CF · CF Industries Holdings, Inc.
Stock Universe · global stock marketQuantifying High-Win-Rate Swing Logic for CF: An In-Depth Analysis of a Core 15-Minute Algorithmic Strategy
This analytical report evaluates the top-ranked algorithmic trading strategy for CF stock on a 15-minute timeframe. Spanning over seven years of backtested data from September 2019 through September 2026, the strategy generated 147 completed trades with an exceptionally high win rate of 80.27 percent and a profit factor of 5.0. Demonstrating strong statistical consistency across changing market regimes, the system yields an average trade return of 3.69 percent alongside a median trade return of 3.94 percent, showing that returns are distributed broadly rather than relying on rare outliers. With a maximum historical drawdown of 21.75 percent and a maximum losing streak of just two trades, the strategy provides an exceptionally resilient performance profile suitable for implementation in automated trading bots, trading robots, and quantitative trading agents. This study breaks down the position duration, yearly distribution, winner concentration, and recent validation since June 2024 to evaluate the robustness of its cumulative backtested return of 11,020.79 percent.
Analysis as of 30.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The evaluated algorithmic model represents the primary core strategy for CF stock within the DevioLab framework, earning a top rank for this symbol with a DevioLab score of 81.38. Operating on a 15-minute candle interval, the system translates intraday micro-price action into multi-day positions, bridging short-term price discovery with medium-term swing movements. Over a simulated evaluation period of 7.04 years spanning from September 16, 2019 to September 30, 2026, the strategy generated 147 completed trade cycles. Across this dataset, the model produced a cumulative simulated return of 11,020.79 percent, corresponding to an annualized metric of 261.58 percent. The overall trade accuracy stands at 80.27 percent, with 118 winning outcomes against 29 losing outcomes. This statistical structure produces an overall profit factor of 5.0, reflecting a highly efficient relationship between gross profits and gross losses. Designed to function as the decision engine for automated trading bots, trading robots, and systematic trading agents, the strategy relies on selective execution parameters that prioritize trade quality and statistical expectancy over trade frequency.
Trading rhythm and position duration
Despite operating on a 15-minute price chart, the strategy exhibits the holding profile of a classic multi-day swing system rather than a high-frequency scalper. Across the 147 completed positions, the average holding time was 285.68 hours (approximately 11.9 days), while the median holding duration registered at 168.00 hours (exactly 7.0 days). This disparity between mean and median holding times indicates that while half of all trades resolve within a single calendar week, select positions are maintained across multiple weeks to capture extended directional moves. The strategy completed an average of 20.88 trades per year, which translates to approximately 1.99 active trades per month. On average, 17.46 days elapsed between trade exits, with a median spacing of 13.81 days. For execution via automated trading bots or algorithmic trading agents, this low-frequency rhythm implies limited turnover and reduced exposure to execution friction, allowing the model to navigate intraday price noise on the 15-minute timeframe while capturing macroeconomic trends in CF stock.
Quality of historical results
The structural quality of the strategy's returns is highlighted by the close alignment between its average trade performance and its median trade performance. The average trade gain across all 147 closed trades stands at 3.69 percent, while the median trade return is slightly higher at 3.94 percent. In quantitative strategy evaluation, a median that matches or exceeds the average indicates that historical profits are generated uniformly across standard trades rather than being inflated by a small handfull of massive winners. Furthermore, the three largest winning trades accounted for only 10.63 percent of total gross profits. This exceptionally low concentration ratio confirms that the strategy's high performance is broad-based and statistically stable. The historical payoff range extends from a single best trade return of 31.07 percent to a worst trade loss of -19.22 percent. Combined with the overall 80.27 percent win rate, the system exhibits strong positive expectation on every trade event executed by automated trading systems.
