COP · ConocoPhillips
Stock Universe · global stock marketAlgorithmic Swing Trading Dynamics in COP Stock: A Quantitative Performance Analysis
This analytical report evaluates a long-horizon algorithmic trading strategy for ConoCOPhillips (COP) stock across a seven-year historical simulation period from September 2019 to October 2026. Operating on a 15-minute price evaluation interval, the automated strategy completed 70 trades, achieving an 80.0% win rate, a 2.50 profit factor, and a cumulative simulated gain of +2075.67%. Characterized by low trade frequency (9.91 trades per year) and multi-week position holdings averaging 544.57 hours, the quantitative model demonstrates remarkable consistency and distributed profitability, with its top three winning trades contributing less than 19% of total gross returns.
Analysis as of 08.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The evaluated quantitative strategy, designated as COP 0 +2075.67% 1TRAD-MPR4, is a core algorithmic trading model built for ConoCOPhillips (COP) equity data. Evaluated on a 15-minute candlestick chart structure, the strategy utilizes granular intraday pricing data to identify entry and exit triggers while executing a swing to position-style holding methodology. Across a continuous backtesting window spanning September 16, 2019, through October 8, 2026—a period of approximately 7.06 years—the model generated a cumulative backtested return of +2075.67%, corresponding to an annualized metric of 123.87%. Within the broader DevioLab analytical framework, this system earns a score of 74.97, placing it second among backtested strategies for the COP asset symbol. The strategy completed a total of 70 trades over the full dataset, reflecting a highly selective signal filter designed for deployment within automated trading bots, trading robots, and algorithmic agents seeking systematic exposure to COP stock movement.
Trading rhythm and position duration
Despite utilizing a high-frequency 15-minute bar interval for monitoring price action, the strategy operates with a deliberate, low-frequency execution rhythm. Over the 7.06-year test history, the strategy averaged 9.91 completed trades per year, which translates to roughly 1.25 trades per active month. The time spent within positions highlights a distinct swing-to-position trading approach: the average holding duration per trade stands at 544.57 hours (approximately 22.7 days), while the median holding duration is 383.63 hours (around 16.0 days). The spacing between trade exits is similarly stretched, exhibiting an average interval of 35.22 days and a median of 29 days. This temporal profile indicates that automated trading agents executing this logic are not scalping intraday fluctuations, but rather using 15-minute price resolution to pinpoint structural entry points for multi-week price trends.
Quality of historical results
The statistical quality of the strategy's closed trades exhibits a balanced payoff distribution and high signal accuracy. Out of 70 total historical trades, 56 were closed with positive returns, yielding an overall win rate of 80.0%. The mean trade return across all closed positions was +5.17%, while the median trade return stood at +4.63%. The close proximity between the mean and median trade returns indicates that the overall return profile is not artificially inflated by extreme single-trade outliers. Furthermore, the top three winning trades accounted for only 18.90% of total gross profits. This relatively low concentration ratio demonstrates that profits are broadly distributed across the 56 winning trades rather than dependent on a few isolated market surges. The overall profit factor of 2.50 underscores a healthy ratio of gross gains relative to gross losses over the full historical sample.
Risk, drawdown and losing behavior
An examination of the strategy's risk profile reveals key behavioral characteristics during adverse market movements. The maximum historical drawdown recorded across the dataset was 31.90%. Notably, this drawdown figure closely mirrors the strategy's single worst trade loss of -31.90%, suggesting that peak-to-trough equity declines were heavily influenced by specific large adverse moves rather than long, compounding series of consecutive losses. Indeed, the longest losing streak observed over the seven-year evaluation period was restricted to just 2 trades, whereas the longest winning streak reached 10 consecutive positive trades. While the 80.0% hit rate provides substantial statistical buffer, the presence of a -31.90% worst-case trade outcome illustrates that risk management within automated trading software executing this logic must account for occasional tail-risk price shifts during multi-week holding periods.
Behavior through time and yearly stability
Analyzing annual performance metrics reveals strong consistency in cumulative returns alongside subtle shifts in signal efficiency over time. In 2020, the strategy recorded 9 trades with an 88.89% win rate, yielding +54.27% total return. In 2021, 6 trades produced +50.75% with an 83.33% win rate. The most active and lucrative calendar year was 2022, which saw 16 trades, an 87.50% win rate, and a net sum return of +130.63%. Performance moderated in 2023 with 14 trades (+43.38%, 85.71% win rate). In 2024, the strategy posted 6 trades with a 66.67% win rate and a net return of +6.96%. The subsequent years in the backtest dataset maintained net positive results: 2025 yielded +32.76% across 10 trades (70.00% win rate), while the 2026 segment through October added +42.87% across 9 trades (66.67% win rate). Across all individual calendar years in the test window, annual net return remained positive.
Recent period since 2024-06-01 versus full history
A focused examination of trades completed on or after June 1, 2024, provides insight into recent strategy performance. During this recent window, the strategy executed 24 closed trades, accumulating a combined sum return of +82.29%. Notably, these 24 recent trades represent over one-third (34.3%) of the strategy's total 70 historical completed positions. This increased operational density in the recent period aligns with stable performance, generating over 80 percentage points of cumulative gain over approximately two years of forward testing data. While annual win rates during 2024, 2025, and 2026 (ranging from 66.67% to 70.00%) were slightly below the full historical average of 80.0%, the trade frequency and positive total net return demonstrate sustained viability in recent market conditions.
Strengths and limitations
The primary analytical strength of this COP trading strategy lies in its combination of a high historical win rate (80.0%), a robust profit factor (2.50), and a well-distributed gross profit structure where no single trade dominates equity growth. The low trade frequency (9.91 trades per year) naturally limits over-trading and trade churn for automated execution systems. Conversely, the principal structural limitation is the potential magnitude of individual losing trades, as evidenced by a worst historical trade of -31.90% and a matching maximum drawdown of 31.89%. Additionally, because position holding times average over 500 hours, capital remains committed for extended periods, requiring traders utilizing automated agents to accommodate multi-week holding durations and overnight market exposure.
DevioLab analytical conclusion
With a DevioLab Score of 74.97 and a rank of #2 for ConoCOPhillips equity strategies, this algorithmic model presents a compelling case study in long-horizon intraday-triggered swing trading. The quantitative data demonstrates that combining 15-minute price granularity with long position holding times can yield high win rates and low profit concentration. While the historical maximum drawdown highlights the necessity of strict position sizing, the strategy's year-over-year profitability across seven simulated years underscores its structural resilience. For algorithmic trading environments and trading robots, the logic offers a systematic, highly selective approach to capturing multi-week market movements in COP stock.
Data sCOPe and methodology
The statistics analyzed in this document are derived strictly from historical simulated backtesting conducted on COP stock between September 16, 2019, and October 8, 2026. All metrics are calculated based on closed trades evaluated at 15-minute candle intervals. The reported returns reflect theoretical model performance and do not include real-world slippage, execution delays, transaction fees, brokerage commissions, or interest charges. Historical backtested results are presented exclusively for quantitative research and software analysis purposes and do not guarantee future performance or constitute financial advice.