ARBUSDT
Crypto market · BinanceQuantitative Performance Analysis: ARB · ARB 15-Minute Strategy Shows High Hit-Rate Consistency and Distributed Profit Structure
This analytical study evaluates an algorithmic trading model operating on the 15-minute timeframe for ARB (ARB) across a 2.63-year backtested historical period from March 23, 2023, through November 7, 2025. Generating 235 completed trades, the strategy produced a win rate of 79.15% and a profit factor of 4.10. A defining characteristic of the model's return profile is its exceptional distribution of gains: the top three winning trades account for only 7.44% of gross profit, demonstrating that historical accumulation was driven by repeatable, modest gains rather than isolated statistical outliers. Position durations reflect a swing-style profile on an intraday timeframe, averaging 26.04 hours per trade with a median holding time of 15.00 hours. While annual win rates experienced a modest contraction from 88.41% in 2023 to 72.92% in 2025, trade frequency expanded significantly in subsequent years. The model maintained strong statistical continuity in the recent observation window from June 1, 2024, onward, generating 104 completed trades with a cumulative sum of closed trade returns reaching 278.54%. This article provides a comprehensive statistical dissection of the strategy's risk, trade distribution, holding dynamics, and temporal durability.
Analysis as of 24.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative strategy under analysis targets the cryptocurrency asset ARB using a 15-minute execution interval. Over a historical evaluation period spanning 2.63 years—from March 23, 2023, to November 7, 2025—the strategy recorded a total of 235 completed trades. Among these closed events, 186 were profitable while 49 closed in loss, resulting in an overall win rate of 79.15%. The cumulative performance metrics reflect a compounded all-history profit metric of 18,772.73% alongside an annualized backtested figure of 982.54%. Within the DevioLab framework, this model carries a non-core classification (is_core: false), with proprietary scoring and asset rank parameters unset (null). The dataset provides a baseline for examining a high-probability model operating on short-timeframe pricing data without relying on rapid-fire scalping execution.
Trading rhythm and position duration
Despite utilizing a 15-minute candlestick interval for signal generation, the strategy demonstrates a holding profile characteristic of short-term swing trading rather than ultra-high-frequency or momentum scalping. The average position holding duration stands at 26.04 hours, while the median holding duration is 15.00 hours. This right-skewed distribution in holding duration indicates that while half of all trades resolve within 15 hours, a subset of positions remains open across multi-day horizons to accommodate ongoing trends or delayed exit triggers. The overall operational cadence is relatively measured relative to the underlying 15-minute chart resolution. The strategy generates an average of 89.35 trades per year, translating to approximately 7.12 trades per active month. Spacing between execution events further highlights this pacing: the average time between trade exits is 4.10 days, while the median interval between exits is 2.02 days. The divergence between the median exit interval (2.02 days) and the median holding duration (0.625 days, or 15 hours) suggests that the strategy enters markets in distinct clusters or punctuated opportunities rather than maintaining continuous market exposure. Traders examining this model observe a rhythm defined by periodic active trading phases followed by multi-day inactive observation periods.
Quality of historical results
The statistical distribution of trade returns demonstrates remarkable payoff balance and consistency. The strategy achieves a overall profit factor of 4.10, indicating that total gross winning trade percentages substantially exceeded total gross losing trade percentages. The average trade return across all 235 completed trades is 2.59%, which aligns closely with the median trade return of 2.55%. This near-identity between mean and median expectations confirms that the model's historical returns are not distorted by a small cluster of massive windfalls. Further evidence of robust profit distribution appears in the concentration metrics: the strategy's top three winning trades account for just 7.44% of total gross profits. In many quantitative strategies, a substantial majority of net gains depends on two or three extreme tail-risk events; here, 92.56% of gross profit was generated across the remaining 183 winning trades. The absolute best single trade produced a return of 21.58%, whereas the worst single trade incurred a loss of -14.64%. The single-trade payoff ratio between maximum gain and maximum loss is roughly 1.47:1. When combined with a 79.15% win rate, this non-symmetric win/loss profile underscores a strategy whose primary engine of performance is high statistical hit frequency rather than extreme reward-to-risk asymmetry on individual positions.
Risk, drawdown and losing behavior
Evaluating the structural risk profile requires looking closely at losing sequences and severe adverse trades. Within the provided empirical record, the formal maximum drawdown percentage metric is unrecorded (null). However, risk dynamics can be inferred effectively through streak characteristics and trade-level loss limits. The strategy's longest winning streak reached an exceptional 31 consecutive profitable trades, whereas its longest losing streak was capped at just 3 consecutive losing trades. This low maximum consecutive loss count demonstrates strong statistical resistance to multi-trade loss compounding during historical market regimes. Nonetheless, the presence of a worst trade loss of -14.64% highlights the magnitude of single-trade adverse exposure compared to the average winning trade return (~2.59%). Because the worst loss is nearly 5.7 times larger than the average trade gain, a sudden clustering of losses—even if historically rare—can temporarily erode accumulated equity. The contrast between a maximum losing streak of 3 trades and a worst trade of -14.64% indicates that risk in this strategy manifests primarily through individual tail events during high-volatility gaps or sharp trend reversals rather than through prolonged degradation over extended losing streaks.
