AIUSDT
Crypto market · BinanceAI · Quantitative Analysis of a High Win-Rate Swing System on the 15-Minute Timeframe
This analytical review examines the historical performance of the quantitative trading strategy designated as AI 215000 +5384.50% 1TRAD-DZJ6 on the AI crypto asset. Operating across a 1.77-year backtest window from January 4, 2024, to October 12, 2025, the strategy achieved a total cumulative profit metric of 5,384.50% across 67 completed trades, earning a DevioLab Score of 62.62 and ranking 6th for the asset. The central statistical character of this strategy is defined by an unusually high win rate of 83.58% paired with a modest profit factor of 1.11. This divergence highlights a structural asymmetry: while 56 out of 67 trades closed in profit, individual drawdown events—exemplified by a worst single trade of -44.28% and a matching maximum drawdown of 44.28%—drastically compress the net payoff efficiency. Position holding times average 84.08 hours, categorizing the model as a multi-day swing system executed on a 15-minute timeframe.
Analysis as of 23.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic model under evaluation, named AI 215000 +5384.50% 1TRAD-DZJ6, trades the AI asset within the cryptocurrency market environment. Executed on a 15-minute chart interval, the strategy was tested over a historical dataset spanning 1.77 years between January 4, 2024, and October 12, 2025. Across this period, the strategy logged 67 fully completed trades, generating a total compounded historical return statistic of 5,384.50% and an annualized percentage metric of 979.23%. These outcomes place the strategy at rank 6 for the AI ticker within the DevioLab framework, accompanied by an overall DevioLab Score of 62.62. A primary observation in the strategy profile is the pronounced contrast between cumulative gains and net efficiency. The system demonstrates a high hit rate, capturing positive returns on 56 of its 67 trades, representing an 83.58% win rate. However, the system registers a profit factor of 1.11, indicating that total gross profits exceed total gross losses by a narrow margin. This configuration reveals that the strategy relies on extreme directional precision to maintain overall profitability, as loss magnitudes systematically exceed average win magnitudes. The balance of statistics underscores a high-frequency entry precision model operating within a high-loss-impact risk envelope.
Trading rhythm and position duration
Despite utilizing a granular 15-minute candlestick chart for trade signal identification, the strategy operates as a low-frequency, multi-day swing trading system rather than a intraday scalper. Over the 1.77-year sample, the strategy completed 67 trades, translating to an average trading frequency of 37.79 trades per year, or approximately 3.05 trades per active month. Market exits are spaced significantly apart, exhibiting an average interval between exits of 9.76 days and a median interval of 6.90 days. Position duration statistics reinforce this swing-trading profile. The average holding duration for a completed trade is 84.08 hours (approximately 3.5 days), while the median holding duration stands at 56.75 hours (approximately 2.36 days). The divergence between median and average holding times indicates that while most trades resolve within two to three days, a subset of positions remains open for extended periods, pushing the arithmetic mean upward. The 15-minute timeframe serves primarily to fine-tune entry and exit execution points rather than to generate rapid, short-duration trades.
Quality of historical results
Evaluating trade quality requires examining the mathematical distribution of individual trade returns. The strategy delivered an average trade return of +7.34%, whereas the median trade return was notably higher at +9.25%. In typical quantitative distributions, a median exceeding the mean points to negative skewness: severe negative outlier trades drag down the average return despite a consistently favorable typical trade outcome. This negative asymmetry is visible when comparing extremes. The strategy's best single trade yielded a gain of +44.96%, whereas its worst trade suffered a loss of -44.28%. Because the worst loss almost perfectly mirrors the best win in magnitude, but winning trades outnumber losing trades 56 to 11, the positive drift of the portfolio is primarily driven by win frequency rather than an advantageous risk-reward payoff ratio per trade. Furthermore, gross profit is moderately well-distributed across the dataset; the top 3 winning trades account for 19.39% of total gross profits. This confirms that historical profitability was not manufactured by a single lucky outlier, but rather by a consistent stream of winning trades counterbalanced by deep occasional losses.
