COMPUSDT
Crypto market · BinanceCOMP Quantitative Strategy Analysis: Evaluating High Hit-Rate Dynamics and Drawdown Asymmetry
This quantitative evaluation examines an algorithmic strategy engineered for COMP on the 15-minute timeframe over a 6.11-year evaluation period spanning July 2020 through August 2026. Generating 463 completed historical trades, the model demonstrates an exceptionally high win rate of 73.22% coupled with a Profit Factor of 3.33. Crucially, statistical analysis reveals that profitability is non-concentrated, with the three largest winning trades accounting for just 7.07% of total gross profit. However, the system exhibits notable tail-risk vulnerability, characterized by a maximum drawdown of 49.29% and a worst single trade loss of -31.93%. This report analyzes how high transaction consistency, modest holding durations averaging 38.65 hours, and strong recent trade volume since June 2024 intersect to define the strategy's DevioLab Score of 69.36.
Analysis as of 26.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The strategy under review operates on COMP within a 15-minute price evaluation interval, spanning a continuous 6.11-year historical dataset from July 7, 2020, through August 17, 2026. Across this multi-year evaluation window, the algorithm generated 463 completed historical trades, comprising 339 winning positions and 124 losing positions. This trade distribution yields an overall historical win rate of 73.22%. The strategy achieved a DevioLab Score of 69.36, placing it at rank 2 among strategies evaluated for COMP on the platform. Over the full history, the cumulative sum of closed trade returns reached 87,440.39%, translating to an annualized return metric of 1,141.75%. The profit factor stands at 3.33, demonstrating that historical gross gains substantially outweighed gross losses. These top-level metrics establish a baseline profile of high transaction accuracy, though comprehensive evaluation requires examining how performance is distributed across time, position durations, and tail losses.
Trading rhythm and position duration
The strategy executes with a measured structural rhythm that balances active intraday monitoring on the 15-minute timeframe with multi-day position holding times. Over the 6.11-year test period, the algorithm averaged 75.74 trades per year, which corresponds to approximately 6.43 completed trades per active month. Position duration metrics indicate that trades are not brief scalps; the average holding time across all completed trades is 38.65 hours, while the median holding duration is 23.75 hours. This divergence between average and median holding times indicates that while half of all positions close within approximately one calendar day, a subset of trades remains open over multiple days to allow trend development. Between trade exits, the strategy exhibits an average spacing of 4.84 days and a median spacing of 3.35 days. This temporal spacing confirms that the algorithm spends considerable time out of the market, waiting for specific quantitative criteria to register on the 15-minute timeframe rather than maintaining continuous market exposure.
Quality of historical results
A granular examination of trade returns highlights a remarkably robust statistical distribution. The strategy's median trade return of +2.48% exceeds its average trade return of +1.88%. In quantitative trading, when the median outcome is higher than the mean outcome, it signifies that the strategy's overall expected value is driven by consistent core wins rather than being artificially inflated by a handful of extreme positive outliers. This interpretation is powerfully reinforced by the concentration metric: the top three winning trades combined account for only 7.07% of the strategy's total gross profit. Even though the best single trade achieved an impressive gain of +53.42%, the overwhelming majority of accumulated gains stem from the repeated realization of moderate winning trades across the 339 profitable events. The strategy's profit factor of 3.33 reflects an efficient ratio of gross profits to gross losses, supported by an exceptionally long maximum winning streak of 16 consecutive trades.
Risk, drawdown and losing behavior
Despite an impressive 73.22% win rate and long winning streaks, the strategy contains distinct historical risk characteristics that highlight asymmetric downside exposure. The maximum historical peak-to-trough drawdown reached 49.29%. This substantial capital decline occurred despite the maximum losing streak being capped at just 4 consecutive trades. The primary structural driver of this drawdown risk is the magnitude of adverse exit events. While the average trade return is +1.88%, the strategy experienced a worst single trade loss of -31.93%. This shows a clear payoff asymmetry where individual losing positions can erode the equity accumulated from multiple small-to-moderate wins. Because losing streaks are short (never exceeding 4 consecutive trades historically), drawdowns are driven less by frequent losses and more by severe single-trade price movements against open positions. Managing capital during these periodic sharp drawdowns represents the primary operational challenge inherent to this statistical profile.
