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Each algorithmic strategy corresponds to a real trading agent (Trading Bot) created by the DevioLab team from historical-data testing. It can be used for automated trading through supported exchange APIs — Binance, OKX, Bybit, Gate.io, KuCoin and others as they are enabled — or as a Signal Bot with Telegram and email notifications.
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APTUSDT

Crypto market · Binance
APT 215000 +17862.56% 1TRAD-TOV6
Recommended by DevioLab · Core 1 iPrimary DevioLab recommendation: a more protected, smoother and more stable profile focused on risk and drawdown control.
Recommended by DevioLab · Core 2 iMore aggressive DevioLab recommendation: accepts higher risk and deeper drawdowns in exchange for potentially higher returns.
68Trades
82.4%Win rate
+7.40%Avg trade
+33.64%Best trade
-19.08%Worst trade
+839.8%Annualized
Strategy analytical profile · 861011de08a0f733

APT · APT Quantitative Strategy Analysis: 15-Minute Algorithmic Model Evaluation

A quantitative examination of the top-ranked APT 15-minute trading algorithm, featuring an 84.29% win rate, a 13.08% maximum drawdown, and a DevioLab score of 80.55 across 70 completed historical trades.

Analysis as of 13.08.2026. Strategy data is updated separately.
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Strategy profile

This research report evaluates the quantitative performance of the algorithmic trading strategy named APT 215000 +17862.56% 1TRAD-TOV6, designed for the spot digital asset APT on the 15-minute timeframe (15m). Within the DevioLab framework, this model is classified as a Core strategy and holds the number 1 rank among all analyzed models for this specific asset. It achieves a DevioLab Score of 80.55 out of 100, reflecting a well-balanced risk-to-reward ratio. The underlying dataset consists of 70 fully closed historical trades with an evaluation end date of August 10, 2026. The algorithm targets short-term structural market opportunities on intraday timeframes.

Trading rhythm and position duration

Across the entire recorded history of 70 trades, the strategy exhibits a selective trading frequency. Because exact metrics for average and median holding hours, as well as days between exits, are not provided in the source dataset, this strategy is not characterized as a high-frequency or scalping model. The modest total trade count suggests that the execution module relies on stringent filtering criteria, prioritizing entry precision over trade volume. Operating on a 15-minute chart requires strict execution quality to manage spread and slippage during volatile periods.

Quality of historical results

The overall cumulative simulated gain of the strategy reached 17862.56%, corresponding to an annualized metric of roughly 839.81%. The trade quality is highlighted by a high win rate of 84.29%, winning 59 out of 70 completed positions with only 11 losing trades. The average trade yield stands at 8.35%, closely aligned with a median trade return of 7.52%, indicating consistent execution performance without reliance on outliers. The single best trade delivered a gain of 36.60%, while the overall profit factor reached 1.67, confirming that total gross profits comfortably exceed total gross losses.

Risk, drawdown and losing behavior

Risk preservation is a defining feature of this quantitative design. The maximum historical drawdown (Max Drawdown) was held to 13.08%, a conservative figure for spot crypto trading on lower timeframes. The single largest loss experienced by the system was -11.60%. The model achieved a maximum winning streak of 20 consecutive profitable trades, compared to a maximum losing streak of just 2 consecutive trades. Furthermore, the top 3 winning trades accounted for only 15.49% of gross profits, demonstrating that the strategy's equity curve is driven by broad consistency rather than concentration risk.

Behavior through time and yearly stability

Detailed yearly breakdown data and an exact historical start timestamp are not present in the current dataset. Consequently, performance distribution across individual calendar years or specific macroeconomic crypto cycles cannot be granularly verified. Nonetheless, the aggregate statistics across the full historical sample reflect robust structural adaptability. Further forward testing remains recommended to monitor behavior across shifting market regimes.

Strengths and limitations

Key strengths of this model include an 84.29% win rate, a low maximum drawdown of 13.08%, a short maximum losing streak of 2 trades, and low profit concentration with the top 3 trades contributing only 15.49%. Limitations stem from the limited total sample size of 70 trades, missing holding duration metrics, and a lack of active trades since June 2024. A smaller sample size requires prudent expectation management when evaluating statistical confidence.

DevioLab analytical conclusion

The APT 215000 +17862.56% 1TRAD-TOV6 strategy secures the 1st rank for the APT ticker with a DevioLab Score of 80.55. It presents an attractive historical risk profile with controlled drawdown metrics and strong win rate consistency. Although trading activity paused in the post-June 2024 window, its core statistical foundation on the 15-minute timeframe remains highly competitive.

Data scope and methodology

All percentage metrics and analytical findings in this report describe historical simulated closed trades on spot APT pairs. These historical figures do not represent live Binance account returns and do not guarantee future results. The historical dataset covers 70 closed trades ending on August 10, 2026. This publication is provided strictly for educational and quantitative research purposes and does not constitute financial advice.

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