AXSUSDT
Crypto market · BinanceQuantifying AXS Swing Execution: High Win-Rate Structure and Drawdown Asymmetry in AXS · AXS Quantitative Strategy
This analytical report provides an in-depth quantitative evaluation of the 15-minute swing trading model developed for AXS. Spanning a 5.62-year backtested historical dataset from November 2020 to June 2026, the strategy executed 229 closed trades, achieving a 70.31 percent win rate, a profit factor of 1.88, and an annualized return metric of 211.93 percent. Despite a modest trade frequency of 40.72 trades per year and a remarkably low gross profit concentration—where the top three winning trades account for just 14.25 percent of total gross profits—the model exhibits a significant structural risk profile. A maximum drawdown of 52.84 percent and a worst single trade loss of -38.20 percent highlight a pronounced risk-reward asymmetry where infrequent but severe losses penalize equity curves. Evaluated across varying market regimes, including a robust recent testing window since June 1, 2024 yielding 47 trades and a +153.96 percent combined sum, the strategy receives a DevioLab score of 43.45, ranking 7th among tested strategy profiles for this asset.
Analysis as of 22.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative strategy identified as AXS 215000 +3836329.41% 1TRAD-CQM5 evaluates 15-minute price data for AXS over an expansive historical window of 5.62 years, beginning November 4, 2020, and extending through June 20, 2026. Across this multi-year observation period, the model closed 229 completed trades, generating an aggregate historical trade return sum of 4,021,957.02 percent and an annualized return performance figure of 211.93 percent. Within the DevioLab quantitative evaluation framework, the strategy achieves a score of 43.45, placing it 7th among comparative strategy configurations evaluated for AXS. The strategy operates on a granular 15-minute sampling bar, yet its statistical fingerprint reflects multi-day positioning rather than short-term noise capture. By maintaining a high historical hit rate of 70.31 percent across 161 winning trades against 68 losing trades, the system establishes a broad operational edge. However, the quantitative structure displays a noticeable tension between high trade precision and downside vulnerability, as evidenced by a profit factor of 1.88 paired with a peak-to-trough historical maximum drawdown of 52.84 percent. The baseline architecture demonstrates an ability to navigate distinct crypto cycles, maintaining positive performance across bull, bear, and consolidation phases while keeping overall signal generation constrained to high-conviction setup windows.
Trading rhythm and position duration
Despite utilizing a 15-minute bar interval for tactical signal monitoring, the strategy demonstrates the structural characteristics of a patient swing trading system. Across the 229 historical trades, the model averaged a position holding duration of 107.25 hours, approximately 4.47 days, while the median holding time stood at 65.50 hours, or roughly 2.73 days. This divergence between execution timeframe and holding duration indicates that the system uses 15-minute data to refine trade timing rather than to engage in rapid, short-term turnover. The overall trading frequency is disciplined and selective, averaging 40.72 trades per year, which translates to approximately 3.69 trades per active month. Spacing between position exits further illustrates this deliberate pacing, with an average of 8.83 days between trade exits and a median interval of 5.93 days. The strategy does not overtrade or generate excessive signal noise; instead, it enters market positions sparingly and allows capital to remain deployed across multi-day price swings. This low trade frequency reduces exposure to market noise during choppy conditions, though it also means the strategy relies heavily on the quality and risk management of each individual trade event.
Quality of historical results
The statistical distribution of trade returns reveals a robust and well-distributed edge across winning positions. The strategy achieved an average trade return of +6.02 percent, closely mirrored by a median trade return of +5.06 percent. The proximity between mean and median expectations indicates that overall historical returns are structurally healthy and not distorted by extreme mathematical skewness. A key strength of the profile is its minimal profit concentration: the three largest winning trades generated only 14.25 percent of total gross profits. This demonstrates that performance is broad-based, reliance on singular runaway winners is low, and positive equity drift is driven by repeatable statistical iteration. The single best historical trade yielded +103.56 percent, providing substantial tail upside, while the profit factor of 1.88 underscores that total gains comfortably exceeded total losses across the sample. The strategy demonstrated impressive directional consistency, reaching a maximum winning streak of 15 consecutive positive trades. When evaluating payoff quality alongside the 70.31 percent win rate, the historical data indicate a system designed to systematically extract moderate, repeatable gains from directional asset moves.