Behavior through time and yearly stability
Analyzing annual performance across the seven calendar years covered by the dataset demonstrates consistent positive profitability across varying crypto market conditions. In 2020, over 32 trades, the strategy posted a 78.13% win rate and a cumulative return sum of +111.69%. In 2021, trade frequency increased to 88 trades, generating a +201.98% return sum with a 70.45% win rate. During the 2022 market downturn, the algorithm maintained profitability across 99 trades—its highest annual trade volume—achieving a 68.69% win rate and a cumulative sum of +55.92%. Performance rebounded strongly in 2023 with 68 trades delivering a 76.47% win rate and a +127.99% return sum. Year 2024 marked the highest annual win rate at 80.00% across 80 trades, accumulating +178.78%. In 2025, 87 trades yielded a 71.26% win rate and +171.20% return sum. Finally, the partial year 2026 recorded 9 trades with a 66.67% win rate and +23.26% sum. The strategy produced positive cumulative return sums in every single calendar year, demonstrating structural resilience across both bull and bear historical cycles.
Recent period since 2024-06-01 versus full history
Evaluating performance in the recent sample period starting June 1, 2024, provides valuable confirmation regarding the strategy's ongoing statistical validity. Out of the total 463 completed historical trades, 147 trades completed on or after June 1, 2024. This recent window represents nearly 32% of all historical trades, indicating a high concentration of sample data in recent market regimes. Over these 147 recent trades, the strategy accumulated a combined return sum of +304.83%. Comparing this recent rate of return accumulation against the full history shows that performance has remained active and robust rather than decaying over time. The substantial trade count of 147 positions over approximately two years confirms that the execution triggers on the 15-minute timeframe continue to fire regularly, providing a statistically meaningful modern sample that aligns closely with the full multi-year backtested record.
Strengths and limitations
The strategy exhibits clear statistical strengths, most notably its high historical hit rate of 73.22%, a robust profit factor of 3.33, and an exceptionally broad profit distribution where the top three trades represent only 7.07% of total gross profit. Additionally, its positive return sums across every evaluated calendar year from 2020 through 2026 illustrate strong historical consistency across diverse volatility environments. Conversely, the primary limitation lies in its drawdown profile and negative payoff skew. A maximum drawdown of 49.29% combined with a worst trade loss of -31.93% demonstrates that losing positions, though infrequent, can cause significant equity retracements. Furthermore, because average holding durations extend to 38.65 hours while exit intervals average 4.84 days, the strategy requires patience during extended out-of-market periods and tolerance for multi-day position exposure.
DevioLab analytical conclusion
With a DevioLab Score of 69.36 and a rank of 2 for COMP strategies, this model represents a highly disciplined quantitative system characterized by outstanding historical hit rates and steady profit accumulation. Its high median trade return relative to its average trade return confirms that historical gains are broad-based rather than dependent on lucky black-swan winners. However, the score also reflects the quantitative friction introduced by a 49.29% maximum drawdown and severe worst-case trade losses. For quantitative analysts, the statistical profile suggests a strategy that reliably captures edge on the 15-minute timeframe, but requires rigorous risk management strategies to handle periodic deep equity drawdowns.
Data scope and methodology
All metrics in this report are derived strictly from closed historical trade simulation data for COMP on a 15-minute price evaluation interval between July 7, 2020, and August 17, 2026. The performance figures describe hypothetical closed trade returns and do not reflect live order execution, real-time order book slippage, exchange fee schedules, or active account management. The start date of the dataset represents the beginning of the historical backtest window and should not be interpreted as the underlying asset listing or creation date. Historical simulated performance is used exclusively for quantitative research and evaluation, and past statistical results do not guarantee future returns.