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Crypto market · BinanceAPT · APT Strategy Analysis: Quantitative Review of 1TRAD-AGF2 Model
An in-depth quantitative examination of the APT 215000 +12257.57% 1TRAD-AGF2 algorithmic trading model operating on the 15-minute timeframe. Analyzing historical metrics including a 90.20% win rate, 5.83 profit factor, and an 8.10% maximum drawdown across 51 trades.
Analysis as of 14.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The strategy, designated as APT 215000 +12257.57% 1TRAD-AGF2, is an algorithmic model built for the APT cryptocurrency asset in the spot market setting, operating on a 15-minute price candle interval. It holds the rank 2 position for the APT ticker within our evaluated strategy directory with a DevioLab score of 72.41 out of 100. This configuration is non-core, representing a targeted quantitative approach. Across its dataset ending on August 10, 2026, the strategy generated a cumulative historical return of 12257.57%, corresponding to an annualized historical return benchmark of 569.29%. The overall trade sample consists of 51 completed trades.
Trading rhythm and position duration
With 51 executed closed trades across the evaluated sample, the strategy demonstrates a relatively selective trade generation frequency on the 15-minute timeframe. Specific holding duration metrics, such as average or median holding hours and days between exits, are not recorded in this dataset. However, the total trade count of 51 confirms that this is not a high-frequency or scalping approach, but rather a deliberate swing or position model that enters market setups conditionally. The sample size remains statistically small, which requires analytical caution when interpreting performance cadence.
Quality of historical results
Historical transaction outcomes display an exceptionally high hit rate and payoff distribution. Out of 51 completed trades, 46 were winning trades, yielding a win rate of 90.20%, while only 5 trades resulted in losses. The average trade gain across all closed positions was 10.54%, with a median trade return of 9.62%. The single best trade achieved a positive return of 50.10%. Furthermore, the profit factor stands at a robust 5.83, indicating that gross historical gains exceeded gross historical losses nearly sixfold. The top 3 winning trades accounted for 21.06% of total gross profit, showing that while major gains contributed meaningfully, historical performance was not overly dependent on a single outlier event.
Risk, drawdown and losing behavior
Risk control metrics in the backtest reveal contained equity declines and limited consecutive losses. The maximum historical drawdown was restricted to 8.10%, which is particularly notable given the overall cumulative gains. The worst single trade registered a loss of -8.10%, matching the maximum overall drawdown magnitude. The longest losing streak observed in the dataset was just 1 trade, contrasted with a maximum winning streak of 17 consecutive profitable trades. These characteristics demonstrate strong empirical risk containment across the 5 historical losing events.
Behavior through time and yearly stability
Detailed yearly breakdown data is not available in this dataset sample, preventing a granular year-by-year distribution evaluation. Consequently, assessing annual consistency or identifying specific regime changes across individual calendar years cannot be performed from the provided statistics. Evaluators must rely on the overall sample metrics, keeping in mind that temporal concentration or cluster behavior cannot be ruled out without multi-year annual segmentation data.
Strengths and limitations
The primary strengths of this trading model include an outstanding historical win rate of 90.20%, a high profit factor of 5.83, and a tightly controlled maximum drawdown of 8.10%. The maximum winning streak of 17 trades further highlights its historical precision. Conversely, key limitations center on the limited sample size of 51 total trades and complete inactivity since June 1, 2024. The absence of specific holding duration data and yearly breakdown records also limits the ability to assess long-term temporal stability under changing market conditions.
DevioLab analytical conclusion
Achieving a DevioLab score of 72.41 and ranking second among strategies for APT, this model presents a compelling quantitative profile driven by high win frequency and disciplined loss containment. However, the total sample of 51 trades is modest, and the zero-trade activity since mid-2024 warrants careful monitoring. Prospective observers should evaluate whether the underlying market mechanics that produced these historical results remain actionable or if market regime shifts have led to reduced signal trigger rates.
Data scope and methodology
This analysis is based on historical simulated closed trade data for APT in the cryptocurrency market on a 15-minute candle interval up to August 10, 2026. All reported percentage returns describe historical backtested trades and do not represent exact Binance exchange account performance or guaranteed future returns. This study serves strictly for quantitative research purposes and does not constitute financial advice.