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Mercado cripto · BinanceAPT · APT Quantitative Strategy Analysis: High-Hit-Rate Swing Trading Profile and Multi-Year Historical Performance
An analytical evaluation of the APT 215000 +19214.77% 1TRAD-RSH4 quantitative trading strategy operating on the 15-minute timeframe for APT (APT). Over a historical testing window spanning nearly three years from October 2022 to October 2025, the strategy executed 68 completed trades with an overall win rate of 85.29% and a profit factor of 12.02. Position holding times average 103.60 hours, characterizing the system as a selective, multi-day swing trading framework rather than a high-frequency model. This analysis details the structural distribution of returns, trade frequency, risk behavior, and historical performance consistency across changing market regimes.
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Strategy profile
The quantitative model evaluated here, designated as APT 215000 +19214.77% 1TRAD-RSH4, operates on the 15-minute timeframe for APT (APT) within cryptocurrency spot or futures markets. Across a sample window spanning 2.98 years between October 19, 2022, and October 12, 2025, the strategy completed 68 closed trades. The historical record shows 58 winning positions against 10 losing positions, establishing a high win rate of 85.29%. The overall cumulative backtested return reaches 19,214.77%, which translates to an annualized return metric of 839.81%. The overall profit factor stands at 12.02, reflecting a strong asymmetry between total gross profits and total gross losses. With an average trade gain of 8.69% and a median trade return of 7.85%, the strategy displays broad distributional support across its trades rather than relying on an isolated extreme outlier. Neither a DevioLab overall score nor a specific ticker rank is assigned to this model in the provided dataset, requiring an evaluation grounded directly upon its underlying trading statistics and duration metrics.
Trading rhythm and position duration
Although the strategy executes on a 15-minute chart interval, its execution rhythm reflects a patient, multi-day swing trading orientation rather than intraday scalping. Over the nearly three-year history, the system generated an average of 22.79 trades per year, or approximately 2.34 trades per active month. The exit frequency is relatively sparse: the average interval between closed trades is 16.21 days, while the median interval between exits is 9.53 days, indicating that trade completions often occur in clusters separated by multi-week periods of flat positioning or active holding. Position duration statistics further reinforce this swing trading character. The average holding duration per trade is 103.60 hours (roughly 4.32 days), while the median holding duration is 73.75 hours (approximately 3.07 days). The proximity between average and median holding times indicates a consistent holding period structure across trades. Because positions remain open across many days despite receiving price updates every 15 minutes, the underlying mechanism appears designed to capture medium-term trend movements while filtering out short-term market noise.
Quality of historical results
The analytical quality of a quantitative strategy depends on the relationship between its win rate, payoff ratio, and profit concentration. With an 85.29% win rate across 68 trades, the model demonstrates high directional accuracy during the historical period. The average winning or overall trade return of 8.69% closely mirrors the median trade return of 7.85%, demonstrating that positive expectation is distributed across the trade population rather than driven by a single statistical fluke. The strategy's best trade yielded a gain of 36.60%, whereas its worst trade incurred a loss of -11.60%. The ratio of maximum gain to maximum loss indicates controlled risk per position relative to potential upside. Furthermore, the top three winning trades collectively account for 15.60% of gross historical profits. This relatively modest profit concentration confirms that the remaining 84.40% of gross profits was generated by the other 55 winning trades, underscoring the structural robustness of the return distribution across the historical sample.
Risk, drawdown and losing behavior
Risk characteristics for this model display a strong imbalance between winning and losing sequences. The longest consecutive winning streak reached 20 trades, representing nearly thirty percent of all trades executed over the three-year sample. Conversely, the longest consecutive losing streak was limited to just 2 trades. Over the entire 68-trade dataset, only 10 trades ended in a loss, contributing to the high overall profit factor of 12.02. The single largest drawdown percentage is not recorded in the available source metrics (listed as null). However, the worst individual trade loss of -11.60% provides an indicator of single-position downside tail risk under historical conditions. Because losing streaks are short and total losses comprise a small fraction of overall turnover, historical drawdowns were likely constrained in duration. Nonetheless, traders evaluating this profile must recognize that a strategy relying on a high win rate can experience unexpected drawdowns if market regimes shift to conditions where entry signals fail repeatedly.
