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Mercado cripto · BinanceENA · ENA 15-Minute Quantitative Strategy Evaluation: Statistical Architecture of a Top-Ranked Core System
This analytical report evaluates the top-ranked quantitative trading strategy for ENA · ENA on the 15-minute timeframe. Across a 2.40-year backtested historical period comprising 317 completed trades, the strategy demonstrates exceptional performance characteristics marked by a profit factor of 6.11, an overall win rate of 73.5%, and an uncompounded cumulative trade return sum of +87,652.98%. Notably, the system exhibits broad-based profitability rather than dependence on windfall outliers, as its top three winning trades account for just 6.27% of total gross profits. However, the strategy operates alongside noticeable structural tail risk, reflected in a maximum historical drawdown of 35.33% and a worst single trade loss of -26.02%. This study dissects the system's position duration, trade distribution, risk parameters, and stability over time to provide a comprehensive evaluation of its quantitative framework.
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Strategy profile
The evaluated algorithm, designated as ENA 215000 +87652.98% 1TRAD-LTS7, represents the primary core strategy for ENA · ENA on DevioLab.com, achieving the number one rank for this ticker with a composite DevioLab score of 78.87. Operating on a 15-minute chart resolution, the backtest spans a total duration of 2.40 years, from April 2, 2024, to August 28, 2026. Over this historical horizon, the model executed 317 completed trades, capturing 233 winning outcomes against 84 losing trades. The strategy achieved an aggregate closed trade profit sum of +87,652.98%, translating to an annualized nominal baseline return of 29,198.74%. Classified as a primary core implementation, the algorithm provides a substantial sample size to observe how short-term execution intervals translate into multi-day holding patterns within high-volatility cryptocurrency markets.
Trading rhythm and position duration
Despite utilizing a granular 15-minute candlestick chart for trade identification and execution timing, the strategy demonstrates a position holding profile characteristic of swing or short-term trend-following systems rather than ultra-high-frequency scalping. The mean position holding time stands at 34.80 hours, whereas the median holding duration is significantly shorter at 14.50 hours. This disparity between mean and median holding times indicates a right-skewed distribution where the majority of trades are concluded within less than a day, while a subset of winning trends is allowed to run over several days. The cadence of trade exits reveals an average interval of 2.78 days between completed trades, accompanied by a median exit interval of 1.71 days. On an annualized basis, the model generates 131.86 completed trades per year, which translates to approximately 12.19 completed trades per active trading month. This rhythm reflects a disciplined selective mechanism that avoids excessive trading activity while systematically engaging significant price developments.
Quality of historical results
The statistical distribution of trade returns demonstrates superior structural efficiency. The strategy achieves a high hit rate of 73.50%, paired with an average closed trade return of +2.55%. Notably, the median trade outcome (+3.35%) exceeds the average trade outcome (+2.55%), confirming that positive performance is driven by a strong, consistent core of positive trades rather than being pulled upward by rare, disproportionate outliers. This finding is further reinforced by the concentration metric: the top three winning trades contributed only 6.27% of total gross profit, proving that the overall profit factor of 6.11 is derived from a broad foundation of repeated successes across hundreds of trades. The best individual historical trade achieved a gain of +37.55%, demonstrating the strategy's capacity to capitalize on extended directional moves when market volatility aligns with its position parameters.
Risk, drawdown and losing behavior
While the strategy displays exceptional gross efficiency, its risk metrics reveal meaningful exposure to downside volatility. The maximum backtested drawdown reached 35.33%, reflecting periods where equity experienced extended contractions before reaching new cumulative highs. This drawdown profile is directly linked to the loss distribution: the worst single trade incurred a loss of -26.02%, which stands as a significant outlier compared to the strategy's average trade expectation. However, losing events remain tightly clustered in sequence. The longest consecutive losing streak was restricted to 5 trades, whereas the longest consecutive winning streak reached 25 trades. The strong profit factor of 6.11 indicates that despite occasional deep single-trade drawdowns, the high win frequency and robust trade payoff structure allow the strategy to systematically recover equity over multi-trade horizons.
Behavior through time and yearly stability
An examination of the annual breakdown illustrates performance dynamics across evolving market regimes. In 2024, the strategy executed 133 trades with a win rate of 80.45% (107 wins, 26 losses), generating a cumulative trade sum of +429.36%. In 2025, trade frequency increased to 171 completed trades, though the win rate adjusted to 67.25% (115 wins, 56 losses), resulting in a closed trade return sum of +331.44%. The partial year of 2026 recorded 13 completed trades through August, registering 11 wins and 2 losses for a win rate of 84.62% and a trade return sum of +48.41%. The elevated win rate in 2024 and 2026 combined with steady activity in 2025 highlights sustained strategy engagement, though the moderate win rate drop in 2025 underscores how shifting market conditions can expand the proportion of losing trades within specific calendar years.
Recent period since 2024-06-01 versus full history
Evaluating performance strictly within the window from June 1, 2024, to August 28, 2026, reveals that the vast majority of historical trade data belongs to this recent period. Out of 317 total completed trades across the entire history, 288 trades (90.85%) were closed after June 1, 2024. During this timeframe, the cumulative sum of closed trade gains reached +671.32%. Because the post-June 2024 dataset comprises nearly the entire strategy lifecycle, the overall historical metrics—including the 73.50% win rate and 6.11 profit factor—largely reflect recent market dynamics. The minimal divergence between full-history data and recent-period data confirms that the strategy's statistical footprint is not an artifact of stale historical regimes, but rather a direct reflection of current volatility patterns.
Strengths and limitations
The primary strength of the ENA 215000 +87652.98% 1TRAD-LTS7 strategy lies in its outstanding profit factor of 6.11 and exceptional payoff distribution. Profitability is remarkably well-distributed across trades, evidenced by the top three winning trades making up only 6.27% of total gross gains. Additionally, a 73.50% win rate paired with a median trade return of +3.35% demonstrates strong trade selection mechanics. Conversely, the system's principal limitation is its severe downside tail risk, exemplified by a single worst trade of -26.02% and a peak drawdown of 35.33%. These drawdown characteristics require substantial risk tolerance and highlight that high cumulative gain figures can involve major unrealized equity swings during adverse market phases.
DevioLab analytical conclusion
Achieving a DevioLab score of 78.87 and holding the rank of number one core strategy for ENA · ENA, this 15-minute quantitative model presents a statistically compelling trade model. Its high win rate, exceptional profit factor, and highly decentralized profit structure establish it as an elite baseline system for this asset class. However, quantitative analysts must distinguish between raw backtested mathematical capability and practical capital management. The strategy's history of a 35.33% drawdown and a -26.02% worst trade demonstrates that high historical profitability coexists with significant price exposure. When evaluated strictly as historical quantitative research, the model demonstrates robust efficiency across 317 closed trades.
Data scope and methodology
The figures analyzed in this report are derived entirely from simulated backtested closed trade statistics for the asset ENA · ENA on the 15-minute timeframe over the period from April 2, 2024, to August 28, 2026. All trade metrics represent uncompounded percentage sums and mathematically calculated distributions based on raw transaction logs. The research data does not account for real-time order execution constraints, exchange order book slippage, variable funding rates, or transaction fee structures. This evaluation is provided strictly for quantitative research and educational purposes and does not constitute investment advice or financial planning guarantees.