Behavior through time and yearly stability
An analysis of the yearly breakdown reveals how the strategy's performance evolved across changing market conditions in the cryptocurrency asset ARB from 2023 through 2025. In 2023 (covering March 23 through December 31), the strategy completed 69 trades with 61 wins and 8 losses, establishing a peak annual win rate of 88.41% and a cumulative summed return of 191.56%. During this initial period, signal selectivity was highest, yielding roughly 7.4 trades per month. In 2024, trade activity increased substantially to 118 completed trades (90 wins and 28 losses). While the sum of trade returns expanded to 308.71%, the annual win rate moderated to 76.27%. This trade-off between higher trade frequency and a lower hit rate suggests that market volatility or structural shifts in ARB in 2024 generated more frequent entry signals that carried slightly lower individual win probabilities. In 2025 (data recorded through November 7), the strategy completed 48 trades with 35 wins and 13 losses, producing a win rate of 72.92% and a cumulative summed return of 107.39%. Across all three calendar periods, the strategy consistently maintained a positive return contribution, though the gradual moderation in win rate points to evolving market efficiency or changing volatility regimes in ARB.
Recent period since 2024-06-01 versus full history
Examining performance since June 1, 2024, provides a window into the strategy's recent operational validity. In this sub-period, the model executed 104 trades, accounting for 44.26% of all completed historical trades across its 2.63-year lifetime. The sum of closed trade returns during this recent period reached 278.54%, representing a major portion of the overall 2024-2025 cumulative returns. Comparing the recent window to the full history demonstrates persistent operational activity. The average trade frequency during this recent 17-month window remained aligned with the broader 2024 execution pace. By producing 104 completed trades with strong positive cumulative returns after June 2024, the strategy confirms that its edge was not merely an artifact of early 2023 market conditions. The high density of recent data points provides a statistically meaningful sample for assessing ongoing market alignment, showing that the strategy's signal mechanism continued to generate positive net trade sums in recent market environments.
Strengths and limitations
The primary analytical strength of this strategy lies in its outstanding profit distribution and statistical consistency. With a 79.15% overall win rate, a 4.10 profit factor, and a top-three winner concentration of only 7.44%, performance is broadly distributed across many trades rather than reliant on rare, unrepeatable price spikes. The close alignment between average trade percentage (2.59%) and median trade percentage (2.55%) confirms stable trade outcomes. Furthermore, a short historical losing streak capped at 3 consecutive trades mitigates extended multi-trade equity drawdowns. Conversely, several material limitations must be noted. First, the maximum drawdown percentage metric is absent in the source data (null), preventing direct quantification of historical peak-to-trough equity curve declines. Second, the strategy exhibits an asymmetric trade loss profile: the worst historical loss (-14.64%) is substantially larger than the median win (~2.55%), meaning that isolated tail-risk losses can swallow several average wins simultaneously. Third, the annual win rate shows a clear downward trajectory from 88.41% in 2023 to 72.92% in 2025, signaling that market adaptation or shifting ARB market dynamics may be gradually compressing the strategy's historical edge.
DevioLab analytical conclusion
The quantitative evaluation of the ARB 15-minute strategy reveals a robust, high-probability trading model characterized by excellent profit dispersion and controlled exposure durations. By capturing modest individual price movements averaging 2.59% over 15 to 26 hours, the strategy avoids both the high transaction drag of ultra-short scalping and the prolonged market exposure of long-term trend following. Its historical profit factor of 4.10 and modest winner concentration metric (7.44% across top 3 trades) demonstrate structural health across its 235-trade sample. Institutional observers and quantitative researchers analyzing this profile should weigh the impressive win rate and recent performance stability against the observed annual win-rate decay and the severe magnitude of worst-case trade outliers (-14.64%). Because the strategy relies on high win frequency to overcome asymmetric single-trade losses, maintaining strict execution discipline and monitoring ongoing win-rate stability remain critical considerations for ongoing research.
Data scope and methodology
This analysis is based entirely on historical backtested signal data for the ARB cryptocurrency asset on a 15-minute time interval between March 23, 2023, and November 7, 2025. All reported statistics—including win rates, trade counts, holding durations, and return sums—are calculated strictly from completed (closed) trade records within the simulated dataset. Trade percentages reflect raw closed-trade performance and do not reflect real-time order execution factors such as exchange fee structures, variable bid-ask spreads, order book slippage, funding rates, or account-level leverage. The historical results analyzed herein are strictly retrospective research outputs and do not represent guaranteed future performance or direct financial advice.