ETHFIUSDT
Crypto market · BinanceETHFI Swing Trading Strategy Analysis: High Win-Rate Selectivity and Multi-Day Holding on 15-Minute Data
This analytical evaluation examines a quantitative swing trading model applied to ETHFI on a 15-minute execution interval across a historical sample period from March 2024 through August 2026. Over 86 completed trades spanning approximately 2.42 years, the strategy demonstrates a high win rate of 76.74 percent, a profit factor of 4.82, and an average return per trade of +7.41 percent. Despite operating on a granular 15-minute execution timeframe, the strategy exhibits low trading frequency, averaging 35.48 trades per year and holding positions for a median duration of 76.25 hours. The top three winning trades account for 21.49 percent of total gross profits, pointing to a balanced distribution of returns across its trade set. Recent trade performance since June 1, 2024 encompasses 76 trades with a cumulative trade return sum of +559.85 percent. This report details the statistical structure, rhythm, risk dynamics, and temporal distribution of the strategy's backtested history.
Analysis as of 28.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The strategy under evaluation operates on ETHFI in the cryptocurrency market using a 15-minute chart interval. The underlying dataset spans from March 18, 2024 to August 20, 2026, representing a testing history of 2.42 years. Across this window, the model completed 86 trades, achieving 66 winning trades and 20 losing trades, which yields a overall win rate of 76.74 percent. The profit factor stands at 4.82, reflecting a strong ratio of cumulative gross profits to gross losses. The overall cumulative profit metric across all historical trades reaches +19,165.58 percent, with a calculated annualized figure of 10,369.79 percent. It is important to contextualize these performance figures: the underlying model operates with selective trade generation despite using a fine 15-minute price series. The dataset contains unranked strategy identification without assigned DevioLab scoring parameters. Overall, the foundational statistical metrics indicate a system calibrated toward high trade accuracy, moderate trade frequency, and positive mathematical expectancy across its historical execution history.
Trading rhythm and position duration
While the strategy executes on a 15-minute bar interval, its temporal dynamics align strictly with multi-day swing trading rather than short-term scalp execution. The model executed 86 trades across 2.42 years of data, translating to an annual trading frequency of 35.48 trades per year, or approximately 3.44 trades per active month. Position durations show that trades remain open for extended periods: the average holding time is 106.93 hours (approximately 4.45 days), while the median holding duration is 76.25 hours (roughly 3.18 days). The divergence between average and median holding times indicates that while most positions exit within three to four days, occasional trades persist significantly longer to capture sustained price moves. Furthermore, trade exits occur infrequently, averaging 10.39 days between completed trades with a median interval of 7.06 days between exits. This demonstrates that the algorithm filters out short-term 15-minute price volatility, entering positions selectively and retaining exposure over multi-day horizons to allow trend development.
Quality of historical results
The mathematical quality of the historical results is characterized by a close alignment between average and median expectations alongside balanced profit distribution. The average trade return across all 86 completed positions is +7.41 percent, which aligns remarkably closely with the median trade return of +7.32 percent. This tight symmetry between mean and median figures suggests that overall strategy profitability is not artificially skewed by a tiny sample of extreme outliers, but is instead supported by recurring, stable return capture. The best individual trade generated a return of +76.93 percent, while the worst single trade incurred a loss of -27.45 percent. An evaluation of profit concentration reveals that the top three winning trades generated 21.49 percent of the aggregate gross profit. This concentration metric indicates that while the largest winning trades provided meaningful performance contributions, remaining profit was broadly generated across the remaining 63 winning positions. A high win rate of 76.74 percent paired with a profit factor of 4.82 underscores positive trade distribution mechanics.
Risk, drawdown and losing behavior
Risk dynamics within the backtest exhibit high win consistency coupled with constrained losing sequences, alongside notable individual trade loss potential. The strategy experienced a maximum consecutive losing streak of only 2 trades, contrasted with a peak winning streak of 9 consecutive trades. This low consecutive loss cluster helped stabilize equity progression throughout the backtested period. However, downside risk on individual trade levels remains tangible, as evidenced by the worst trade loss of -27.45 percent. The maximum drawdown metric is unrecorded in the provided statistical dataset, meaning full peak-to-trough equity curves must be interpreted primarily through closed-trade loss magnitudes and streak characteristics. The asymmetry between the win rate of 76.74 percent and the worst single trade loss highlights the structural requirement for high accuracy: because single losses can reach double-digit percentages, maintaining a high hit rate and solid profit factor (4.82) is essential to preserving net strategy gains over time.
