APTUSDT
Crypto market · BinanceQuantitative Analysis of APT · APT: High Win Rate Swing Execution Across 15-Minute Market Cycles
This empirical performance evaluation analyzes the historical backtest metrics of the APT quantitative trading strategy operating on a 15-minute execution timeframe. Over a 2.98-year sample period from October 2022 to October 2025, the strategy executed 51 closed trades, generating a 90.20% win rate and a Profit Factor of 37.06. Despite utilizing 15-minute price bars for entry and exit triggers, the system exhibits classic swing trading characteristics, with a median position holding duration of 42.0 hours and an average holding duration of 94.05 hours. Profitability is remarkably well-distributed across the sample, with the top three winning trades accounting for only 20.03% of total gross profit, while the median trade yield of +10.44% closely tracks the average trade yield of +11.12%. Recent performance since June 1, 2024, remains active and robust, accounting for 16 completed trades with a cumulative trade return sum of +199.42%. This analysis explores the statistical interplay between low trade frequency, multi-day holding times, drawdown containment, and long-term signal stability.
Analysis as of 24.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative strategy under review, designated as APT 215000 +16110.98% 1TRAD-KTO2, evaluates price structure on the APT asset within the cryptocurrency market using a 15-minute candlestick chart interval. Historical backtesting spans approximately 2.98 years, beginning on October 19, 2022, and concluding on October 12, 2025. Across this full test window, the system generated a compact sample of 51 completed trades, corresponding to an average annual trade frequency of 17.10 trades per year, or approximately 2.43 trades per active month. Out of the 51 completed trades, 46 closed in profit while 5 ended in losses, establishing a historical hit rate of 90.20%. While the underlying market data feed operates on 15-minute intervals, the strategy's operational profile differs fundamentally from high-frequency intraday trading or scalping. The lower trade frequency indicates that signal generation criteria are highly selective, filtering out short-term price noise in favor of specific market setups. Over the entirety of the evaluation period, the strategy achieved an aggregate closed-trade return sum of +16,110.98%, supported by an extraordinary Profit Factor of 37.06. This baseline statistical structure establishes the strategy as a selective, high-expectancy quantitative model optimized for capturing medium-term price swings on APT.
Trading rhythm and position duration
Analyzing position duration reveals a distinct structural pattern in how the strategy interacts with market volatility. The average holding duration for open positions stands at 94.05 hours (approximately 3.92 days), whereas the median holding duration is 42.00 hours (1.75 days). The positive divergence between the median and average holding times indicates a right-skewed duration distribution: while half of all positions resolve within roughly 1.75 days, a selection of trades remains open for multiple days or weeks to capture extended directional trends. The temporal spacing between trade exits further illustrates the strategy's patient execution rhythm. The average time between closed exits is 21.65 days, whereas the median time between exits is 7.16 days. This discrepancy highlights that trade activity is not uniformly distributed throughout the year. Instead, trade generation occurs in concentrated clusters during favorable market regimes, separated by multi-week periods of total market inactivity when structural criteria are not met. The combination of a 15-minute signal resolution and multi-day holding periods confirms that lower timeframe candles are utilized primarily for entry precision, while exit conditions are governed by broader swing market dynamics.
Quality of historical results
The payoff statistics demonstrate strong mathematical symmetry and broad-based capital accumulation. The average trade yield across all 51 historical events is +11.12%, which aligns remarkably closely with the median trade yield of +10.44%. In quantitative backtesting, a close alignment between average and median expectations indicates that overall performance is driven by repeatable, systematic trades rather than distorted by extreme positive outliers. The single best trade achieved a gain of +50.10%, while the worst trade resulted in a loss of -8.10%. The payoff asymmetry between the best winner (+50.10%) and the worst loser (-8.10%) highlights favorable historical reward-to-risk dynamics per trade event. Furthermore, concentration analysis shows that the top three winning trades collectively generated 20.03% of the strategy's total gross profits. This relatively low concentration ratio confirms that the cumulative gains were broadly distributed across the 46 winning trades rather than dependent on a few isolated market events. Combined with a Profit Factor of 37.06, the quality of historical trade results reflects structural execution strength and low dependency on single-trade windfalls.
Risk, drawdown and losing behavior
Risk management metrics within the strategy's trade history highlight bounded downside exposure and exceptional streak resistance. The maximum single-trade loss observed across the entire 2.98-year sample was held to -8.10%, establishing a firm historical threshold on downside tail risk per event. Over the 51 closed trades, the system encountered only 5 losing trades in total, achieving a maximum consecutive losing streak of exactly 1 trade. Conversely, the longest winning streak reached 21 consecutive profitable trades. Because the portfolio-level maximum drawdown percentage metric is unpopulated in the primary closed-trade dataset, drawdown characteristics must be interpreted through closed trade sequences and open-position duration dynamics. Given that individual losses never occurred in back-to-back sequence and were capped at -8.10%, closed-trade equity drawdowns remained historically shallow. However, because positions are held for an average of 94.05 hours, prospective traders must acknowledge that unbooked intra-trade adverse float inevitably occurs during position lifetimes. Managing intra-trade equity swings remains a core risk consideration even when closed-trade loss metrics appear exceptionally controlled.
