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Marché crypto · BinanceETC · ETC Strategy Analysis: High Win Rate, Balanced Profit Distribution, and Multi-Year Consistency
This quantitative evaluation analyzes a historical backtest of the ETC · ETC trading strategy executed on a 15-minute bar interval across a 5.27-year observation window from July 2020 through October 2025. Generating 128 total trades, the strategy demonstrates an extraordinary 81.25% win rate and a profit factor of 6.33, accumulating a total closed-trade profit sum of 31,201.45%. Rather than relying on high-frequency turnover, the model behaves as a selective swing strategy with an average holding period of 71.79 hours and a moderate execution frequency of 24.30 trades per year. A defining statistical feature is its exceptionally low profit concentration: the top three winning trades account for only 10.81% of gross profits, indicating that structural performance is broadly distributed across numerous trades. Performance stability across individual calendar years remains remarkably consistent, backed by a recent post-June 2024 sample of 26 trades delivering 198.00% in cumulative returns.
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Strategy profile
The strategy evaluated under the name ETC 215000 +31201.45% 1TRAD-FMG5 operates on the 15-minute timeframe for the ETC · ETC asset in the cryptocurrency market. Over an extensive evaluation period spanning 5.27 years, from July 9, 2020, through October 14, 2025, the historical backtest recorded exactly 128 completed trades. The aggregate performance across this multi-year sample demonstrates a cumulative closed-trade sum of 31,201.45%, corresponding to an annualized metric of 505.95% under standard uncompounded analytical parameters. Despite using a low-granularity 15-minute price chart, the strategy is defined by high selectivity rather than dense intraday activity. Out of 128 total closed positions, 104 resulted in positive returns while 24 yielded losses, establishing an overall win rate of 81.25%. The resulting profit factor stands at 6.33, reflecting a substantial structural advantage in historical gross profits relative to gross losses. While the dataset does not provide DevioLab score or ranking metrics, the raw trade log offers a complete foundation to assess the model's structural mechanics, risk exposure, and payoff profile.
Trading rhythm and position duration
A critical distinction in evaluating quantitative strategies is differentiating the chart execution bar from the structural position holding duration. Although this strategy evaluates price action using 15-minute candles, its trading rhythm reflects a patient swing-trading framework. The historical trade volume averages 24.30 trades per year, which translates to approximately 2.91 trades per active month. Across the 5.27-year backtest, the model went extended periods without opening new positions, preferring to wait for specific market configurations. The time duration of active trades further confirms this swing-trading profile. The average holding time for a position is 71.79 hours (approximately 3.0 days), while the median holding time is 35.25 hours (about 1.47 days). The gap between the median and average holding times demonstrates that while many trades complete within one to two days, select positions extend into multi-day trends. Furthermore, the strategy averages 13.54 days between position exits, with a median exit interval of 7.98 days. This spacing confirms that the strategy avoids rapid intraday noise and focuses on capturing intermediate price swings.
Quality of historical results
The statistical quality of this strategy's historical returns is characterized by consistent positive expectancy and broad-based profit generation. The strategy achieves an average trade return of +5.06% and a median trade return of +4.57%. The close proximity between the average and median trade outcomes reveals a symmetric payoff curve across trades, free from distortion by singular anomalous outliers. This broad distribution of performance is reinforced by analyzing winner concentration metrics. The three best winning trades historically generated just 10.81% of the strategy's total gross profit. In many trend-following or high-payoff models, a handful of extreme winners account for 30% to 50% or more of total returns. Here, the low concentration ratio confirms that the strategy's high profit factor of 6.33 is driven by systemic repeatability across its 104 winning trades rather than reliance on rare, unpredictable price surges. The best individual trade generated a gain of +36.57%, illustrating solid upside capture without overshadowing the baseline performance.
Risk, drawdown and losing behavior
While the strategy boasts an impressive 81.25% win rate, analyzing its adverse trade outcomes reveals key operational risk characteristics. The worst individual closed trade in the history of the strategy resulted in a loss of -19.96%. Comparing this single maximum loss to the average winning trade of +5.06% reveals an asymmetrical trade-off: individual losses can significantly exceed the average positive payout. The high profit factor is maintained primarily through high win probability rather than an expansive reward-to-risk ratio per position. In terms of consecutive losing performance, the strategy demonstrates remarkable resilience. Its historical maximum losing streak is capped at just 2 consecutive trades, contrasted against a maximum winning streak of 13 consecutive trades. This brief duration of losing runs helped prevent severe equity erosion at the trade level. Maximum drawdown data is not available in the provided statistical sample, meaning equity peak-to-trough trajectory must be interpreted strictly through closed trade losses rather than continuous unrealized mark-to-market fluctuations.
