ETHFIUSDT
Mercado cripto · BinanceETHFI Algorithmic Strategy Analysis: High Win-Rate Dynamics and Low Winner Concentration Across 87 Completed Trades
This quantitative investigation evaluates an algorithmic trading strategy applied to ETHFI on a 15-minute execution interval across a simulated history spanning March 18, 2024, to March 21, 2026. Across 2.01 years of backtested operations, the strategy completed 87 trades, achieving an 87.36% win rate, a profit factor of 7.11, and an average trade return of 8.02%. The empirical data reveal a swing-trading operational profile with an average position holding duration of 77.94 hours and a remarkably low profit concentration, where the top three winning trades account for only 14% of total gross profit. This report explores the statistical interactions between position duration, win rate consistency, yearly performance stability, and risk characteristics recorded in the backtest dataset.
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Strategy Profile
The evaluated algorithmic strategy operates on the cryptocurrency asset ETHFI using a 15-minute candlestick interval backdrop. The historical evaluation period spans from March 18, 2024, to March 21, 2026, representing approximately 2.01 years of continuous historical backtesting. Over this multi-year data horizon, the strategy generated a sample of 87 completed trades, comprising 76 winning trades and 11 losing trades. This yields a historical win rate of 87.36%. The strategy achieved an aggregate historical profit sum of 44,220.25%, with an annualized metric of 12,229.31%, accompanied by an overall profit factor of 7.11. While the underlying chart evaluation takes place on a 15-minute resolution, the overall trade cadence remains selective, averaging 43.34 trades per year and 4.14 trades per active month. These baseline metrics establish a system designed around high-probability trade setups rather than high-frequency micro-scalping.
Trading Rhythm and Position Duration
Although the strategy processes data on a 15-minute interval, its execution rhythm reflects a medium-term swing-trading profile rather than rapid intraday turnover. The average holding time across all completed trades is 77.94 hours, equivalent to approximately 3.25 days, while the median holding time stands at 38.50 hours. This structural difference between average and median holding durations indicates that while half of the positions close within approximately 1.6 days, a subset of trades remains open significantly longer, stretching the overall arithmetic mean upward. In terms of trade frequency, the average time between position exits is 8.50 days, whereas the median time between exits is 4.69 days. The strategy completes roughly 4.14 trades per active month. Consequently, market exposure is selective and intermittent, allowing positions sufficient time to mature over multi-day holding windows rather than forcing frequent intraday transactions.
Quality of Historical Results
The historical return profile demonstrates strong payoff quality characterized by alignment between average and median trade expectations. The strategy achieved an average trade return of 8.02% and a median trade return of 8.37%. The close proximity of the median return to the mean return suggests that performance is consistently distributed across individual trade setups rather than being inflated by rare positive outliers. Furthermore, the top three winning trades generated 14% of the strategy's total gross profit. This 14% winner concentration metric is exceptionally low, confirming that historical profitability was driven by broad-based contribution across the 76 winning trades rather than reliance on a handful of oversized gains. The best individual trade generated a return of 59.15%, while the worst individual trade registered a loss of -34.56%. Combined with a profit factor of 7.11, the trade distribution highlights robust expected value per trade event.
Risk, Drawdown and Losing Behavior
In assessing historical downside risk, maximum drawdown is unrecorded (reported as null) within the supplied dataset, requiring analytical focus to center on realized trade losses and streak patterns. The maximum single trade loss observed in the 87-trade sample was -34.56%, representing a sizable downside outlier relative to the average trade return of 8.02%. However, the strategy exhibited strong streak stability, recording a longest winning streak of 25 consecutive profitable trades compared to a longest losing streak of only 2 consecutive losing trades. Out of 87 total trades, only 11 ended in a loss, translating to an overall loss frequency of 12.64%. The combination of brief losing streaks and an 87.36% win rate helped limit consecutive equity erosion events in the simulation, though the magnitude of the worst individual trade (-34.56%) emphasizes the necessity of evaluating downside risk controls at the single-trade level.
Behavior Through Time and Yearly Stability
Breaking down performance by calendar year demonstrates performance across differing market conditions between 2024 and early 2026. In 2024, the strategy executed 47 trades, securing 43 wins and 4 losses for a 91.49% win rate and a cumulative trade sum of 357.85%. In 2025, trade frequency adjusted slightly to 39 completed trades, yielding 32 wins and 7 losses, which produced an 82.05% win rate and a cumulative trade sum of 281.14%. In the initial period of 2026 covered by the dataset (up to March 21, 2026), 1 trade was executed, resulting in a single winning trade of 59.15% (a 100% win rate for that year). Comparing 2024 and 2025 reveals consistent activity levels (47 trades versus 39 trades) alongside a modest reduction in win rate (91.49% to 82.05%), yet both full calendar years delivered substantial positive cumulative returns, reflecting multi-year operational consistency.
Recent Period Since 2024-06-01 Versus Full History
Evaluating the recent performance window from June 1, 2024, to March 21, 2026, provides insight into the strategy's active operational sample. Out of the 87 total trades in the full history, 68 trades closed on or after June 1, 2024, representing 78.16% of all completed historical trades. During this recent window, the sum of closed trade returns reached 559.24%. Because nearly four-fifths of the total trade dataset occurred within this recent timeframe, the overall historical metrics (such as the 87.36% total win rate and 7.11 profit factor) are heavily weighted by performance observed since mid-2024. The substantial concentration of sample data in this recent window confirms that the system's statistical properties are strongly representative of trading behavior across recent market environments.
Strengths and Limitations
The primary statistical strengths of this strategy include a high historical win rate of 87.36%, an elevated profit factor of 7.11, and a low gross profit concentration where the top three trades account for only 14% of gross gains. Additionally, the strategy demonstrated high consistency with a 25-trade winning streak and a maximum losing streak of just 2 trades. Conversely, key limitations center on the total sample size of 87 completed trades across a 2.01-year evaluation period, which provides a moderate sample size for statistical extrapolation. Furthermore, the dataset contains an unrecorded (null) maximum drawdown metric and displays a severe worst-trade loss of -34.56%, indicating that downside volatility on failing trades can be significant despite their infrequent occurrence.
DevioLab Analytical Conclusion
The quantitative profile of this ETHFI strategy depicts an effective swing-trading model characterized by high win probability and evenly distributed trade gains. By capturing an average return of 8.02% per trade across multi-day holding periods (median 38.5 hours), the system avoids dependency on fast micro-scalping while maintaining steady trade generation (43.34 trades per year). The high win rate (87.36%) and strong profit factor (7.11) are reinforced by broad profit distribution across winning trades. However, analysts must weigh these structural strengths against the presence of a -34.56% worst trade loss and a sample size bounded at 87 closed transactions over 2.01 years of backtested history.
Data Scope and Methodology
This analysis is based strictly on historical backtested performance data generated for ETHFI on a 15-minute chart interval from March 18, 2024, through March 21, 2026. All cited metrics—including trade counts, win rates, percentage profit sums, holding durations, and profit factors—reflect simulated closed trades. Cumulative profit percentages represent the mathematical sum of individual trade performance percentages within the backtest framework and do not mirror live execution account returns, slippage, exchange fees, or funding costs. Past simulated results do not guarantee future performance in live trading environments.