ETCUSDT
Mercado cripto · BinanceETC · ETC Strategy Analysis: Quantitative Evaluation of the Top-Ranked Core Model
This analytical report examines the historical performance structure of the top-ranked ETC strategy model on DevioLab, designated as ETC 215000 +31201.45% 1TRAD-DMU7. Operating on a 15-minute timeframe over a 6.14-year backtest horizon from June 2020 to August 2026, the strategy achieved a DevioLab score of 81.04, securing the primary rank for the ETC asset under core balanced selection criteria. Key statistical characteristics include a high win rate of 81.25% across 128 completed trades, a profit factor of 3.75, and a cumulative simulated gain of +31,201.45%. Notable structural traits include tight alignment between average (+5.06%) and median (+4.57%) trade returns, low profit concentration with the top three winning trades contributing just 10.81% of gross profit, and a peak-to-trough drawdown of 19.96% that matches its worst single trade loss. However, the strategy recorded zero completed trades in the recent observation window beginning June 1, 2024, presenting a critical temporal data limitation that requires careful interpretation.
Ler análise completa
Strategy Profile and Core Performance Metrics
The quantitative strategy model under review, identified publicly as ETC 215000 +31201.45% 1TRAD-DMU7, operates on the cryptocurrency asset ETC · ETC within crypto spot or derivative markets. Designed for a 15-minute candlestick interval, the strategy carries a DevioLab score of 81.04 and holds the number one rank among evaluated strategy configurations for this specific ticker. Selected under the core balanced methodology, the model prioritizes a high-probability payoff profile while maintaining controlled historical drawdowns. Over a total sample span of 6.14 years ranging from June 30, 2020, to August 21, 2026, the strategy generated 128 completed trade executions. Historical performance across this timeline yields a cumulative net simulated yield of +31,201.45%, translating to an annualized benchmark figure of 505.95%. Out of 128 total closed trades, 104 terminated in positive return territory while 24 resulted in realized losses. This establishes a baseline win rate of 81.25% and a overall profit factor of 3.75, signaling a substantial historical surplus of gross gains relative to gross losses.
Trading Rhythm and Position Duration Analysis
Despite operating on a low 15-minute chart resolution, the strategy demonstrates a highly selective, low-frequency execution cadence. Across the 6.14-year backtest duration, the model executed 128 total positions, which equates to an average frequency of 20.84 trades per year, or approximately 1.73 closed trades per month. This low transaction frequency highlights that the underlying logic requires strict confluence criteria before committing capital, avoiding high-turnover intraday noise. While specific sub-metrics for average holding hours, median holding hours, and days between exits are unpopulated in the provided dataset, the relationship between chart resolution (15 minutes) and trade frequency (20.84 trades annually) offers distinct structural insights. Rather than executing rapid micro-scalps, the system maintains open positions across longer multi-bar intervals. Exits occur at wide temporal intervals, averaging roughly 17 to 18 days between completed trades. This execution rhythm eliminates rapid portfolio churn, though statistical analysis of precise holding durations remains bounded by the lack of granular hourly duration fields.
Quality and Distribution of Historical Results
Evaluating the distribution of closed trade outcomes reveals exceptionally strong payoff symmetry and broad-based performance across the trade sample. The average closed trade yield stands at +5.06%, while the median trade yield is +4.57%. The close proximity between the mean and median metrics is a key quantitative finding. It demonstrates that historical profitability is driven by a consistent, repeatable expectancy core rather than being distorted by a small number of massive, unrepresentative positive outliers. Further evidence of outcome dispersion is visible in the winner concentration metric. The three largest winning trades account for only 10.81% of cumulative gross profits generated across the 104 winning positions. In many quantitative strategies, a small handful of tail-event trades contribute 30% to 50% of gross gains, creating single-point-of-failure vulnerabilities. Here, the low 10.81% concentration figure confirms that equity growth was distributed broadly across the trade history. The single best trade in the dataset yielded +36.57%, whereas the worst single trade incurred a loss of -19.96%, reflecting a wide but controlled individual trade range.
