ASTSBUSDT
Instrumento de ações · execução via BinanceASTS · AST SpaceMobile, Inc. Quantitative Analysis: High-Win-Rate Selectivity and Statistical Profile of the 15-Minute Model
An in-depth quantitative examination of the top-ranked strategy for AST SpaceMobile, Inc. (ASTS) reveals a highly selective trading profile characterized by an 81.61% win rate and a cumulative historical return of +218,645.34% across a 5.22-year evaluation window. Operating on a 15-minute price series, the model registered 87 completed trades between June 2021 and August 2026, achieving a profit factor of 3.89. The strategy exhibits favorable trade payoff distribution, with an average trade of +11.16% and a median trade of +9.15%, while the top three winning trades account for 23.37% of gross profit. However, analytical scrutiny highlights two key structural features: a historical maximum drawdown of 36.39% with a worst trade of -31.12%, and complete trade dormancy in the recent evaluation window since June 1, 2024, where zero trades were recorded. This article provides a comprehensive statistical decomposition of the model's return distribution, risk metrics, and structural trade frequency.
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Strategy profile
The quantitative model designated as ASTSB 0 +218645.34% 1TRAD-YTR7 represents the primary core strategy for AST SpaceMobile, Inc. (ASTS) within the DevioLab database, holding the rank of 1 for this asset with a DevioLab score of 77.03. Evaluated across a dataset spanning 5.22 years from June 1, 2021, to August 21, 2026, the strategy processed 15-minute price data to generate a total of 87 completed trades. Over this sample, the model achieved a total cumulative historical return of +218,645.34%, translating to an annualized return metric of 22,829.97% under compounding assumptions. The model recorded 71 winning trades against 16 losing trades, establishing a baseline win rate of 81.61%. The purpose of this analysis is to dissect the underlying statistical mechanics of these historical results, examining trade frequency, win-loss distribution, tail risk, and structural dormancy in recent periods without making assumptions about unsupplied technical indicators or execution parameters.
Trading rhythm and position duration
Despite operating on a 15-minute timeframe, the strategy exhibits an exceptionally low trade frequency, completing 87 trades over 5.22 years of history. This corresponds to an average annual trading frequency of 16.66 trades per year. The metric indicates that the algorithm operates as a highly selective systematic model rather than an intraday high-frequency or scalping strategy. Specific metrics for average holding duration in hours, median holding hours, and days between exits are not provided in the source statistics. However, the macro trading pace of approximately 1.38 trades per month demonstrates that signal generation occurs sparingly. The 15-minute chart resolution serves as the temporal grid for monitoring price action, but the low trade count proves that entry triggers require stringent conditional alignment. This selective pacing reduces exposure frequency to open market movements, concentrating the strategy's statistical evidence into a small sample of discrete historical trade events.
Quality of historical results
The quality of the strategy's return profile is defined by a high hit rate combined with positive payoff asymmetry. Out of 87 completed trades, 71 resulted in positive returns, yielding an 81.61% win rate. The strategy generated a profit factor of 3.89, indicating that gross historical gains exceeded gross historical losses by nearly four to one. The mathematical structure of individual trade gains reveals substantial consistency: the average trade return stands at +11.16%, closely mirrored by a median trade return of +9.15%. This tight alignment between average and median outcomes demonstrates that the strategy's expectancy is supported by a broad distribution of solid winning trades rather than being skewed solely by extreme statistical anomalies. The single best trade achieved a gain of +119.55%, whereas the concentration of gross profits within the top three winning trades was measured at 23.37%. Because less than a quarter of total gross gains originated from its three largest trades, the strategy's historical profit engine demonstrates broad distribution across its 71 winning positions.
Risk, drawdown and losing behavior
A thorough examination of downside metrics shows that despite an 81.61% win rate, the strategy experiences notable peak-to-trough drawdowns and tail risks during adverse trade outcomes. The maximum historical drawdown reached 36.39%. This drawdown magnitude highlights that risk in this strategy is concentrated in individual trade losses rather than extended series of consecutive failures. The longest losing streak observed in the dataset is only 2 consecutive trades, contrasted with a peak winning streak of 15 consecutive positive trades. However, the severity of individual loss events is substantial: the worst historical trade produced a loss of -31.12%. This single adverse event accounts for a dominant share of the worst-case drawdown. The contrast between short losing streaks and a 36.39% drawdown illustrates that losing positions, when they occur, can inflict severe capital impairment. Investors and analysts must recognize that an 81.61% win rate does not eliminate downside volatility, as trade-level stop mechanisms or exit triggers permit sizeable single-trade drawdowns.
Behavior through time and yearly stability
Analyzing historical performance across multi-year cycles requires tracking annual distribution and trade density over time. In the supplied strategy dataset, granular annual breakdown metrics are omitted. As a result, specific yearly return values, annual trade counts, and year-by-year win rates cannot be empirically evaluated. What the data confirm is the aggregate trajectory: 87 trades distributed across 5.22 years of history resulting in +218,645.34% total return. Because annual data points are unavailable, it is impossible to determine whether historical returns were generated uniformly across the 2021-2026 timeline or concentrated in specific high-volatility years. The average frequency of 16.66 trades per year provides a general benchmark for expected annual trade generation, but without explicit yearly figures, conclusions regarding yearly stability or macro regime dependency remain statistically unverified.
Strengths and limitations
The strategy presents a distinct set of analytical trade-offs. Key structural strengths include a strong historical win rate of 81.61%, an elevated profit factor of 3.89, and a balanced profit structure where the top three winning trades account for only 23.37% of gross profits. Furthermore, the close relationship between the average trade return (+11.16%) and median trade return (+9.15%) underscores systemic trade-level consistency, supported by a peak winning streak of 15 trades. Conversely, primary limitations center on sample size, downside severity, and recent inactivity. With only 87 completed trades over 5.22 years, the statistical sample is relatively small, increasing sensitivity to individual trade outcomes. Risk limitations are evident in the 36.39% maximum drawdown and the -31.12% worst trade. Finally, the total lack of trades since June 1, 2024, creates an empirical evaluation gap regarding the strategy's recent historical applicability.
DevioLab analytical conclusion
The quantitative profile of ASTSB 0 +218645.34% 1TRAD-YTR7 establishes it as a highly selective model with a high historical hit rate on AST SpaceMobile, Inc. Ranking 1 for the asset with a DevioLab score of 77.03, the algorithm demonstrates strong overall backtested metrics, including a profit factor of 3.89 and a cumulative return of +218,645.34%. Its performance model relies on high win accuracy (81.61%) and broad profit distribution across successful entries rather than hyper-dependence on singular tail-event winners. However, potential evaluators must weigh these strengths against the historical risk parameters, specifically a -31.12% worst trade and a 36.39% maximum drawdown. Additionally, the complete absence of trades since June 2024 requires careful consideration, as the strategy's selective filter has kept it entirely disengaged from the market during recent quarters.
Data scope and methodology
This analysis is based strictly on historical backtested performance data generated for AST SpaceMobile, Inc. (ASTS) under ticker ASTSB on a 15-minute chart interval. The evaluation span covers 5.22 years from June 1, 2021, to August 21, 2026. All reported metrics, including completed trade counts, win rates, trade return averages, drawdowns, and profit factors, represent simulated historical closed trades and do not constitute actual live trading account statements or financial guarantees. Transaction costs, execution slippage, borrowing fees, and real-time order routing conditions are not modeled in the source statistics unless explicitly stated. Historical strategy performance is not indicative of future results, and this research report is provided exclusively for educational and quantitative analysis purposes.