GRTUSDT
Crypto market · BinanceQuantitative Evaluation of GRT · GRT 15-Minute Swing Strategy Generating a 7.22 Profit Factor Across 5 Years of Historical Data
This quantitative research study analyzes the performance metrics of the top-ranked swing trading strategy for GRT (GRT) over a 5.06-year test period from December 2020 to January 2026. Executing on a 15-minute timeframe, the strategy demonstrates a highly symmetrical profit profile characterized by an 84.06% historical win rate across 138 closed trades, a profit factor of 7.22, and a DevioLab score of 83.08. Notably, the strategy avoids profit concentration, with its three largest winning trades accounting for just 8.13% of gross realized profit. The model maintains historical profitability across every analyzed calendar year, including difficult market conditions in 2022, while generating 33 completed trades with a aggregate return sum of 250.32% in the recent testing window since June 1, 2024. This analysis explores the statistical tensions between the system high hit rate, multi-day holding times, and its historical maximum drawdown of 38.42%.
Analysis as of 30.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative trading model designated for GRT (GRT) operates on a 15-minute execution interval within the cryptocurrency spot or derivatives market architecture. Across a historical dataset spanning 5.06 years from December 17, 2020, to January 9, 2026, the strategy recorded 138 completed trade cycles. Out of these trades, 116 closed with positive historical returns while 22 resulted in losses, establishing a baseline win rate of 84.06%. The overall profit factor stands at 7.22, reflecting a strong historical balance between total realized gains and losses. Based on these composite statistical outcomes, the model achieves a DevioLab performance score of 83.08, holding the number one rank among evaluated strategies for this specific ticker. While the strategy uses a 15-minute price series for trigger evaluation, its structural parameters lead to multi-day trade durations rather than high-frequency intraday churn, positioning it firmly within the swing trading domain.
Trading rhythm and position duration
The statistical distribution of trade frequency and holding time demonstrates a selective, swing-oriented operational pace. The strategy averages 27.25 completed trades per year, which translates to approximately 2.42 completed positions per active month. Position durations are substantially longer than the underlying 15-minute chart timeframe suggests. The average holding duration per trade is 65.77 hours (approximately 2.74 days), while the median holding duration is 40.63 hours (approximately 1.69 days). This positive skew between median and average holding time reveals that while most trades resolve within roughly 40 hours, a subset of positions remains open for several days to capture broader market moves. Inter-trade pacing shows an average of 13.46 days between position exits, with a median interval of 8.94 days. The disparity between active holding time and exit spacing highlights extended out-of-market periods where the algorithm remains flat, waiting for statistical setup conditions to materialize rather than continuously taking market exposure.
Quality of historical results
Analyzing payoff metrics reveals strong statistical consistency across individual trade outcomes. The strategy achieves an average trade return of +6.40% and a median trade return of +6.34%. The near-perfect alignment between mean and median closed-trade expectations indicates that overall historical performance is driven by a uniform distribution of repeated gains rather than being skewed by extreme outliers. The single best trade returned +35.49%, while the worst single trade registered -25.70%. The profit factor of 7.22 is further reinforced by examining gross profit dispersion: the top three winning trades combined account for only 8.13% of total gross historical profit. This exceptionally low concentration metric confirms that the strategy systematic historical growth relies on structural edge distributed across dozens of trades rather than luck derived from a handful of hyper-profitable market events.
Risk, drawdown and losing behavior
Risk statistics demonstrate a localized risk profile characterized by rare losing sequences but non-trivial peak-to-trough drawdowns. The strategy maximum historical drawdown reached 38.42%. Given the high win rate of 84.06% and a longest losing streak of only 3 consecutive trades, this equity decline was not caused by prolonged, compounding loss series. Instead, drawdowns in this model stem from the mathematical interplay between position size dynamics and occasional deep adverse trades, as evidenced by the worst individual trade drawdown of -25.70%. In contrast, the strategy recorded a maximum consecutive winning streak of 26 trades. The sharp contrast between a 26-trade winning streak and a 3-trade losing streak illustrates that equity drawdowns tend to occur abruptly during localized market volatility spikes rather than through continuous decay.
Behavior through time and yearly stability
An examination of the yearly performance breakdown shows consistent historical profitability across varying market regimes from 2020 through early 2026. In 2020, during its initial test period, the strategy recorded 2 trades, both winning, yielding a sum of +31.09%. In 2021, trade volume expanded to 48 completed trades with an 85.42% win rate and a cumulative return sum of +375.10%. During the 2022 market downturn, the algorithm completed 22 trades with its lowest recorded annual win rate of 63.64%, yet still closed the year positive with a sum of +65.76%. Performance recovered in 2023 with 17 trades, an 82.35% win rate, and a +79.46% sum. In 2024, the strategy executed 27 trades with an 85.19% win rate and a +145.55% sum, followed by 2025 where all 21 completed trades were winners (100% win rate), adding a sum of +178.06%. The first trade of 2026 also closed in profit at +8.43%. The model maintained positive annual aggregate returns in every calendar year evaluated.
Recent period since 2024-06-01 versus full history
The evidence window covering completed trades with sell timestamps on or after June 1, 2024, provides a view of contemporary strategy behavior. During this recent sub-period, the strategy completed 33 trades, generating a cumulative return sum of +250.32%. This constitutes approximately 23.9% of all completed historical trades (33 out of 138) executed within roughly 31% of the overall 5-year evaluation window. The recent sample demonstrates robust execution rhythm, averaging over 1.7 trades per month during this timeframe. The high proportion of successful trade completions during recent market conditions confirms that the statistical dynamics observed in the full history have remained fully active without signs of decay or structural drop-off.
Strengths and limitations
The principal statistical strength of this trading model lies in its exceptional expectancy distribution, defined by an 84.06% historical win rate, a 7.22 profit factor, and balanced mean-to-median trade performance (+6.40% versus +6.34%). Its low profit concentration (top 3 winners at 8.13%) demonstrates broad reliance on systemic edge rather than outlier noise. However, material statistical limitations must be noted. With 138 total closed trades over 5.06 years, the sample size per year is relatively modest, averaging 27.25 trades annually. Additionally, the maximum historical drawdown of 38.42% and a worst-case single trade loss of -25.70% indicate that while losing trades are infrequent, individual loss events can exert meaningful downward pressure on historical equity.
DevioLab analytical conclusion
Earning a DevioLab score of 83.08 and ranking first for GRT, this quantitative strategy displays a well-documented historical profile characterized by high win frequency and disciplined trade selectivity. By combining a 15-minute analytical timeframe with multi-day position holding times (median 40.63 hours), the model filters out high-frequency noise while capturing medium-term price trends. Its risk profile is dominated by infrequent but moderately sharp drawdown events rather than prolonged losing streaks. Investors and quantitative analysts evaluating this strategy should weigh its strong profit factor and multi-year annual consistency against its modest trade count and peak historical equity drawdowns.
Data scope and methodology
This analysis is derived entirely from simulated backtested trade data for GRT (GRT) covering the timeframe from December 17, 2020, to January 9, 2026. All trade metrics, holding periods, profit factors, and drawdown statistics represent closed historical positions evaluated on a 15-minute timeframe using UTC timestamping. Recent metrics evaluated since June 1, 2024, reflect trades with completion dates on or after that threshold. Historical simulated backtest calculations do not represent real-time Binance account executions, do not account for unstated order slippage or variable commission structures, and provide no guarantee of future financial performance.