FTTUSDT
加密市场 · BinanceQuantitative Strategy Analysis of FTT · FTT on 15m Timeframe
In-depth research of the top-ranked quantitative trading strategy for FTT on DevioLab. Built on 570 completed backtest trades over 6.65 years, this model achieved a 70.88% win rate and a profit factor of 2.0 despite experiencing a peak drawdown of 86.54%.
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Strategy profile
This quantitative trading model is engineered for the cryptocurrency asset FTT · FTT utilizing the 15-minute timeframe (15m). Evaluated by the DevioLab scoring framework, it holds a score of 43.20 and ranks number 1 among strategies analyzed for this specific ticker. Although it is not designated as a Core strategy, it demonstrates strong statistical parameters driven by a high win frequency. The historical dataset covers the span from 2019-12-20 to 2026-08-14, amounting to approximately 6.65 years of continuous backtesting history. Over this timeframe, the model recorded 570 completed trades. This study evaluates its empirical performance solely based on the supplied statistical evidence.
Trading rhythm and position duration
The algorithm executes an average of 85.7 trades per year, reflecting a selective approach on the 15m timeframe. It is not a high-frequency or scalping algorithm, as entry signals are generated only when specific market conditions align. By avoiding market noise, the system focuses on structured price swings. Granular statistics for average and median holding hours, as well as average days between exits, are not available in this dataset. However, given the yearly frequency of around 85 trades, positions naturally duration vary from several hours to multiple days depending on market volatility.
Quality of historical results
Across the complete 6.65-year historical horizon, the model generated a cumulative backtest return of 6,716,726.55%, corresponding to an annualized metric of 45,481.33%. Out of 570 finished trades, 404 were profitable while 166 ended in a loss, yielding an overall win rate of 70.88%. The strategy achieved a profit factor of 2.0, meaning total gross profits were double the total gross losses. The average trade gain was 2.64%, with a median trade performance of 2.10%. The single best trade delivered a gain of 180.40%. Crucially, the top 3 winning trades accounted for only 10.15% of total gross profits, proving that cumulative gains are generated consistently across many trades rather than relying on an outlier trade.
Risk, drawdown and losing behavior
Despite its elevated win rate, the strategy exhibits significant drawdown metrics. The maximum historical drawdown reached 86.54%, indicating that long-term holding periods entailed substantial equity contractions. The worst individual trade generated a loss of -70.77%. Regarding trade streaks, the system recorded a maximum winning streak of 18 consecutive trades and a maximum losing streak of 5 trades. The substantial peak drawdown suggests that during major structural downturns in FTT, the algorithm remained exposed while waiting for its exit parameters to trigger.
Behavior through time and yearly stability
A granular year-by-year performance breakdown table is not present in the provided dataset, limiting direct year-over-year stability comparisons. Nevertheless, observing the overall 6.65-year history reveals that the system sustained positive expectancy across major market regimes. An average rate of 85.7 trades per year demonstrates consistent distribution of signals over the broader historical sample. The positive profit factor across 570 trades confirms that the strategy edge is grounded in statistical structure rather than short-term curve fitting.
Strengths and limitations
The primary strengths of this quantitative model include its strong win rate of 70.88%, a solid profit factor of 2.0, its top ranking (#1) for the FTT ticker, and a healthy profit distribution where the top 3 trades represent only 10.15% of gross profits. Conversely, its limitations center on severe historical drawdown (86.54%), a large maximum single-trade loss (-70.77%), and complete trading dormancy since June 2024. Furthermore, the absence of detailed holding duration parameters requires traders to exercise additional risk management diligence.
DevioLab analytical conclusion
The quantitative strategy FTT 215000 +10045876.13% 1TRAD-LNB7 demonstrates robust analytical performance on FTT · FTT. With a DevioLab score of 43.20 and the top rank for its ticker, the algorithm proves that high win-rate selective trading on 15m charts can build substantial historical returns. However, the peak drawdown of 86.54% highlights that capital protection rules must be carefully considered. The strategy excels in active market environments and automatically steps aside during extended periods of low regime fit.
Data scope and methodology
This analysis is derived strictly from historical simulated closed trade records spanning from 2019-12-20 to 2026-08-14. Performance metrics represent backtested results on the 15m timeframe for FTT · FTT and do not constitute actual live trading account statements on Binance or any exchange. Historical backtests do not guarantee future performance. This research is published for educational and quantitative analysis purposes only and does not constitute financial or investment advice.