Behavior through time and yearly stability
Analysis of the yearly performance breakdown reveals how the strategy performed across different market conditions between late 2022 and late 2025. In 2022 (covering late October through December), the strategy closed 4 trades, all 4 of which were profitable (100% win rate), producing a summed return of 74.63%. In 2023, activity expanded to 19 trades with 16 wins and 3 losses (84.21% win rate), generating a summed return of 167.58%. Performance peaked in 2024, which generated 29 completed trades—the highest annual volume in the dataset. Of these, 27 were winners and 2 were losses, establishing a 93.10% win rate and a cumulative return sum of 258.23%. In 2025 (up to October 12), the strategy recorded 16 trades with 11 wins and 5 losses, resulting in a lower win rate of 68.75% and a total sum of 90.25%. While the 2025 win rate declined compared to 2024, the strategy remained net positive, demonstrating multi-year profitability across varying volatility environments.
Recent period since 2024-06-01 versus full history
Isolating the recent window from June 1, 2024, to October 12, 2025, provides insight into the strategy's current effectiveness. During this recent sub-period, 36 trades were completed, representing more than half (52.94%) of the entire 68-trade historical dataset. The sum of closed trade returns during this recent timeframe reached 249.29%. Comparing the recent window to the full history demonstrates sustained trading frequency and performance continuity. The generation of 36 trades over approximately 16.4 months translates to an annualized rate of about 26.3 trades per year, slightly higher than the overall historical average of 22.79 trades per year. This active engagement confirms that the entry conditions continue to trigger regularly in recent APT market structures rather than stalling or degrading into non-activity.
Strengths and limitations
The primary strength of this strategy lies in its high statistical accuracy and favorable payoff metrics. An 85.29% win rate combined with a profit factor of 12.02, a low top-three winner concentration (15.60%), and a short maximum losing streak of 2 trades reflect a robust backtested return profile. Additionally, position durations averaging 103.60 hours protect the model from excessive turnover and bid-ask spread attrition that typically affect higher-frequency 15-minute strategies. The principal limitation of the strategy is its relatively small total trade sample size of 68 completed positions over 2.98 years. While adequate for preliminary quantitative assessment, a sample of 68 trades carries a wider statistical confidence interval than systems with hundreds of trades. Furthermore, the absence of explicit maximum drawdown data, commission costs, slippage parameters, and order execution details requires cautious interpretation when extrapolating backtested outputs to live trading environments.
DevioLab analytical conclusion
The APT 215000 +19214.77% 1TRAD-RSH4 strategy presents a compelling quantitative profile characterized by high win rates, multi-day holding periods, and broad profit distribution across trades. Operating on a 15-minute bar structure while maintaining average position durations above four days allows the model to capture intermediate swings in APT without succumbing to intra-day noise. While historical compounding yielded an overall return metric of 19,214.77% across nearly three years, prospective evaluators should contextualize these results within the 68-trade sample size and the observed win-rate contraction in 2025 (68.75%). The strategy demonstrates high historical efficiency, but ongoing performance monitoring across diverse market regimes remains essential.
Data scope and methodology
This analysis is based strictly on backtested closed-trade performance metrics provided by DevioLab for the APT symbol on a 15-minute timeframe between October 19, 2022, and October 12, 2025. All trade metrics—including profit factor, win rates, holding durations, and annual return sums—are calculated from historical simulated executions. These results do not account for live order execution factors such as exchange fees, bid-ask spreads, order book slippage, or funding rates. Historical backtested performance does not guarantee future operational results. This analysis is intended solely for quantitative research and educational evaluation, not as financial advice or an investment recommendation.