Risk, Drawdown and Losing Streak Dynamics
Risk characteristics for the strategy are defined by tight losing streak containment and a maximum drawdown profile that directly reflects individual trade risk boundaries. The maximum peak-to-trough equity drawdown observed across the 6.14-year history was 19.96%. Remarkably, this maximum drawdown percentage matches the strategy worst single historical trade loss of -19.96%. This statistical equivalence indicates that equity decline peaks were driven primarily by isolated stop-out events rather than prolonged compounding loss clusters. This drawdown behavior is reinforced by consecutive loss statistics. The longest losing streak recorded throughout the entire backtest was just 2 consecutive trades. Conversely, the strategy achieved a maximum winning streak of 13 consecutive profitable trades. Combined with an overall win rate of 81.25%, the model demonstrated strong historical resilience against extended equity drawdowns. The primary historical risk factor for capital allocation is not compounding consecutive failures, but rather managing the impact of individual outlier losses like the observed -19.96% worst-case realization.
Behavior Through Time and Yearly Stability
The backtest sample spans more than six calendar years, covering diverse crypto market macro regimes from mid-2020 through mid-2026. Over this entire multi-year window, the strategy accumulated +31,201.45% in closed trade gains. The baseline annualized performance figure of 505.95% reflects compounding efficiency across the 128 executed trades. Because the granular yearly breakdown table is unpopulated in the provided dataset, direct year-over-year comparisons of trade counts, annual win rates, and annual profit totals cannot be computed explicitly. However, taking the macro multi-year view, an average rate of 20.84 trades per year indicates that signal generation remained sparse but consistent across the broader history. The overall stability of the strategy over 6.14 years relies heavily on its high hit rate (81.25%) and robust profit factor (3.75), which allowed equity curves to expand effectively without long multi-year stagnation phases in the simulated historical data.
Strengths and Limitations
The strategy primary quantitative strengths include an outstanding win rate of 81.25%, an impressive profit factor of 3.75, and exceptional distribution of returns where the top three winning trades represent just 10.81% of gross profits. The alignment between the mean trade (+5.06%) and median trade (+4.57%) confirms baseline structural consistency. Furthermore, losing streaks were strictly capped at 2 consecutive trades, and maximum historical drawdown was contained at 19.96%. Conversely, important limitations must be acknowledged. First, the total trade sample of 128 trades over 6.14 years is relatively small, increasing statistical uncertainty relative to high-frequency models. Second, the total absence of trade executions since June 1, 2024, leaves recent model behavior unverified. Third, the maximum single-trade loss of -19.96% reveals that when negative exits occur, they can be sizeable relative to average gains. Finally, the unpopulated holding time fields prevent deep analysis into intra-trade time exposure.
DevioLab Analytical Conclusion
The ETC 215000 +31201.45% 1TRAD-DMU7 model represents a high-conviction, low-frequency quantitative design for ETC · ETC. Achieving a DevioLab score of 81.04 and ranking first for its asset, the strategy excels in win-rate reliability (81.25%) and profit factor performance (3.75). Its mathematical architecture successfully avoids over-reliance on individual jackpot trades, building equity through a steady cadence of positive expectations averaging +5.06% per trade. The strategic trade-off centers on patience versus recent activity. With an average execution rate of ~20.84 trades per year and zero trades registered since mid-2024, capital allocation under this design requires accepting long periods of signal latency. While historical backtest metrics are exceptionally robust, analysts must weigh full-history stability against the present lack of active forward trade validation.
Data Scope and Methodology
All metrics presented in this analysis are derived strictly from simulated historical backtest results recorded between June 30, 2020, and August 21, 2026, for ETC · ETC on a 15-minute timeframe. Performance statistics cover 128 closed trade records and assume full signal execution according to backtest parameters. Percentage gains and drawdown calculations describe historical closed-trade simulations and do not represent live account balances or real-time trading logs. The backtest methodology does not incorporate live operational variables such as execution slippage, variable order book spread costs, platform exchange fees, or funding rate adjustments. Historical simulated returns provide analytical context regarding strategy design and logic behavior, but offer no guarantee of future live performance. Market conditions change continuously, and historical quantitative modeling should be evaluated as analytical research rather than direct investment advice.