Risk, drawdown and losing behavior
Risk parameters demonstrate significant resilience throughout the seven-year testing sample. The maximum historical drawdown was constrained to 21.75 percent, a moderate equity pullback given the overall cumulative return of 11,020.79 percent. The losing behavior of the model is notably disciplined: the longest consecutive losing streak across 147 historical trades was capped at just two trades. This low loss cluster rate means that equity drawdowns were historically brief and recovered rapidly, minimizing extended drawdown duration. The strategy's worst single trade loss of -19.22 percent represents a defined tail risk event, but its impact was consistently mitigated by the strategy's high win frequency (80.27 percent) and strong profit factor of 5.0. From a risk management perspective for automated trading agents and execution bots, the combination of short losing streaks and modest maximum drawdown supports consistent capital allocation strategies.
Behavior through time and yearly stability
An examination of yearly breakdown statistics reveals consistent profitability across distinct market conditions for CF stock. Following an initial setup phase in late 2019 consisting of 2 trades with a sum return of -1.94 percent, the strategy established a multi-year track record of positive performance. In 2020, the strategy recorded 21 trades, a 71.43 percent win rate, and a summed trade return of 100.88 percent. Performance peaked during 2021 and 2022, generating 131.63 percent across 25 trades (84.00 percent win rate) and 116.02 percent across 23 trades (86.96 percent win rate), respectively. The system remained positive during 2023 with 22 trades, an 81.82 percent win rate, and a summed gain of 23.97 percent. In 2024, performance rebounded with 16 trades, an 81.25 percent win rate, and a summed return of 48.55 percent. The model generated 16.16 percent in 2025 across 16 trades (68.75 percent win rate) before accelerating in 2026 to produce 107.41 percent across 22 trades with an 86.36 percent win rate. The strategy consistently generated positive summed returns across every full calendar year tested.
Recent period since 2024-06-01 versus full history
The performance window starting June 1, 2024 provides crucial evidence regarding the recent effectiveness of the algorithmic model. During this period, the strategy recorded 46 completed trades, representing 31.29 percent of its total lifetime trades. Summed returns during this recent sub-period reached 148.54 percent. Because 46 trade events offer a robust statistical sample size, this recent period confirms that the strategy's edge in CF stock has remained active and sustained rather than decaying over time. Comparing recent trade density with historical averages shows a slight increase in trade execution rate, as 46 trades occurred across approximately 2.33 years. The strategy's ability to continue producing substantial cumulative gains in modern market conditions offers quantitative validation for systematic deployment in live trading robots and trading agents.
Strengths and limitations
The primary strength of this strategy lies in its high statistical expectancy, driven by an 80.27 percent win rate, a 5.0 profit factor, and a broad distribution of returns where the top three trades account for only 10.63 percent of gross profit. The system's disciplined losing streak ceiling of two trades further reduces capital drawdown risk. However, specific structural limitations must be noted. The worst trade loss of -19.22 percent indicates that while losing trades are rare, individual drawdowns on failed positions can be significant relative to the average trade gain of 3.69 percent. Additionally, with an average holding time of 285.68 hours, positions are held open across multiple overnight sessions and weekends, exposing active positions to gap risk in CF equity price action that cannot be adjusted while markets are closed.
DevioLab analytical conclusion
With a DevioLab score of 81.38 and a number-one rank for CF stock, this 15-minute algorithmic strategy represents a robust, highly stable swing trading framework. Its statistical profile balances high accuracy (80.27 percent win rate) with controlled equity pullbacks (21.75 percent maximum drawdown) and low top-winner dependency. The strong historical output across both multi-year full data and the recent post-June 2024 window confirms the underlying edge of the system logic. Designed for implementation within automated trading bots, trading robots, and algorithmic agents, the system provides a structured mechanism for capturing multi-day trend movements in CF stock while filtering out lower-probability intraday signals.
Data scope and methodology
This analysis is based strictly on backtested, simulated historical trade data for CF stock across the 15-minute interval covering September 16, 2019 through September 30, 2026. All reported performance metrics, including total return percentages, win rates, drawdown figures, holding durations, and profit factors, are computed from closed trade records within this specific simulation environment. Historical strategy results do not include real-world slippage, execution fees, borrow costs, or unexpected market liquidity conditions. Past backtested performance is strictly evaluative and does not guarantee future live trading results.