Behavior through time and yearly stability
An examination of the yearly performance breakdown shows steady consistency across changing market environments. In 2021, the strategy recorded its highest annual activity, completing 42 trades with an 80.95% win rate and a cumulative return sum of +226.30%. During the challenging bear market of 2022, activity adjusted slightly to 36 trades while maintaining a strong 75.00% win rate and generating +115.21% in cumulative closed profits. In 2023, trade frequency compressed noticeably to 12 trades, but efficiency remained high with an 83.33% win rate and a sum of +68.34%. Performance re-accelerated in 2024 with 26 trades, an 84.62% win rate, and +140.21% accumulated profits. In the final partial sample of 2025, 12 trades yielded 11 wins (91.67% win rate) and +97.38%. Across all calendar years evaluated, the strategy consistently maintained win rates above 75.00% and positive net trade sums, demonstrating robust temporal stability without multi-year structural decay.
Recent period since 2024-06-01 versus full history
Evaluating recent strategy behavior since June 1, 2024, provides a key checkpoint to confirm whether historical performance parameters remain valid in recent market conditions. In this recent window, the strategy completed 26 trades, representing roughly 20.3% of its total historical trade volume. These trades generated a cumulative return sum of +198.00%, reflecting an average return of approximately +7.62% per trade during this specific timeframe. Comparing the recent window to the multi-year history indicates that the strategy's operational edge has remained fully intact. The recent trade frequency aligns closely with the overall historical average of 24.30 trades per year. Furthermore, the return generation per trade in the recent period slightly exceeds the full-history baseline average of +5.06%, proving that the system did not suffer from performance compression or regime failure in modern market environments.
Strengths and limitations
The primary structural strength of this strategy lies in its high win rate of 81.25% combined with an exceptional profit factor of 6.33. Because the top three winning trades contribute only 10.81% of total gross profit, the system is structurally resilient and does not depend on unrepeatable extreme events. Its short losing streaks (maximum 2 trades) and consistent multi-year stability across both bull and bear regimes further reinforce its quantitative viability. Conversely, material limitations must be acknowledged. The primary risk factor is the downside asymmetry on individual trades, as evidenced by a worst trade loss of -19.96%, which is nearly four times larger than the average trade gain of +5.06%. Additionally, with only 128 total trades over 5.27 years, the overall sample size is relatively small, yielding approximately 24 trades per year. Traders evaluating this model must tolerate long periods of inactivity between trade signals and accept that unrecorded real-time mark-to-market drawdowns could exceed trade-level closed metrics.
DevioLab analytical conclusion
The quantitative profile of the ETC · ETC 15-minute strategy reveals a highly disciplined, low-frequency swing-trading mechanism operating effectively within a granular price chart framework. By combining patience with highly selective entry criteria, the strategy achieves a rare balance of high win frequency (81.25%) and exceptionally low profit concentration (10.81% top-3 profit share). The continuous profitability observed across every individual calendar year from 2021 through 2025 highlights strong adaptability across structural crypto cycles. Prospective implementation of such a strategy requires rigorous attention to risk control and trade execution. Because individual trade losses can reach -19.96%, effective position sizing and risk management are paramount to ensure that infrequent losing trades do not compromise overall portfolio stability. Ultimately, historical evidence demonstrates a robust quantitative model with durable performance characteristics across both long-term and recent test windows.
Data scope and methodology
This statistical analysis is derived strictly from historical simulated backtest records for the ETC · ETC asset across the 15-minute time interval covering July 9, 2020, to October 14, 2025. All performance metrics, including trade counts, win rates, holding times, profit factors, and annual sum statistics, represent closed simulated trade results. Metrics are calculated on a non-compounded, trade-sum basis and do not reflect continuous compounding, real-time portfolio rebalancing, or active leverage management. The history start date reflects the beginning of the historical evaluation dataset and does not denote the underlying asset's market creation or listing date. These results are presented strictly for historical strategy research and quantitative evaluation purposes. They do not account for live market execution factors such as order book slippage, variable exchange fee structures, network latency, or liquidity constraints, and must not be interpreted as financial advice or guarantees of future returns.