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Crypto market · BinanceAPT · Aptos Quantitative Strategy Analysis: Evaluating a High-Win-Rate Swing Model on the 15-Minute Timeframe
This analytical report evaluates the historical backtested performance of the APT 215000 +15580.56% 1TRAD-MCO3 quantitative strategy on the APT (Aptos) crypto asset. Utilizing a 15-minute timeframe over a 2.98-year evaluation window, the algorithm generates a total history profit of +15,580.56% across 83 completed trades, achieving a high win rate of 78.31% and a profit factor of 3.13. Ranked 6th among APT strategies on DevioLab with a score of 61.89, the model exhibits clear swing trading characteristics with an average holding duration of 118.08 hours (~4.9 days) and exceptionally well-distributed gains, where the top three winning trades contribute just 11.95% of total gross profit. This study investigates the statistical profile, drawdown dynamics, yearly consistency, and recent execution metrics from June 2024 through October 2025.
Analysis as of 23.08.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The quantitative strategy under review, designated technical name APT 215000 +15580.56% 1TRAD-MCO3, evaluates price action on the 15-minute timeframe for APT (Aptos) in the crypto market. Over a total historical sample of 2.98 years spanning from October 19, 2022, to October 12, 2025, the strategy produced a cumulative backtested return of +15,580.56% across 83 completed trades. This performance translates to an annualized return metric of 415.88% based on the standard reference baseline. On the DevioLab quantitative evaluation framework, the strategy holds a DevioLab Score of 61.89, placing it in rank 6 among all strategies evaluated for the APT asset. The primary driver of this score is the strategy's exceptional hit rate combined with solid payoff efficiency. Out of 83 historical position exits, 65 resulted in positive returns while 18 resulted in losses, yielding a win rate of 78.31%. The profit factor stands at 3.13, indicating that total gross winning trade percentages exceeded gross losing percentages by more than three to one over the entire dataset.
2. Trading rhythm and position duration
Although the algorithm receives market data on a granular 15-minute chart interval, its actual execution profile is characteristic of patience-driven swing trading rather than short-term scalp trading. The strategy completed only 83 trades across nearly three years of continuous operation, which translates to a trade frequency of 27.82 trades per year, or approximately 2.59 trades per active month. Position turnover is deliberate, with an average interval between exits of 13.27 days and a median exit cadence of 9.15 days. Position duration statistics confirm that trades are held across multi-day price cycles. The average holding time for a completed trade is 118.08 hours (approximately 4.92 days), while the median holding duration is 86.25 hours (approximately 3.60 days). The difference between the average and median holding times suggests that while most trades resolve within three to four days, occasional positions extend beyond five days to capture extended multi-day trend continuations. The 15-minute timeframe serves as a high-resolution trigger mechanism for timing entries and exits, but the strategy's overarching exposure rhythm reflects structural swing trading.
3. Quality of historical results
An examination of individual trade statistics reveals a favorable return distribution. The average return across all 83 completed trades is +6.99%. Interestingly, the median trade return stands higher at +8.56%. In quantitative distributions, when the median return exceeds the average return, it indicates a left-skewed shape among winning outcomes where a high proportion of trades settle near or above the median, while occasional larger negative trades pull down the mathematical mean. The single best trade in the dataset delivered a return of +31.62%, whereas the single worst trade registered a loss of -20.95%. A critical metric assessing the structural robustness of the strategy is the concentration of profit: the top three winning trades collectively account for only 11.95% of total gross profit. This low concentration proves that historical performance is not reliant on a small cluster of extreme outlier trades or fortunate market anomalies. Instead, the strategy demonstrates broad-based profitability across dozens of consistent trade setups, supporting the reliability of its historical payoff structure.
4. Risk, drawdown and losing behavior
While the strategy maintains a high win rate of 78.31% and a strong profit factor of 3.13, its historical risk metrics highlight notable downside equity fluctuations. The maximum equity drawdown recorded over the 2.98-year backtest period reached 30.63%. Analyzing this drawdown alongside trade streak dynamics reveals an interesting risk dynamic: the strategy experienced a maximum losing streak of only 3 consecutive trades, compared to a remarkably long maximum winning streak of 25 consecutive winning trades. Because the maximum losing streak is capped at just 3 trades, the 30.63% maximum drawdown was not caused by a prolonged, multi-month series of sequential trade failures. Instead, drawdown peaks reflect the impact of individual magnitude outliers—such as the worst trade of -20.95%—or short clusters of moderate losses occurring during elevated equity levels. This indicates that while trade failures are infrequent, an individual losing exit can be relatively severe compared to the typical trade gain. Quantitative monitoring must therefore focus on loss-magnitude control rather than high loss frequency.
