Loading US stock market status…
➜
HOW TO START
Complete the full system setup in just a few minutes.
Open Quick Start Guide →
Public catalog · DEVIOLAB

TRADING STRATEGIES & BOTS

Choose a trading strategy and activate a bot that will trade automatically by its rules.
A strategy defines the buying and selling rules. A trading bot executes those rules through the API of a supported exchange. You can also use the selected strategy as a signal bot with Telegram and email notifications.
FILTERS
Choose Core 1 for a balanced start, select an exchange for trading and find the ticker you need. Filters work together.
Trading exchange?Shows instruments available for SPOT trading against USDT according to the latest normalization. Click the selected exchange again to show all exchanges.
Selected strategy overview

CFXUSDT

Crypto market · Binance
CFX 215000 +19012.62% 1TRAD-XRU8
Archived strategy
34Trades
85.3%Win rate
+18.43%Avg trade
+87.09%Best trade
-18.85%Worst trade
+810.8%Annualized
Strategy analytical profile · 115cc4562b6fcc48

CFX Quantitative Strategy Analysis: 15-Minute Framework Evaluation

A deep statistical examination of the CFX core algorithmic trading system, highlighting an 85.29% win rate, a 5.0 profit factor, and a controlled 18.85% maximum drawdown across 34 historical trades.

Analysis as of 12.08.2026. Strategy data is updated separately.
Read full analysis

Strategy profile

This analysis evaluates an algorithmic trading framework designed for CFX on the 15-minute execution timeframe. Receiving a DevioLab Score of 72.36, the system ranks second among evaluated strategies for this specific ticker. Tested over a multi-year horizon of 5.37 years from March 29, 2021, to August 11, 2026, the strategy generated an all-time historical return of 19012.62%, which translates to an annualized return figure of 810.78%. Operating within the spot cryptocurrency market context, the model focuses exclusively on capturing selective market expansions.

Trading rhythm and position duration

Despite utilizing a 15-minute timeframe for signal execution, the algorithm exhibits an extraordinarily low transaction frequency. Over the 5.37-year dataset, it completed only 34 trades, averaging approximately 6.33 trades per year. While specific holding hours and inter-trade durations are omitted from the recorded metrics, the total trade tally clearly confirms that this strategy is not a scalping or high-frequency mechanism. Instead, it operates as a patient, high-conviction momentum or structural trend system that remains idle until stringent parameters are satisfied.

Quality of historical results

The quality metrics for this strategy demonstrate strong historical efficiency. Out of 34 closed trades, 29 ended in profit, establishing a win rate of 85.29%. The overall profit factor stands at 5.0, indicating that total gross gains were five times greater than total gross losses. The mean trade return reached 18.43%, backed by a median trade return of 15.59%. The best individual trade delivered an 87.09% gain. Furthermore, the top three winning trades generated 27.89% of the total gross profit, pointing to a well-distributed performance profile rather than reliance on a single extreme anomaly.

Risk, drawdown and losing behavior

Risk parameters are tightly managed throughout the backtested history. The maximum recorded peak-to-trough drawdown was limited to 18.85%, matching the worst single trade loss of -18.85%. Out of 34 total trades, only 5 resulted in losses, with the longest losing streak capped at just 1 trade. In contrast, the strategy achieved a maximum consecutive winning streak of 12 trades. This strong positive payoff ratio effectively safeguards equity during unfavorable market phases.

Behavior through time and yearly stability

Granular yearly return breakdowns are not present in this specific dataset. However, evaluating the overall 5.37-year span reveals a stable profile driven by strict opportunity filtering. By avoiding over-trading during noisy or sideways market regimes in crypto assets, the strategy protects capital and maintains consistent statistical expectancy over long time horizons.

Strengths and limitations

Key strengths include an outstanding 85.29% win rate, a 5.0 profit factor, a maximum drawdown capped at 18.85%, and balanced profit distribution where the top three trades represent only 27.89% of gross gains. Primary limitations include the small historical sample size of 34 trades across 5.37 years, requiring significant discipline from the operator, and zero trades executed since mid-2024.

DevioLab analytical conclusion

Holding the rank 2 position for CFX with a DevioLab Score of 72.36, this strategy stands out for its high win rate and strict risk containment. Its ability to produce 19012.62% in total backtested return while maintaining a peak drawdown of 18.85% makes it a compelling quantitative case study. However, traders must account for its ultra-low trade frequency and acknowledge that historical backtest performance does not guarantee future results.

Data scope and methodology

This evaluation relies on simulated historical closed trade data for CFX on the 15-minute timeframe from March 29, 2021, to August 11, 2026 (5.37 years). All figures reflect simulated spot market backtesting conditions and do not represent live Binance execution, slippage, or guaranteed future trading performance.

Full strategy analysis