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公开目录 · DEVIOLAB

策略目录

探索算法策略、比较表现,并查看每个模型的完整历史。
不知道从哪里开始?
1. 目录 2. 选择 Core 1 3. 加入篮子 4. API 密钥 5. 激活交易机器人
我们的建议: 首次配置建议使用 Core 1——这是 DevioLab 的主要、更稳健的筛选。Core 2 适合明确愿意承受更高风险和更深回撤的用户。
i
什么是 Core 1 和 Core 2?
DevioLab 会分析加密货币和股票市场策略,并整理成可直接使用的精选组合,让您无需手动筛选数百种方案。
★ Core 1
由 DevioLab 筛选的加密货币与股票策略组合,作为主要、更均衡且更稳健的起步选择,重点控制风险与回撤。
◆ Core 2
DevioLab 的独立进取型精选,适合明确愿意承受更高风险和更深回撤,以换取潜在更高收益的用户。
所选策略概览

CFXUSDT

加密市场 · Binance
CFX 215000 +19012.62% 1TRAD-BSY4
DevioLab 推荐 · Core 2 iDevioLab 更激进的推荐:接受更高风险和更深回撤,以换取潜在更高回报。
34交易
85.3%胜率
+18.43%平均交易
+87.09%最佳交易
-18.85%最差交易
+810.8%年化
策略分析档案 · 38868e8fd1f65cf3

CFX · CFX Quantitative Strategy Analysis: 15m Performance Profile

In-depth quantitative analysis of a 15-minute algorithmic trading strategy for CFX over a 5.38-year historical sample. The model highlights an 85.29% win rate, 19012.62% cumulative return, and an 18.85% maximum drawdown.

阅读完整分析

Strategy profile

This quantitative trading strategy operates in the crypto market using the CFX asset on a 15-minute timeframe. The historical evaluation spans 5.38 years from March 29, 2021, through August 14, 2026. Within the scope of evaluated models for CFX, this strategy ranks 3rd with a DevioLab Score of 72.36. The strategy functions as a low-frequency, high-selectivity framework designed to capture substantial price movements while maintaining disciplined capital protection rules.

Trading rhythm and position duration

Across the entire 5.38-year sample period, the strategy executed a total of 34 completed trades, translating to approximately 6.32 trades per year. This low frequency confirms that the underlying logic requires strict confluence criteria before opening a position. Although average holding hours and days between exits are not recorded in this specific dataset, the low annual trade volume demonstrates a patient swing-oriented posture rather than continuous market exposure.

Quality of historical results

The quality of historical trades remains strong throughout the tested period. Out of 34 completed trades, 29 were winning trades and 5 were losing trades, establishing an 85.29% win rate. Total cumulative return reached 19012.62%, translating to an annualized return of 810.78%. The average trade return was 18.43%, while the median trade return stood at 15.59%. The best trade produced a 87.09% gain. With a profit factor of 5.00 and the top 3 winning trades representing 27.89% of gross profit, the overall return profile reflects healthy distribution across multiple winning setups.

Risk, drawdown and losing behavior

Risk parameters were contained relative to total returns. The strategy registered a maximum historical drawdown of 18.85%, which directly corresponds to the worst individual losing trade of -18.85%. The maximum winning streak reached 12 consecutive trades, whereas the longest losing streak did not exceed 1 trade. This indicates that drawdown episodes were isolated and quickly resolved by subsequent trade executions.

Behavior through time and yearly stability

Because granular yearly breakdowns are omitted from this source dataset, performance continuity is evaluated through the average execution rate of 6.32 trades per year over 5.38 years. This trade density confirms that long periods of inactivity are normal for this model, as it prioritizes setup quality over trade frequency.

Strengths and limitations

Key strengths include a high historical win rate of 85.29%, a robust profit factor of 5.00, a controlled maximum drawdown of 18.85%, and balanced profit concentration with the top 3 trades contributing 27.89% of gross profit. Limitations stem from the small total sample size of 34 trades over more than five years, extended dormant periods such as zero trades since June 2024, and the absence of granular duration metrics.

DevioLab analytical conclusion

Holding a DevioLab Score of 72.36 and ranking 3rd for CFX, this strategy displays impressive historical efficiency characterized by strong trade accuracy and limited drawdown depth. However, because the total dataset comprises only 34 trades, traders should consider sample size limitations and apply cautious risk management when analyzing simulated performance.

Data scope and methodology

This analysis covers simulated closed trades for CFX on the 15m timeframe from March 29, 2021, to August 14, 2026. All percentage figures represent historical backtest results, do not reflect live Binance exchange account returns, and offer no guarantee of future trading performance.