UNIUSDT
Kryptomarkt · BinanceUNI · UNI: Quantitative Analysis of Trading Strategy TRAD-TGG8
A comprehensive quantitative examination of the algorithmic model UNI 215000 +792506.40% TRAD-TGG8 on the 15-minute timeframe, evaluating historical profitability, drawdowns, and trade mechanics.
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Strategy profile
The trading system named UNI 215000 +792506.40% TRAD-TGG8 is designed to trade the crypto asset UNI on a 15-minute time interval (15m). Within the DevioLab ranking for this specific ticker, the strategy holds the 2nd position, achieving a DevioLab Score of 67.90 out of 100. The model is categorized as a non-core strategy (is_core is false) and was selected for detailed historical review based on its statistical footprint. The analytical scope is strictly spot-oriented within the cryptocurrency market without assuming artificial leverage or borrowed funds. The underlying historical dataset spans from September 17, 2020, to August 12, 2026, representing approximately 5.9 years of market data.
Trading rhythm and position duration
Over the full historical span of nearly six years, the strategy completed a total of 266 trades. This translates to an annualized trading frequency of roughly 45.07 trades per year. Given this moderate trading pace, the approach cannot be categorized as scalping, but rather as a selective momentum or swing model operating on intraday bars. Specific statistics regarding average and median position holding times in hours, as well as the duration between trade exits, are not available in the source metadata. Nevertheless, the total count of 266 completed positions indicates a disciplined execution model where entry triggers are generated selectively.
Quality of historical results
The strategy demonstrates strong historical accuracy. Out of 266 closed trades, 196 ended in profit while 70 recorded losses, yielding a win rate of 73.68%. Cumulative historical gain across the entire sample reached 792506.40%, with an annualized return figure of 2438.10%. The profit factor stands at 3.75, confirming a robust ratio of gross profits relative to gross losses. The average trade performance is 4.04%, with a median trade return of 3.60%. The single best trade generated a return of 76.74%. Significantly, the top three winning trades account for only 9.24% of gross profits, proving that historical performance was driven by consistent repeated gains rather than a few isolated lucky trades.
Risk, drawdown and losing behavior
The maximum historical equity drawdown (Max Drawdown) recorded by the strategy was 29.95%. For a volatile cryptocurrency asset, this drawdown level represents a contained risk profile relative to the total accumulated performance. The single worst trade recorded a loss of -23.99%. In terms of trade streaks, the algorithm experienced a maximum winning streak of 24 consecutive trades, whereas its longest losing streak was capped at just 4 consecutive losses. This short losing sequence highlights effective capital preservation dynamics during unfavorable market conditions.
Behavior through time and yearly stability
The analytical sample covers 5.9 years of continuous data starting from September 2020. Detailed yearly performance breakdowns are not provided in the source dataset. However, the average frequency of 45.07 trades per year reflects a stable, rule-based approach that triggers trades only when specific statistical conditions are met on the 15-minute UNI charts.
Strengths and limitations
Key strengths of this model include a high win rate of 73.68%, an impressive profit factor of 3.75, short losing streaks limited to 4 trades, and a well-distributed profit curve where the top 3 trades contribute under 10% of gross profits. Primary limitations involve the lack of trade activity after June 2024, the presence of a maximum single-trade loss of -23.99%, and a total sample size of 266 trades, which remains moderate and warrants cautious statistical interpretation.
DevioLab analytical conclusion
The UNI strategy TRAD-TGG8 exhibits compelling historical efficiency, backed by its DevioLab Score of 67.90 and 2nd rank among strategies for this asset. The model demonstrated strong edge distribution without reliance on outlier trades. However, its extended inactivity in the most recent market window suggests traders should monitor current volatility regimes before drawing practical conclusions.
Data scope and methodology
All figures and percentages presented describe historical simulated closed trades based on the provided dataset from September 17, 2020, to August 12, 2026. They do not represent real-time Binance exchange trading results and do not guarantee future performance. This study is provided for educational and analytical research purposes only and does not constitute financial advice.