DVN · Devon Energy Corporation
Stock Universe · global stock marketDVN · DVN Algorithmic Trading Strategy Analysis: High Win Rate Dynamics, Multi-Day Swing Duration, and Drawdown Management
This quantitative evaluation inspects a 15-minute algorithmic trading strategy applied to DVN stock across a seven-year historical dataset spanning September 2019 to September 2026. Achieving a cumulative closed-trade gain of +17,549.62% and an 80.87% win rate across 115 completed trades, the strategy demonstrates a strong profit factor of 3.75 and an average trade return of +5.71%. Designed as high-conviction decision logic suitable for automated trading bots and trading agents, the strategy pairs granular intraday candle analysis with extended multi-day holding periods averaging 295.9 hours. However, the system exhibits substantial risk exposure, highlighted by a maximum peak-to-trough drawdown of 64.56% and a worst individual trade loss of -61.57%. This analysis details the structural trade-offs between high win consistency, trade distribution, regime shifts across market cycles, and recent performance since June 2024.
Analysis as of 22.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The DVN algorithmic trading strategy operates on a 15-minute bar interval for DVN stock, processing high-frequency price action to generate discrete position signals over a complete seven-year dataset running from September 16, 2019, to September 14, 2026. Over this backtested window, the algorithm completed 115 trades, yielding a cumulative total trade return sum of +17,549.62% and an annualized rate of 124.62%. Within the DevioLab quantitative evaluation framework, the system earned a DevioLab score of 49.06, placing it third in rank among strategies tested on this specific asset ticker. The core architecture relies on selective signal filtering, generating an overall win rate of 80.87% through 93 winning trades against 22 losing positions. The resulting profit factor stands at 3.75, indicating that gross historical gains exceeded gross losses by nearly fourfold. Designed to serve as the analytical decision module for automated trading bots, trading robots, and autonomous trading agents, the logic translates intraday technical events into structured execution directives. Rather than engaging in hyperactive trading, the rules enforce patient exposure parameters that prioritize position quality over operational volume.
Trading rhythm and position duration
Despite utilizing a 15-minute candlestick chart for signal identification, the strategy displays position holding characteristics far removed from high-frequency intraday trading or scalping. Across the 115 completed historical trades, the average holding duration reached 295.8 hours, equivalent to approximately 12.3 days, while the median holding time was 192.0 hours, or exactly 8 trading days. This pronounced duration disparity between the 15-minute bar frequency and multi-day position lifespans indicates that short-term price bars are utilized for precise entry timing and trailing exit execution rather than rapid turnover. The strategy exhibits an exceptionally disciplined trading cadence, averaging 16.44 completed trades per year and 1.58 trades per active month. Exits occur at extended intervals, with an average of 22.09 days between trade closures and a median spacing of 19.38 days. Consequently, trading agents deploying this logic spend substantial time holding positions through intermediate price swings, using the 15-minute resolution to continuously re-evaluate exit conditions without triggering excessive trade churn.
Quality of historical results
The quality of historical performance is defined by high statistical consistency and a remarkably well-distributed profit structure. The system achieved an average trade return of +5.71% and a median trade return of +4.65%, demonstrating that positive expectation is firmly anchored in standard trade outcomes rather than skewed by a few extreme outliers. Further evidence of structural balance is found in the gross profit concentration metric: the top three winning trades generated 20.56% of total historical gross profit. While individual top-performing trades delivered substantial returns—punctuated by a best single trade gain of +85.42%—the overall net growth was built across the broader sequence of 93 winning positions. A profit factor of 3.75 confirms that individual gains consistently outweighed capital drawdowns across the sample. The proximity of the median trade to the average trade reflects a symmetric, predictable distribution among winning outcomes, making the quantitative logic well-suited for systematic execution where edge stability across recurring trade events is required.
Risk, drawdown and losing behavior
A critical tension within the strategy's statistical footprint exists between its extraordinary win rate and its severe downside tail risk. While the system sustained an impressive 21-trade winning streak and limited its longest losing streak to just 4 consecutive trades, it experienced a maximum drawdown of 64.56%. This profound equity decline highlights the vulnerability of high-win-rate trend or swing models when structural market shifts occur while a position remains open. The single worst trade in the dataset suffered a loss of -61.57%, demonstrating that when losses do materialization, they can severely impact cumulative equity if risk parameters are unmitigated. The dynamic where 80.87% of trades succeed but occasional losing trades inflict major drawdowns indicates that losing positions were historically allowed to run through wide adverse price moves before exit criteria were met. Automated trading bots adopting this algorithmic framework must incorporate robust portfolio risk controls and position sizing models to absorb deep historical drawdown thresholds.