Behavior through time and yearly stability
Analyzing the yearly breakdown demonstrates consistent positive statistical output across each calendar period present in the 2.42-year dataset. In 2024, the strategy logged 34 completed trades, securing 25 wins and 9 losses (73.53 percent win rate) for a summed trade return total of +199.14 percent. In 2025, trade activity increased to 45 completed trades with 35 wins and 10 losses, elevating the win rate to 77.78 percent and yielding a summed trade return total of +313.41 percent. The partial data window for 2026 includes 7 completed trades, yielding 6 wins and 1 loss (85.71 percent win rate) and adding +124.87 percent in summed returns. Across all three periods, trade frequency remained disciplined while win rates consistently ranged between 73.53 percent and 85.71 percent. The steady year-over-year accumulation of positive returns indicates that historical profitability was distributed across multiple calendar phases rather than concentrated within a single isolated market movement.
Recent period since 2024-06-01 versus full history
Evaluating performance since June 1, 2024 provides insight into the strategy's operational profile across recent market conditions. Out of 86 total historical trades, 76 completed positions closed after June 1, 2024. This recent sample generated a cumulative sum of trade returns equal to +559.85 percent. Because 88.37 percent of all historical trade exits occurred within this recent window, the overall dataset is heavily weighted toward post-June 2024 price dynamics. Comparing recent activity to the full history demonstrates consistent execution behavior: the strategy maintained its selective cadence of roughly 3.44 trades per month, accumulating substantial positive trade returns during this timeframe. The high sample density in the recent period confirms that the primary backtest results reflect recent asset behavior rather than relying heavily on older, non-replicable market states from early 2024.
Strengths and limitations
The primary statistical strength of this quantitative model lies in its combination of a 76.74 percent win rate, a 4.82 profit factor, and strong symmetry between mean (+7.41 percent) and median (+7.32 percent) trade outcomes. The moderate concentration metric, where the top three trades account for 21.49 percent of gross profits, indicates resilient return distribution across many winning trades. Additionally, the short maximum losing streak of 2 trades minimizes severe multi-trade drawdowns. Conversely, notable limitations exist. The worst single trade loss of -27.45 percent demonstrates that individual losing positions can be sizable, requiring the high win rate to be maintained to protect historical returns. Furthermore, while executing on a 15-minute timeframe, the strategy averages only 35.48 trades per year, resulting in a relatively modest sample size of 86 total trades over 2.42 years. Finally, maximum drawdown percentage statistics are unrecorded in this dataset.
DevioLab analytical conclusion
The historical data for this ETHFI 15-minute strategy reveals a selective swing trading model designed to capture multi-day price moves while filtering out lower-tf noise. By combining a median holding duration of 76.25 hours with an average frequency of 3.44 trades per active month, the strategy avoids excessive turnover while maintaining robust statistical metrics across its 86 completed trades. The historical profit factor of 4.82 and win rate of 76.74 percent demonstrate high historical trade efficiency. Analytical observations confirm that trade outcomes are structural rather than outlier-dependent, supported by close agreement between mean and median trade returns. However, prospective evaluators must weigh these positive attributes against single-trade downside exposure (-27.45 percent worst trade) and the total sample size of 86 trades when evaluating the system's structural profile.
Data scope and methodology
This analysis is derived exclusively from historical backtested trade records generated for ETHFI on a 15-minute time interval between March 18, 2024 and August 20, 2026. All reported metrics, including profit factors, trade durations, win rates, yearly breakdowns, and streak lengths, reflect simulated closed trade performance within this specific window. The data assumes fixed execution logic without modeling real-world order book slippage, exchange fee structures, variable liquidity constraints, or live execution latency. Percentage values describe mathematical trade performance outputs within the simulated test environment and do not represent actual live trading account returns. Historical backtest results serve solely as quantitative research tools and do not constitute financial advice or guarantee future performance outcomes.