BMNRBUSDT
股票工具 · 通过 Binance 执行Algorithmic Strategy Analysis for BMNR · BitMine Immersion Technologies, Inc.
An in-depth quantitative research analysis of the BMNRB 0 +202088.80% 1TRAD-MEP5 trading strategy on the 15-minute chart for BMNR. Evaluating high historical win rate, rare execution rhythm, and drawdown control across 4.44 years of backtested data.
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Strategy profile
The quantitative trading system BMNRB 0 +202088.80% 1TRAD-MEP5 is designed for BMNR (BitMine Immersion Technologies, Inc.) operating on a 15-minute chart timeframe. Within the DevioLab analytical framework, this algorithm holds the top rank for this asset with a DevioLab Score of 75.91 out of 100, designating it as a core strategy model. The historical dataset covers the timeframe from March 10, 2022, to August 19, 2026, encompassing approximately 4.44 years of market history. The dataset start date indicates the beginning of our available backtest data and should not be interpreted as the asset founding or initial listing date. The strategy focuses on selective entry triggers aimed at capturing substantial price moves in the stock market.
Trading rhythm and position duration
A defining characteristic of this trading algorithm is its exceptionally low trade frequency. Over the 4.44-year historical sample, the strategy executed and completed only 13 trades, translating to an average of roughly 2.92 closed trades per year. Detailed metrics regarding average or median position holding hours, as well as exact spacing between trade exits, are not available in the dataset. However, the overall trade count confirms that this model does not engage in high-frequency trading or rapid day-trading styles. Instead, it operates with strict patience, remaining idle until rare statistical setups materialize on the 15-minute price charts of BitMine Immersion Technologies, Inc.
Quality of historical results
In terms of cumulative growth, the backtested parameters show extraordinary numbers: total historical net return reached 202088.80%, corresponding to an annualized figure of 44356.32%. Out of 13 closed positions, 12 resulted in gains, yielding a win rate of 92.31%. The average trade returned +200.57%, whereas the median trade performance was +27.44%. The large disparity between average and median returns is explained by a few massive winning trades, led by a single top trade that generated a gain of +1731.89%. Furthermore, the top 3 winning trades accounted for 92.03% of total gross profits, underscoring that performance is driven by catching occasional massive momentum events.
Risk, drawdown and losing behavior
Despite the immense cumulative upside figures, the strategy maintained remarkably tight risk parameters throughout the backtest. The maximum historical drawdown was constrained to just 4.93%. The strategy achieved a profit factor of 3.21, reflecting a healthy ratio of gross profits relative to losses. Across the entire 4.44-year dataset, the system incurred only 1 losing trade, which recorded a loss of -4.93% and serves as the single worst trade on record. The longest winning streak reached 7 consecutive trades, while the maximum losing streak was limited to 1 trade. This demonstrates an effective risk mitigation structure when market conditions turn unfavorable.
Behavior through time and yearly stability
Because the total trade volume stands at 13 closed positions over 4.44 years, granular year-by-year statistical breakdowns are limited. The average execution rate of 2.92 trades per year highlights that performance is highly concentrated around specific calendar windows when momentum is present. During prolonged periods of consolidation or adverse price movement, the algorithm suppresses trade signals entirely, effectively protecting capital at the expense of trade activity continuity.
Strengths and limitations
The primary strengths of this quantitative strategy include its high win rate of 92.31%, an exceptionally mild maximum drawdown of 4.93%, and its demonstrated ability to capture explosive price moves, as evidenced by a peak trade gain of +1731.89%. The low trade count also minimizes friction from trading fees. Conversely, the main limitation lies in the small sample size of 13 trades over 4.44 years, making statistical conclusions sensitive to individual trade outcomes. Additionally, reliance on the top 3 trades for 92.03% of gross profits means long periods of dormancy are required before major returns materialize.
DevioLab analytical conclusion
The BMNRB 0 +202088.80% 1TRAD-MEP5 algorithm represents a highly patient, momentum-oriented approach tailored to BMNR. By combining a top rank position for the ticker, a DevioLab Score of 75.91, and tight drawdown control, it serves as a compelling quantitative benchmark. Nevertheless, prospective observers must evaluate the strategy with caution, recognizing that historical returns were heavily driven by a small cluster of extraordinary winning trades.
Data scope and methodology
All statistics presented are derived from simulated backtests of historical closed trades between March 10, 2022, and August 19, 2026, using 15-minute market data for BMNR. These figures represent historical simulated performance and do not guarantee future live account performance. This analysis is prepared strictly for research and educational purposes and does not constitute financial or investment advice.