Behavior through time and yearly stability
An analysis of the yearly performance breakdown reveals significant structural variation across different market regimes between 2019 and 2026. The strategy experienced its most explosive growth phase during the 2020 to 2022 period. In 2020, 16 completed trades produced a sum return of +220.97% with an 87.50% win rate. Momentum continued in 2021 with 18 trades yielding +150.92% at an 88.89% win rate, followed by 2022 which recorded 22 trades, a 90.91% win rate, and +126.81% in accumulated gains. However, performance underwent a clear transition starting in 2023. Trade frequency remained stable at 20 trades in 2023, but the win rate moderated to 75.00% and total net return compressed to +40.03%. The slowdown deepened in 2024 and 2025, where annual win rates fell to 62.50% (8 trades, +28.05%) and 61.54% (13 trades, +27.65%), respectively. Performance began recovering in 2026, delivering 14 trades with an 85.71% win rate and a +58.93% return sum. This multi-year trajectory shows that while the strategy remained profitable every calendar year, its absolute edge expanded dramatically during high-trend volatility cycles and compressed during range-bound conditions.
Recent period since 2024-06-01 versus full history
Evaluating the performance window from June 1, 2024, to September 14, 2026, provides valuable insight into the algorithm's recent functionality. During this 27-month period, the strategy recorded 34 completed trades, generating a cumulative return sum of +83.31%. This equates to a substantial portion of recent overall market engagement, averaging slightly over one completed trade per month. Comparing this recent window against the full historical record reveals a subtle stabilization following the strategy's trough in early 2024. While the recent sum return of +83.31% across 34 trades reflects a lower average gain per trade than the historical multi-year average—owing to the lower win rates observed throughout late 2024 and 2025—it confirms that the strategy maintained its positive expected value without suffering terminal degradation. The recent activity underscores that trading robots deploying this logic continue to operate within established statistical boundaries, generating steady net positive returns through changing equity conditions.
Strengths and limitations
The primary structural strength of the DVN strategy lies in its exceptionally high trade accuracy, evidenced by an 80.87% historical win rate, a 3.75 profit factor, and a massive 21-trade winning streak. Furthermore, profit distribution is broad-based, with the top three winning trades accounting for only 20.56% of total gross gains, reducing reliance on rare windfalls. The strategy's multi-day holding period also minimizes overtrading and unnecessary trade exposure. Conversely, the principal limitation is severe historical peak-to-trough equity risk, demonstrated by the 64.56% maximum drawdown and an individual worst trade loss of -61.57%. Additionally, the system exhibits clear performance cyclicality, experiencing reduced win rates and smaller annual return sums during neutral market regimes such as 2024-2025 compared to the hyper-profitable 2020-2022 era.
DevioLab analytical conclusion
With a DevioLab score of 49.06 and a rank of 3 for DVN stock, this 15-minute algorithmic strategy represents a compelling swing-trading framework characterized by high precision and long position persistence. The backtested metrics demonstrate that the strategy successfully captures directional momentum over multi-day periods, producing strong cumulative statistical gains. However, its historical profile requires careful risk contextualization: the combination of an 80.87% win rate with a 64.56% maximum drawdown indicates that quantitative developers and automated trading agents must implement strict stop-loss rules, position size caps, or portfolio-level overlay controls to mitigate severe tail-risk events while preserving the core analytical edge.
Data scope and methodology
All figures, statistics, and performance indicators presented in this article are derived strictly from backtested historical trade logs for DVN stock covering the period from September 16, 2019, to September 14, 2026. The dataset encompasses 115 completed closed trades evaluated on 15-minute price candle intervals. Percentage gains and return sums reflect simulated compounding and mathematical aggregations of closed positions and do not account for live order execution factors such as slippage, variable brokerage commissions, exchange fees, or liquidity constraints. Historical performance metrics serve exclusively as an analytical evaluation of mathematical decision rules and do not constitute investment advice or guarantees of future live trading results.