SANDUSDT
Mercado cripto · BinanceSAND · SAND Quantitative Analysis: Evaluating Strategy Performance and Risk Metrics
Detailed historical performance study of the top-ranked SAND trading algorithm on the 15-minute timeframe, featuring a 77.29% win rate and a 2.50 profit factor across 251 closed positions.
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Strategy profile
The quantitative trading model SAND 215000 +286839.13% 1TRAD-HUQ3 operates on the 15-minute timeframe for the crypto asset SAND. Holding the top rank among evaluated strategies for this specific asset, it achieves a DevioLab score of 69.27. Across its documented backtest record up to August 10, 2026, the strategy generated a cumulative historical profit of 161597.61%, which corresponds to an annualized benchmark rate of 177.11%. These metrics represent historical mathematical evaluations of closed positions and illustrate how the algorithm navigated spot price movements for SAND.
Trading rhythm and position duration
The backtested dataset contains a total of 251 completed trades. Specific metrics regarding average holding hours, median holding duration, and execution frequency per active month are unrecorded in the available dataset, which prevents a detailed calculation of average trade length or intraday turnover. Operating on a 15-minute timeframe with 251 total closed trades indicates a selective execution style rather than high-frequency scalping. Quantitative analysts must account for the absence of specific entry and exit timestamps when assessing execution rhythm.
Quality of historical results
Out of 251 total closed positions, 194 ended in profit, delivering an overall win rate of 77.29%. The strategy achieved a profit factor of 2.50, demonstrating that cumulative gross gains exceeded gross losses by two and a half times. Returns across winning trades showed high consistency, with an average trade return of 3.52% and a median trade return of 3.63%. Furthermore, the top three winning trades generated only 8.35% of total gross historical profits. This low concentration ratio highlights that overall performance was driven by statistical consistency rather than isolated luck. The maximum single winning trade yielded 37.47%.
Risk, drawdown and losing behavior
The algorithm recorded 57 losing trades across the evaluation period. The largest single trade loss reached -33.94%, matching the overall peak-to-valley maximum drawdown of 33.94%. This indicates that the primary drawdown event was directly linked to a single adverse trade movement. Regarding trade sequences, the model achieved a longest winning streak of 17 consecutive trades against a maximum losing sequence of 4 trades. Managing tail risk remains critical, as the peak trade loss demonstrates the importance of strict position sizing when trading volatile digital assets like SAND.
Behavior through time and yearly stability
Year-by-year performance breakdowns and detailed annual statistics are not available in the historical dataset for this strategy profile. As a result, performance across individual bull and bear market cycles cannot be verified through yearly tables. Nevertheless, aggregate historical metrics reflect consistent execution, backed by a 77.29% win rate and low reliance on extreme outlier trades. Readers should consider the missing annual segmentation alongside broader crypto market dynamics during evaluation.
Strengths and limitations
The primary strength of this strategy is its strong win rate of 77.29% combined with a profit factor of 2.50. The minimal profit concentration, with the top three trades contributing under 8.35% of gross gains, underscores broad distribution of returns. Key limitations include the peak trade loss of -33.94%, the lack of holding time logs, and zero completed trades in the recent tracking period since mid-2024.
DevioLab analytical conclusion
Ranking first for SAND with a DevioLab score of 69.27, this strategy offers a compelling quantitative benchmark. Its high win rate and well-distributed returns demonstrate a disciplined approach to the 15-minute timeframe. However, prospective users must weigh these historical results against the peak single-trade drawdown of 33.94% and recent execution inactivity. This analysis is prepared for spot market research and does not constitute financial advice.
Data scope and methodology
All statistics reported in this study are derived from backtested closed trade logs ending August 10, 2026. Percentage metrics reflect historical closed position performance and do not represent exact live account balances, real-time slippage, or exchange fees on Binance. Past historical performance is provided for educational research and is not a guarantee of future results.