Behavior through time and yearly stability
Evaluating the yearly breakdown demonstrates how the strategy performed across changing market regimes from late 2022 through late 2025. In 2022 (covering the period from October 19 onward), the strategy completed 5 trades, all 5 of which were profitable (100.00% win rate), delivering a cumulative return sum of +82.02%. In 2023, trading frequency increased to 19 completed trades, yielding 18 wins and 1 loss (94.74% win rate) with a aggregate return sum of +159.75%. The year 2024 represented the strategy's highest activity period, completing 23 trades with 20 wins and 3 losses (86.96% win rate), generating an annual return sum of +282.37%. In the partial period of 2025 up to October 12, the system recorded 4 completed trades, comprising 3 wins and 1 loss (75.00% win rate) and a cumulative return sum of +42.91%. Across all complete annual periods, the strategy maintained trade win rates above 85% and positive aggregate return sums, demonstrating consistent operational stability across different multi-month volatility cycles.
Recent period since 2024-06-01 versus full history
Focusing on the recent evaluation window from June 1, 2024, to October 12, 2025, provides crucial insight into the strategy's recent operational validity. During this 16.3-month recent sub-period, the strategy executed 16 completed trades, representing 31.37% of its total historical trade sample. These 16 trades produced a cumulative sum of trade returns equal to +199.42%. Comparing the recent window to the full history shows sustained trade frequency and performance consistency. The generation of 16 trades over roughly 16 months matches the full-history average pace of approximately 17.10 trades per year. Furthermore, the average return contribution per trade during this recent period remained well above historical baselines. This confirms that the quantitative signals did not experience performance decay or alpha compression following structural changes in the broader cryptocurrency landscape after mid-2024.
Strengths and limitations
The statistical evaluation highlights several clear structural strengths alongside notable operational limitations: Key Strengths: 1. High Historical Win Rate: Achieving a 90.20% win rate across 51 trades creates a highly favorable expectancy profile. 2. Exceptional Profit Factor: A Profit Factor of 37.06 demonstrates minimal aggregate loss production relative to gross gains. 3. Broad Profit Distribution: The top three winning trades represent only 20.03% of gross profits, proving that performance is non-dependent on rare statistical outliers. 4. Robust Recent Performance: The execution of 16 trades yielding +199.42% since June 2024 confirms ongoing system stability. Key Limitations: 1. Compact Sample Size: A total sample of 51 trades over nearly 3 years limits absolute statistical power, requiring cautious forward assumptions. 2. Intra-Trade Volatility Exposure: Holding positions for a median of 42.0 hours on a 15-minute trigger means accounts remain exposed to unmeasured open equity fluctuations. 3. Execution and Cost Exclusions: Historical backtest figures do not incorporate real-world execution slippage, exchange fee tiers, or potential funding rate costs associated with holding leveraged positions multi-day.
DevioLab analytical conclusion
The APT quantitative strategy presents a highly compelling model of low-frequency, high-expectancy swing trading on cryptocurrency assets. By extracting high-conviction signals from 15-minute price data and holding positions across multi-day swing horizons, the system successfully bridges the gap between intraday execution timing and macro trend capture. Its statistical signature is defined by an extraordinary 90.20% win rate, a Profit Factor of 37.06, and an exceptionally balanced profit contribution across trade events. Nevertheless, quantitative analysts must evaluate these simulated backtest results within proper context. The total sample size of 51 completed trades reflects selective filtering but provides a moderate statistical base. While recent performance since June 2024 confirms structural continuity (+199.42% across 16 trades), real-world implementation demands strict attention to capital preservation, fee management, and floating drawdown tolerance during multi-day holding periods.
Data scope and methodology
This analysis is based entirely on historical backtested strategy data generated for the asset APT on the 15-minute chart interval across the time window spanning October 19, 2022, to October 12, 2025. All metrics, including trade counts, win rates, holding durations, yearly breakdowns, and profit factor calculations, represent closed simulated trade performance under static backtest conditions. The start date of October 19, 2022, marks the commencement of the quantitative historical dataset and does not denote the official market launch or listing date of the underlying asset. Simulated historical returns do not account for real-time account parameters, variable order book slippage, exchange commission structures, or margin funding fees. Historical statistical performance is provided exclusively for quantitative research and educational evaluation and does not constitute financial advice, investment recommendations, or a guarantee of future live performance.