CFXUSDT
Crypto market · BinanceCFX Quantitative Analysis: Swing-Scale Holding Horizons and High Win-Rate Structure on a 15-Minute Execution Framework
This analytical evaluation examines the historical performance profile of the core algorithmic strategy for CFX on a 15-minute timeframe over a 4.54-year backtest window from March 29, 2021, to October 12, 2025. Generating a historical cumulative return of 19,012.62% across just 34 completed trades, the strategy exhibits an exceptionally high win rate of 85.29% (29 wins against 5 losses) alongside a Profit Factor of 3.33 and a maximum drawdown capped at 18.85%. Despite evaluating price action on a 15-minute candle resolution, the algorithm operates as a highly selective swing trading system, maintaining an average position holding duration of 279.40 hours and averaging only 7.49 trades per year. The statistical evidence reflects structural stability across diverse market cycles, reinforced by a strong recent performance window since June 1, 2024, which generated a cumulative 264.53% return across 12 trades.
Analysis as of 26.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The evaluated algorithm represents a core quantitative model designed for CFX within the cryptocurrency market. Operating on a 15-minute price chart resolution, the strategy earns a DevioLab score of 75.24 and holds the rank 4 spot among evaluated algorithms for this specific asset. Across the complete historical backtest period spanning 4.54 years—from March 29, 2021, to October 12, 2025—the strategy logged a total of 34 completed trades. It achieved a cumulative historical profit of 19,012.62%, which translates to an annualized return figure of 810.78%. The historical trade distribution is heavily skewed toward positive outcomes, with 29 winning closed trades against 5 losing closed trades, establishing an overall hit rate of 85.29%. The aggregate Profit Factor stands at 3.33, demonstrating that historical gross profits substantially outpaced total gross losses. The model is categorized as a core strategy based on its top-tier independent performance metrics within the asset dataset.
Trading rhythm and position duration
Although the algorithm evaluates price data on a 15-minute chart, its operational rhythm is characterized by low transaction frequency and extended holding horizons typical of multi-day swing trading. The system averages 7.49 completed trades per year, or approximately 1.48 trades per active month. Across the 34 historical trades, the average position duration reached 279.40 hours (approximately 11.6 days), while the median holding duration stood at 121.00 hours (roughly 5.0 days). This right-skewed holding time distribution implies that while typical trades exit within five days, a selection of trend-following positions remains open for multiple weeks to capture larger market movements. Inter-trade pacing is similarly relaxed: the average time between trade exits is 49.64 days, with a median spacing of 23.99 days. Consequently, market exposure is sparse, requiring long periods of patience between execution events and eliminating high-frequency churn.
Quality of historical results
The historical return profile demonstrates strong payoff quality paired with broad profit contribution across trades. The strategy achieved an average trade profit of 18.43%, while the median trade profit was 15.59%. The close alignment between average and median returns indicates that the overall return stream is driven by a consistent distribution of gains rather than being artificially inflated by a single massive statistical anomaly. The single best trade generated a return of 87.09%, whereas the worst trade logged a loss of -18.85%. Further analysis of trade concentration reveals that the top three winning trades accounted for 27.89% of historical gross profit. Because fewer than thirty percent of gross gains are concentrated in the top three outcomes, the strategy demonstrates that its cumulative profitability relies on broad-based performance across its 29 winning trades rather than extreme outlier dependency.
Risk, drawdown and losing behavior
From a risk management perspective, the strategy displayed tight downside control throughout the 4.54-year evaluation window. The maximum historical drawdown recorded was 18.85%, matching exactly the magnitudinal loss of the strategy's worst single trade (-18.85%). This structural symmetry reveals that drawdowns were primary reflections of individual isolated losing trades rather than compounded sequences of consecutive failures. The longest losing streak observed in the dataset was exactly 1 trade, meaning the strategy never suffered back-to-back losses across its 34 historical trades. Conversely, the longest consecutive winning streak reached 12 trades. With an 85.29% win rate and isolated single-trade losses, equity curve drawdowns were historically brief, recovering quickly as winning trades resumed.
Behavior through time and yearly stability
Analyzing the strategy on a calendar-year basis demonstrates consistent historical performance across changing crypto market conditions. In 2021 (from March 29), the strategy completed 10 trades with an 80.00% win rate (8 wins, 2 losses), generating a sum return of 236.66%. During the challenging 2022 market environment, activity contracted significantly to 3 trades (2 wins, 1 loss, 66.67% win rate), closing the year with a slight net loss sum of -12.09%. In 2023, performance rebounded with 6 trades, all of which were profitable (100.00% win rate), accumulating a sum return of 105.70%. In 2024, the strategy executed 9 trades with an 88.89% win rate (8 wins, 1 loss), totaling 151.61%. In 2025 (up to October 12), it logged 6 trades with an 83.33% win rate (5 wins, 1 loss) and a summed profit of 144.66%. Except for a minor pull-back in 2022, every full or partial calendar year recorded substantial net gains.
Recent period since 2024-06-01 versus full history
Evaluating the recent sample window from June 1, 2024, to October 12, 2025, provides insight into recent strategy behavior. During this period of approximately 16.4 months, the strategy executed 12 completed trades, generating a combined return sum of 264.53%. This recent period accounts for over a third (35.29%) of all 34 historical trades executed since March 2021. The trade frequency during this window increased relative to the historical baseline, averaging roughly 0.73 trades per month compared to the long-term historical average of 0.62 trades per month. The high concentration of trades and robust return output of 264.53% indicate that recent market conditions have provided favorable alignment for the algorithm's entry and exit mechanics.
Strengths and limitations
The primary historical strength of this strategy lies in its combination of a high win rate (85.29%), low maximum drawdown (18.85%), and well-distributed profit sources, where the top three winners contribute less than 28% of gross returns. The low trade count and long holding times (average 279.40 hours) mean the system avoids market noise and over-trading. However, key analytical limitations must be noted. The strategy's main constraint is its small total sample size of 34 closed trades over 4.54 years. Statistically, a low sample size reduces the confidence interval around win rate and expected return metrics. Additionally, the low execution frequency means traders must endure long periods of inactivity—averaging nearly 50 days between trade exits—which requires significant behavioral discipline during extended flat periods.
DevioLab analytical conclusion
With a DevioLab score of 75.24 and a ranking of 4 for CFX, this strategy represents a highly disciplined, swing-oriented algorithmic framework. Its historical record is defined by exceptional trade selectivity, achieving a 3.33 Profit Factor and an 85.29% win rate while strictly limiting maximum drawdown to 18.85%. The structural stability observed across multiple yearly cycles, combined with sustained positive results in the recent evaluation window since June 2024 (+264.53% across 12 trades), confirms its historical efficacy. While prospective users must consider the low trade frequency and modest sample size of 34 historical events, the data demonstrate a robust risk-reward profile.
Data scope and methodology
The metrics presented in this analysis are derived strictly from historical simulated backtest data for CFX on a 15-minute timeframe between March 29, 2021, and October 12, 2025. All statistics reference completed closed trades without accounting for external leverage, variable order execution slippage, or exchange fee tiers unless specified. The recent performance sub-sample includes all trades with exit timestamps on or after June 1, 2024 UTC. Past backtested performance is an analytical tool for evaluating statistical characteristics and structural patterns; it does not guarantee future operational performance or real-money account returns.