Risk, drawdown and losing behavior
While the strategy boasts a high hit rate and extended winning streaks, its risk dynamics expose structural downside vulnerabilities. The primary quantitative constraint of the system is a maximum historical drawdown of 52.84 percent. This substantial drawdown occurred despite a remarkably constrained maximum losing streak of just 3 consecutive trades. The core tension within this risk profile stems from loss magnitude rather than loss frequency. The single worst historical trade produced a severe contraction of -38.20 percent, indicating that when market positions break against the system, individual losses can penetrate deeply before risk controls or exit conditions trigger. Because the strategy averages a gain of +6.02 percent per trade, recovering from a single tail-risk drawdown event requires multiple consecutive winning cycles. The balance between a 70.31 percent win rate and a worst-case loss of -38.20 percent creates an asymmetrical loss curve where a small cluster of adverse trades can inflict disproportionate damage on historical balance curves. Managing position sizing and monitoring downside open-risk duration represent critical factors when interpreting this strategy's historical drawdowns.
Behavior through time and yearly stability
An examination of yearly performance metrics demonstrates notable consistency across diverse market environments, alongside evolving trade frequency. In 2020, during the initial sample window, the strategy recorded 2 trades, both winning, for a sum return of +125.41 percent. The strategy experienced its highest activity in 2021, completing 70 trades with an 81.43 percent win rate and a cumulative return sum of +865.06 percent. During the challenging crypto market conditions of 2022, signal generation moderated to 60 trades while maintaining a positive closed trade sum of +63.63 percent and a 60.00 percent win rate. In 2023, trade frequency adjusted to 38 trades, yielding a 71.05 percent win rate and a +147.96 percent sum. This stable trend continued into 2024 with 35 trades, a 65.71 percent win rate, and +94.15 percent in combined gains. In 2025, 20 trades were closed with a 65.00 percent win rate and +56.96 percent sum, followed by 2026 showing 4 trades, a 75.00 percent win rate, and +26.10 percent sum. The annual data show that while trade frequency peaked early in the dataset, win rates remained consistently above 60 percent every calendar year, reflecting broad operational stability across changing volatility regimes.
Recent period since 2024-06-01 versus full history
Analyzing recent performance from June 1, 2024, through the end of the historical sample offers valuable insight into the strategy's current behavioral persistence. During this recent window, the strategy executed 47 completed trades, delivering a combined return sum of +153.96 percent. This recent sample accounts for over 20 percent of all historical trades completed by the system, confirming that signal generation has remained active and statistically functional in recent market conditions. Comparing the recent window to the full 5.62-year historical average reveals that the strategy's return cadence and signal selectivity have remained intact. The recent average sum per trade aligns closely with full-history baseline expectations, showing no evidence of edge decay or strategy breakdown. The persistence of active trade setups and positive accumulated returns during this recent period provides empirical confirmation that the algorithm's core parameters continue to capture structural price movements in AXS without requiring structural readjustment.
Strengths and limitations
The primary strength of this quantitative strategy lies in its historical accuracy and gain distribution. A win rate of 70.31 percent, supported by a maximum winning streak of 15 trades and a low top three profit concentration of 14.25 percent, establishes that the historical returns are broad-based and statistically robust across time. Furthermore, the model has delivered positive closed trade sums across every calendar year analyzed, demonstrating adaptability across changing crypto cycles. Conversely, the strategy's major limitation rests in its downside tail risk. A maximum drawdown of 52.84 percent combined with a single worst trade of -38.20 percent highlights significant loss severity when trade invalidation occurs. Additionally, with an average trading frequency of only 3.69 trades per month and average holding times exceeding 4 days, the strategy requires substantial patience and capital deployment across multi-day horizons. Traders evaluating this profile must weigh the benefits of high execution accuracy against the psychological and mathematical demands of enduring deep historical peak-to-trough drawdowns.
DevioLab analytical conclusion
The AXS 15-minute swing strategy represents a highly accurate, selective quantitative model with clear performance trade-offs. Its DevioLab score of 43.45 and rank of 7th for AXS reflect a system that excels at capturing multi-day trend movements while maintaining high win consistency across multiple market phases. The statistical profile is characterized by healthy organic return symmetry, evidenced by an average trade of +6.02 percent and a median trade of +5.06 percent, alongside minimal reliance on outlier winners. However, the score is constrained by downside structural risk, specifically the 52.84 percent maximum drawdown and asymmetrical tail losses. For quantitative analysts, the strategy serves as an insightful example of how high win rates and low profit concentration can coexist with material equity volatility. Overall, the historical dataset highlights a persistent mechanical edge, provided the underlying risk profile and holding duration requirements align with broad operational constraints.
Data scope and methodology
The statistics cited throughout this report reflect backtested closed trade performance derived from historical 15-minute market data for AXS between November 4, 2020, and June 20, 2026. All trade metrics, including win rates, average trade percentages, profit factors, holding durations, and drawdown figures, are calculated strictly from completed simulated trades. Summed percentage figures represent the linear addition of individual trade returns across closed positions. The results presented herein do not account for real-world execution factors such as order book slippage, exchange fee structures, variable leverage, or live order routing latency. These historical backtested findings are presented purely for quantitative research purposes and do not represent guaranteed future performance or direct financial advice.