframes. The average position holding time stands at 468.7 hours (roughly 19.5 days), while the median holding time is 335.0 hours (approximately 13.96 days). This disparity indicates that while entries are timed using granular intra-day structure, exits are dictated by multi-day trend developments rather than scalp targets. Furthermore, the temporal spacing between trade exits reflects deliberate patience: the average duration between closed trades is 37.14 days, with a median spacing of 34.0 days. For automated execution agents, this rhythm implies prolonged periods of market exposure interspersed with long observation intervals, significantly curtailing historical trade volume while allowing individual trends to fully materialize.
Quality of historical results
The performance profile of the strategy demonstrates strong statistical asymmetry in favor of winning trades. Across the 69 closed positions, 50 concluded with positive returns while 19 resulted in losses, yielding a 72.46% win rate. The strategy achieves a profit factor of 2.50, indicating that gross historical gains outweighed gross losses by a factor of two and a half. The expected magnitude per trade remains substantial: the average trade return is +11.60%, closely aligned with the median trade return of +9.52%. The single best trade generated a return of +109.82%, whereas the single worst trade registered a loss of -35.93%. Notably, the top three winning trades account for 23.92% of the cumulative gross profits. This moderate concentration factor confirms that the strategy's long-term total return (+64,492.24%) is driven by consistent, recurring statistical payoff across multiple cycles rather than being disproportionately reliant on a singular black-swan winner.
Risk, drawdown and losing behavior
Risk metrics reveal that high structural win rates do not eliminate equity curve volatility. The strategy experienced a maximum backtested peak-to-trough drawdown of 37.41%. This drawdown reflects the potential impact of deep individual trade adverse moves, as highlighted by the historical worst trade of -35.93%. However, the strategy exhibits remarkable discipline regarding consecutive losses: the longest recorded losing streak is restricted to just 3 trades, whereas the longest winning streak extended to 9 trades. The combination of a 72.46% hit rate and constrained losing streaks indicates that historically, drawn-out loss sequences were rare. Nevertheless, the peak drawdown of 37.41% underscores that holding multi-week positions during equity drawdowns requires robust capital preservation strategies and risk management frameworks when configuring automated trading bots.
Behavior through time and yearly stability
Annual performance tracking illustrates how the strategy responds across varying equity market environments. Strong performance was delivered during 2020 (11 trades, 72.73% win rate, +313.96% sum) and 2021 (13 trades, 76.92% win rate, +155.14% sum). The strategy maintained profitability in 2022 (9 trades, 88.89% win rate, +98.91% sum), followed by a consolidation phase in 2023 (10 trades, 50.00% win rate, +25.38% sum). The year 2024 represented a temporary historical drag, recording a net flat result of -0.40% across 6 trades with a 33.33% win rate. However, the quantitative model rebounded vigorously in 2025 (11 trades, 90.91% win rate, +110.60% sum) and maintained momentum into 2026 (7 trades, 71.43% win rate, +79.94% sum). Trade cadence remained consistent throughout, varying between 6 and 13 trades per year, demonstrating persistent market engagement without signal proliferation.
Recent period since 2024-06-01 versus full history
An examination of the window beginning June 1, 2024, provides valuable insight into the strategy's recent efficiency relative to its overall history. Within this sub-period, 22 completed trades closed, delivering a combined return sum of +188.62%. This cluster of 22 trades represents nearly one-third of all historical trade activity, indicating an accelerated frequency of signal completions in recent market conditions. Comparing this recent trajectory to the full multi-year history demonstrates that the model successfully navigated past the soft performance observed during early 2024. The strong return sum of +188.62% achieved across these 22 positions confirms that the model's intermediate trend-following cAPAbilities remained effective through recent structural shifts in APA.
Strengths and limitations
The primary analytical strength of this strategy lies in its robust expectancy and execution structure: a high win rate of 72.46%, a profit factor of 2.50, and a well-distributed profit structure where the top three trades represent only 23.92% of gross profit. The low annual trade count (9.85 trades/year) offers operational benefits for automated trading agents by minimizing execution frequency and market exposure duration relative to total time. Conversely, the principal limitation stems from the sample size of 69 trades over 7.01 years, which, while sufficient for initial statistical modeling, remains relatively compact. Additionally, the maximum drawdown of 37.41% and a worst-case trade return of -35.93% highlight significant downside exposure during sharp trend reversals, requiring disciplined position sizing.
DevioLab analytical conclusion
With a DevioLab score of 70.87 and a #3 overall rank for APA, this 15-minute algorithmic strategy establishes a compelling historical benchmark for medium-term equities trading logic. By translating intra-day bar patterns into multi-week holding patterns (averaging 19.5 days), the strategy successfully bridges signal resolution with macro trend capture. Its historical ability to generate a 2.50 profit factor alongside controlled losing streaks (maximum 3 consecutive losses) reinforces its quantitative validity. For algorithmic trading deployments, this strategy provides a structured, high-expectancy framework suitable for automated execution robots seeking disciplined swing capture in APA.
Data scope and methodology
The metrics presented in this report are derived from historical backtested trade executions for APA on a 15-minute chart interval covering the period from September 16, 2019, to September 17, 2026. Performance statistics, including recent statistics starting June 1, 2024, are calculated based on closed trades where the sell event occurred within the specified timeframe. All returns represent unleveraged percentage changes per trade and do not account for variable slippage, order routing delays, or transaction fees. Historical simulated results are purely analytical tools for evaluating strategy logic and do not represent guaranteed future performance or real-time trading executions.