Risk, drawdown and losing behavior
The strategy's risk metrics demonstrate severe loss concentration. The maximum observed historical drawdown reached 44.28%, a value that matches the worst trade return of -44.28%. This exact numerical alignment suggests that peak equity impairment was caused directly by a single catastrophic trade event or a tight cluster of downside moves culminating in that loss, rather than a prolonged series of cascading losing trades. This interpretation is further supported by streak analysis. The strategy recorded a maximum losing streak of only 2 consecutive trades, compared to a maximum winning streak of 13 consecutive trades. Across 67 total trades, there were only 11 losses in total. The primary risk exposure for this strategy is not consecutive trade failure or low win probability, but rather tail-risk severity when a trade invalidates. Because individual losses can reach -44.28%, capital preservation relies heavily on preventing unmanaged tail risk from eroding accumulated gains.
Behavior through time and yearly stability
A multi-year examination demonstrates consistent win-rate stability across separate calendar periods, alongside variations in trade frequency and total return generation. In calendar year 2024, the strategy executed 42 trades, securing 35 wins and 7 losses, yielding an 83.33% win rate and a cumulative return sum of +338.56%. In 2025 (through October 12), the system executed 25 trades with 21 wins and 4 losses, registering an 84.00% win rate and a cumulative return sum of +153.35%. The hit rate remained stable between the two calendar periods, fluctuating by less than one percentage point (83.33% vs 84.00%). Trade frequency averaged 3.5 trades per month in 2024 and approximately 2.6 trades per month in 2025. Summed returns per trade averaged +8.06% in 2024 and +6.13% in 2025. This indicates that while signal accuracy remained uniform, trade opportunity density and per-trade yield moderated slightly in the latter period.
Recent period since 2024-06-01 versus full history
Analyzing the recent testing window from June 1, 2024, through October 12, 2025, provides insight into the strategy's recent behavior. During this 16.4-month subset, the strategy completed 49 trades, achieving a cumulative summed return of +300.27%. This recent sample accounts for 73.13% of all 67 historical trades generated since January 2024 and 60.91% of the total uncapped sum of percentages. The concentration of trade activity in the post-June 2024 period confirms that the historical dataset is predominantly weighted toward recent market dynamics. The average trade sum within this recent window was +6.13%, slightly below the full-history average trade yield of +7.34%. This demonstrates that while trade generation accelerated in the recent period, individual trade gains were somewhat lower on average than those recorded during early 2024.
Strengths and limitations
The primary analytical strengths of the strategy include: - Exceptional historical accuracy, featuring an 83.58% win rate across 67 completed trades. - Minimal losing streak exposure, capped at a maximum of 2 consecutive losing trades. - Broadly distributed gross profit structure, where the top 3 winning trades account for less than 20% (19.39%) of gross profit. - Stable multi-year hit rate consistency across 2024 (83.33%) and 2025 (84.00%). The fundamental analytical limitations include: - Highly asymmetrical payoff structure, where individual losses (-44.28%) far exceed typical win gains, depressing the profit factor to 1.11. - Elevated historical drawdown risk (44.28%), matching the maximum trade loss severity. - Moderate overall sample size (67 trades over 1.77 years), which limits statistical power for long-term stress testing.
DevioLab analytical conclusion
The quantitative evaluation of AI 215000 +5384.50% 1TRAD-DZJ6 reveals a specialized swing trading model operating on a 15-minute bar structure. Its performance model is built on high directional hit rates (83.58%) and short losing series, enabling significant cumulative backtested expansion (+5,384.50%). It achieves a DevioLab Score of 62.62 and ranks 6th for the AI asset. However, the low profit factor of 1.11 and the high peak drawdown of 44.28% highlight structural vulnerability to large individual losses. For quantitative refinement, focus would center on risk containment mechanisms aimed at capping downside tail severity without disrupting the strategy's core entry precision.
Data scope and methodology
This analysis is based entirely on backtested performance statistics generated for the AI asset (AI) over the historical period from January 4, 2024, to October 12, 2025 (1.77 years). All reported figures describe closed, completed simulated trades executed on a 15-minute timeframe. Summed returns, profit factors, and drawdown statistics reflect mathematical backtest output based on historical price data. These statistics do not account for real-world order execution variables such as exchange fees, slippage, latency, or liquidity constraints. Historical backtested results are non-predictive indicators and do not guarantee future performance outcomes. This report is strictly for quantitative research purposes and does not constitute financial advice.