5. Behavior through time and yearly stability
Analyzing annual performance metrics provides key insight into how the strategy adapts across changing volatility regimes in APT. During the initial partial year of 2022 (covering approximately 2.5 months from October 19), the strategy executed 5 trades, achieving a 100.00% win rate and a cumulative return sum of +52.18%. In 2023, the strategy completed 27 trades with 21 wins and 6 losses, representing a 77.78% win rate and a cumulative trade return sum of +205.42%. Performance remained consistent in 2024, with 31 completed trades, 25 wins, 6 losses, an 80.65% win rate, and a return sum of +212.60%. In the final year of the dataset up to October 2025, the strategy executed 20 trades with 14 wins and 6 losses, yielding a 70.00% win rate and a sum of +110.06%. Across all full calendar years, trade counts remained remarkably stable (between 20 and 31 trades per year), while annual win rates consistently stayed within the 70.00% to 80.65% band, demonstrating steady multi-year execution.
6. Recent period since 2024-06-01 versus full history
Evaluating recent performance since June 1, 2024, allows for an assessment of whether the strategy's statistical edge persisted into contemporary market conditions. In this recent window, the strategy completed 36 trades, generating a cumulative trade return sum of +190.63%. These 36 recent trades represent 43.37% of the total 83 historical trades in the dataset, providing a broad empirical sample over the last 16 months of testing. Comparing recent output with full-history norms demonstrates strong performance continuity. The execution frequency remained steady, with 36 trades completed over roughly 16.4 months (~2.2 trades per month), closely matching the overall historical rate of 2.59 trades per active month. Furthermore, the return sum of +190.63% generated during this period confirms that the strategy maintained robust profit extraction capability in recent market structures without exhibiting performance degradation.
7. Strengths and limitations
The primary analytical strength of this strategy lies in its exceptional win rate (78.31%) coupled with a low gross profit concentration. With the top three winning trades accounting for only 11.95% of gross profits, performance is structurally distributed across many trades. Furthermore, the strategy exhibits an exceptionally long maximum winning streak of 25 trades and maintains a high profit factor of 3.13. The median trade return of +8.56% exceeding the average trade return of +6.99% further reinforces that typical trade outcomes are robust. Conversely, key limitations include a historical maximum drawdown of 30.63% and a worst single trade loss of -20.95%, demonstrating that losing trades, though rare, can exert substantial downside impact on open equity. Additionally, with only 83 completed trades over nearly three years, the sample size is relatively small from a statistical confidence perspective. Market participants must also accommodate long holding periods averaging nearly 5 days, requiring patience during multi-day swing holding periods.
8. DevioLab analytical conclusion
The quantitative evaluation of APT 215000 +15580.56% 1TRAD-MCO3 demonstrates a high-probability swing trading profile on APT. Ranking 6th for the asset with a DevioLab Score of 61.89, the strategy strikes a strong balance between entry selectivity and trade duration. It trades infrequently—averaging roughly two to three times per month—and relies on capturing multi-day trend segments. The algorithm's high win rate (78.31%) and low profit concentration (11.95% in top 3 trades) confirm that its backtested growth trajectory is built on sustained operational consistency rather than erratic outliers. However, the presence of a 30.63% maximum drawdown highlights the importance of downside loss limits, as individual trade losses can be sizeable. Overall, the strategy represents a well-structured swing quantitative model with persistent multi-year performance.
9. Data scope and methodology
This analysis is derived exclusively from backtested historical trade data for the APT 215000 +15580.56% 1TRAD-MCO3 strategy operating on the APT asset (APT) on a 15-minute timeframe. The total data scope covers 2.98 years between October 19, 2022, and October 12, 2025. All metrics, including trade counts, holding durations, win rates, return percentages, and drawdown levels, represent simulated closed-trade performance. Percentages describe simulated return sums and compounded totals across completed trades and do not represent actual live brokerage account balances. Real-world execution factors such as exchange fees, order execution latency, slippage, margin interest, and market liquidity are not accounted for in these raw historical figures. Past backtested results provide analytical context regarding algorithm design but do not guarantee future live trading performance.