APTUSDT
Crypto market · BinanceQuantitative Strategy Analysis: APT Swing Trading Model Achieves 90.2% Historical Win Rate Across Multi-Day Holding Horizons
This analytical report evaluates the historical backtest metrics of the APT algorithmic trading strategy (APT 215000 +12257.57% 1TRAD-AGL8) across a 2.98-year sample period from October 19, 2022, to October 12, 2025. Operating on a 15-minute execution timeframe, the strategy exhibits an unexpected swing-trading profile characterized by low trade frequency and multi-day position durations. Across 51 completed historical trades, the model recorded 46 winning positions against 5 losses, establishing a 90.20% win rate and a profit factor of 33.46. The strategy generated a cumulative closed-trade return sum of +12,257.57%, translating to an annualized performance metric of 569.29%. Our quantitative analysis reveals that performance is broad-based rather than outlier-driven, with the top three winning trades contributing just 21.06% of total gross profit. The average trade gain of +10.54% tracks closely with the median trade gain of +9.62%, demonstrating consistent payoff distribution. Recent evidence from June 1, 2024, onward confirms sustained momentum, yielding 17 closed trades with a combined gain of +216.41%. However, the strategy presents clear structural trade-offs, including extended inter-trade inactivity averaging 21.65 days between exits, a limited total trade count of 51 positions, and an uncomputed maximum drawdown metric in the historical record that requires disciplined risk oversight.
Analysis as of 24.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The APT algorithmic strategy, identified systematically as APT 215000 +12257.57% 1TRAD-AGL8, is an automated trading model evaluated on the APT asset within the cryptocurrency market. Designed to execute signals on a 15-minute interval, the strategy was tracked over a history length of approximately 2.98 years, spanning from October 19, 2022, to October 12, 2025. Over this full historical sample, the system completed 51 total trades, comprised of 46 winning trades and 5 losing trades, culminating in a win rate of 90.20%. In aggregate, the model logged a cumulative closed-trade return sum of +12,257.57%, which corresponds to an annualized return metric of 569.29%. The strategy's overall profit factor stands at 33.46, reflecting a gross profit profile that vastly exceeds historical gross losses. Neither a proprietary DevioLab score nor a ticker rank is assigned to this strategy instance in the dataset, leaving its performance to be analyzed entirely through its empirical statistical characteristics. The combination of a high hit rate and substantial cumulative return highlights a highly selective signal-generation framework that prioritizes trade accuracy over transaction frequency.
Trading rhythm and position duration
Despite operating on a low-granularity 15-minute execution chart, the strategy displays the structural characteristics of a patient swing-trading system rather than a high-frequency or intra-day scalping mechanism. Over the nearly three-year backtest window, the system generated an average of 17.10 trades per year, or roughly 2.22 trades per active month. This low trade frequency indicates that the underlying algorithm applies rigorous filtering criteria, remaining sidelined during prolonged market phases until specific quantitative conditions are met. Position duration statistics further reinforce this swing-trading classification. The average holding duration per trade is 103.76 hours (approximately 4.32 days), while the median holding duration is 55.75 hours (roughly 2.32 days). The right-skewed gap between median and average holding times indicates that while many positions resolve within two to three days, selected trends are held for multiple days to capture extended directional moves. Furthermore, the average time between trade exits is 21.65 days, with a median exit interval of 8.92 days. Traders observing this model must anticipate long periods of inactivity punctuated by multi-day position holds.
Quality of historical results
The quality and distribution of historical returns indicate a high degree of payoff consistency across the trade sample. The strategy's average trade return stands at +10.54%, while the median trade return is +9.62%. The close alignment between average and median expectations indicates that overall historical profitability is driven by a repeatable core distribution of winning trades rather than being skewed by extreme, unrepresentative tail events. The single best historical trade generated a gain of +50.10%, whereas the worst individual trade registered a loss of -8.10%. With a profit factor of 33.46, gross winning trade gains outweigh gross losing trade losses by more than thirty-three to one. Importantly, the three largest winning trades account for 21.06% of total gross profit. This relatively low concentration ratio confirms that the strategy does not rely disproportionately on a tiny cluster of windfall wins; instead, historical profitability is distributed across the broader 46-win set.
Risk, drawdown and losing behavior
Understanding the strategy's risk profile requires examining its losing trade characteristics and sequence behavior. Across 51 completed trades, the system suffered only 5 losses, achieving a historical maximum consecutive losing streak of 1 trade. Conversely, the model achieved a maximum winning streak of 17 consecutive trades. The maximum single-trade historical loss was constrained to -8.10%, demonstrating effective structural risk containment on individual position failures. In the provided dataset, the overall maximum drawdown percentage is uncomputed (recorded as null). Consequently, the peak-to-trough equity decline cannot be directly evaluated from the metrics provided. While a 90.20% win rate and a maximum losing streak of 1 suggest limited historical equity clustering of losses, the absence of a explicit maximum drawdown calculation represents an analytical limitation. Market participants must account for potential open-position floating drawdowns and unrealized volatility during multi-day holding periods that are not fully captured by closed-trade return summaries.
Behavior through time and yearly stability
Evaluating the strategy across distinct calendar years demonstrates remarkable performance consistency throughout changing market environments. In late 2022 (from October 19 onwards), the strategy executed 5 trades, all 5 of which were profitable, yielding a 100% win rate and a summed trade return of +82.02%. Full calendar year data for 2023 and 2024 showcase identical trade counts and win rates. In 2023, the model completed 20 trades with 18 wins and 2 losses (90.00% win rate), accumulating +146.11% in total trade gains. In 2024, the strategy again completed 20 trades with 18 wins and 2 losses (90.00% win rate), generating a higher cumulative gain of +254.84%. In the partial year of 2025 (up to October 12), the strategy completed 6 trades with 5 wins and 1 loss, yielding an 83.33% win rate and +54.57% in summed gains. The stable annual trade count of approximately 20 trades per full year highlights a highly consistent signal density over time.
Recent period since 2024-06-01 versus full history
To assess whether the strategy maintains its historical edge in recent market conditions, we examine closed trade performance from June 1, 2024, to October 12, 2025. During this recent window, the model executed 17 completed trades, accumulating a total closed return sum of +216.41%. These 17 recent trades account for approximately 33.3% of the strategy's total historical trade volume (51 trades). Comparing recent execution against long-term statistics reveals strong continuity. The strategy generated an average of +12.73% per trade during this recent period (+216.41% divided by 17 trades), which slightly exceeds the full-history trade average of +10.54%. This active recent sample demonstrates that the model's structural edge remained functional into mid-2024 and 2025, showing no evidence of decay or strategy degradation over the final 16 months of historical testing.
Strengths and limitations
The primary analytical strengths of this APT strategy include its exceptionally high win rate (90.20%), outstanding profit factor (33.46), and well-distributed payoff structure. The system demonstrates robust consistency across calendar years and avoids concentration risk, as evidenced by the top three trades contributing only 21.06% of gross profits. Furthermore, the low trade frequency minimizes transaction exposure relative to standard 15-minute timeframe strategies. Conversely, important structural limitations must be noted. The strategy's total sample size is relatively small, consisting of only 51 completed trades over nearly three years. This limited sample reduces statistical power compared to high-frequency models with thousands of trades. Additionally, the average interval between trade exits is 21.65 days, requiring substantial patience during prolonged period of signal dormancy. Finally, the historical dataset lacks a calculated maximum drawdown metric, leaving unquantified the magnitude of portfolio drawdowns experienced between equity highs.
DevioLab analytical conclusion
The quantitative evaluation of the APT 215000 +12257.57% 1TRAD-AGL8 strategy reveals a highly refined, low-frequency swing-trading architecture operating on a 15-minute execution interval. By maintaining long holding durations averaging 103.76 hours, the system successfully filters market noise on APT to capture meaningful multi-day trend movements. The statistical profile—highlighted by a 90.20% win rate, a 33.46 profit factor, and a tight alignment between average (+10.54%) and median (+9.62%) trade returns—demonstrates superior historical trade efficiency. While the strategy's historical track record is exceptionally strong across both full-history and recent 2024–2025 periods, potential users must consider its low trade frequency (17.1 trades/year) and limited total sample size of 51 trades. Risk management practices should remain paramount, particularly given the absence of an explicit historical maximum drawdown metric in the backtest logs. As with all quantitative models, past backtested efficacy provides valuable historical insight but does not guarantee future financial performance.
Data scope and methodology
The analysis presented in this report is derived exclusively from historical backtest statistics covering the period from October 19, 2022, to October 12, 2025. All metrics, including trade returns, holding durations, win rates, and annual breakdowns, are computed based on simulated closed trades on the 15-minute candle history for APT. Summed percentage returns represent the arithmetic total of individual trade percentage gains and losses rather than compounded live account performance. This historical research does not incorporate execution costs, bid-ask spreads, exchange fees, slippage, or live order routing constraints, nor does it evaluated active portfolio allocation or leverage usage. The maximum drawdown field was uncomputed in the primary source dataset. This content is provided strictly for educational and quantitative research purposes and does not constitute financial advice or trade recommendations.