AIUSDT
Crypto market · BinanceHigh Win Rate and Controlled Drawdown in AI · AI Quantitative Strategy Analysis
This quantitative evaluation examines the historical performance of the algorithmic trading strategy AI 215000 +582.43% 1TRAD-QZU4 on the AI cryptocurrency asset across a 1.09-year evaluation window from January 4, 2024, to February 3, 2025. Evaluating 15-minute price data, the strategy exhibits an exceptionally high win rate of 93.33% across 15 completed trades, generating a total cumulative closed trade return of 582.43% and an annualized rate of 110.30%. The model demonstrated exceptional capital preservation with a maximum historical drawdown of just 0.89%, matching the exact magnitude of its single worst trade (-0.89%). Despite utilizing short-term 15-minute timeframe bars, the strategy operates as a low-frequency swing model with an average holding duration of 75.77 hours (approximately 3.16 days) and an average gap between trade exits of 28.29 days. With a profit factor of 7.81 and a DevioLab score of 60.02 (ranking 7th for the AI ticker), the system presents an unusually asymmetrical payoff profile, though its primary analytical limitation remains a compact statistical sample of 15 closed trades.
Analysis as of 22.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy under review, designated as AI 215000 +582.43% 1TRAD-QZU4, evaluates price structure on the 15-minute candlestick interval for the crypto asset AI (AI). Over a backtested sample period spanning 1.09 years from January 4, 2024, to February 3, 2025, the strategy executed a total of 15 completed trade cycles. Within the DevioLab quantitative evaluation framework, the strategy earned a score of 60.02, placing it 7th in rank among evaluated strategies for the AI asset. Over its complete backtested history, the strategy registered 14 winning trades against 1 losing trade, delivering a full-history cumulative closed trade return of 582.43% and an annualized baseline figure of 110.30%. A defining characteristic of this profile is the stark contrast between its underlying 15-minute price sampling frequency and its selective execution pacing. Rather than engaging in hyperactive intraday trading, the system functions as a highly restrained swing mechanism that filters out market noise, entering trades only under specific historical price conditions.
Trading rhythm and position duration
The temporal mechanics of the strategy demonstrate a distinct multi-day swing profile. Across the 15 completed positions, the average holding duration was 75.77 hours (roughly 3.16 days), while the median holding duration stood at 54.25 hours (approximately 2.26 days). This distribution indicates that while a majority of trades resolved within two to three days, several positions extended longer to capture multi-day trend movements. In terms of trade pacing, the model generated an average of 13.80 trades per year, or approximately 1.50 trades per active month. The spacing between trade exits was similarly extended, registering an average of 28.29 days between exits and a median interval of 14.39 days. The divergence between the median exit interval (14.39 days) and average exit interval (28.29 days) suggests periods of clustered execution followed by prolonged inactive observation windows. The strategy's operational profile shows that while entry timing is calculated from high-resolution 15-minute data, individual exposure is carried across multi-day horizons rather than closed within single sessions.
Quality of historical results
The statistical quality of the strategy's closed trade record is highlighted by a 93.33% historical win rate, representing 14 profitable trades out of 15 overall executions. The strategy recorded a profit factor of 7.81, reflecting a strong dominance of total gross gains over total gross losses. The mean trade return across the sample was 14.25%, closely aligned with the median trade return of 15.79%. This close alignment between mean and median trade returns indicates a remarkably uniform distribution of profitability among winning trades, rather than a profile driven by a single lucky gain. The strategy's best single trade produced a gain of 31.46%, while its worst single trade resulted in a modest loss of -0.89%. Profit concentration statistics further corroborate this balance: the three largest winning trades accounted for 36.93% of total gross profits. This moderate concentration level demonstrates that performance was distributed broadly across the 14 winning trades rather than over-concentrated in a tiny handful of anomalous spikes.
Risk, drawdown and losing behavior
From a risk perspective, the strategy's historical metrics demonstrate exceptional downside confinement. The maximum recorded historical peak-to-trough drawdown was 0.89%, which precisely equals the loss registered on its single historical losing trade (-0.89%). Because 14 of the 15 executed trades were closed in profit, equity drawdowns were minimal and brief. The strategy maintained a maximum winning streak of 13 consecutive profitable trades, while its maximum losing streak was restricted to a single trade. The historical payoff ratio between the average trade (+14.25%) and the single worst trade (-0.89%) displays a powerful positive asymmetry. However, from a quantitative analysis perspective, this near-zero historical drawdown profile must be interpreted alongside the sample size. With only 15 historical executions, the model has not yet encountered a broad array of severe market stress environments, meaning future drawdown depth could exceed historical bounds if adverse regime shifts occur.
Behavior through time and yearly stability
Examining yearly statistical breakdowns illustrates how historical closed trade gains accumulated across different calendar periods. In calendar year 2024, which covered nearly the entirety of the evaluation window, the strategy executed 14 trades, securing 13 wins and 1 loss for a 92.86% win rate and a cumulative trade sum of 182.29%. In early 2025 (up to February 3, 2025), the strategy completed 1 trade, which was a winning trade yielding a 31.46% return—representing the single best trade in the strategy's entire historical log. The complete full-history cumulative closed trade return of 582.43% reflects compounding effects across closed positions relative to simple trade additions. The yearly data demonstrate consistent win rate performance across both calendar years, showing that the strategy's high-precision filter maintained its structural efficacy across different market phases during the 1.09-year observation period.
Recent period since 2024-06-01 versus full history
Evaluating performance strictly within the recent window starting June 1, 2024, provides insight into the strategy's operational stability over recent market conditions. Between June 1, 2024, and February 3, 2025, the strategy completed 6 trades, generating a summed trade return of 84.00%. These 6 recent trades constitute 40.00% of the strategy's full-history trade count (15 trades). The average closed trade return during this recent sub-period was 14.00%, matching the full-history average trade return of 14.25% almost exactly. Pacing in the recent window remained consistent with the long-term baseline, averaging slightly under one trade per month. The alignment between recent performance metrics (84.00% total gain across 6 trades) and broader full-history metrics indicates that the strategy did not experience performance decay or operational degradation in recent months.
Strengths and limitations
The primary analytical strength of this strategy lies in its outstanding trade efficiency and loss containment. A 93.33% historical win rate paired with a 7.81 profit factor and an upper drawdown bound of just 0.89% demonstrates an asymmetrical trade architecture where downside risk on individual positions was kept remarkably small relative to target returns. Furthermore, profit distribution statistics (36.93% top-three winner concentration) confirm that gains were broadly distributed. Conversely, the strategy's primary limitation is its compact statistical sample. With only 15 total trades completed over 1.09 years, the sample size introduces statistical variance, making it difficult to guarantee that the 93.33% hit rate reflects a permanent edge rather than a favorable historical sample period. Additionally, the low execution frequency (13.80 trades per year) requires long holding times and patience, as weeks can pass between trade setups.
DevioLab analytical conclusion
The quantitative assessment of the strategy AI 215000 +582.43% 1TRAD-QZU4 highlights an effective swing trading architecture tailored for the AI crypto market. By combining a high-resolution 15-minute price input bar with strict filters, the model achieves multi-day holding periods (median 54.25 hours) with disciplined exit timing. Its DevioLab score of 60.02 and rank of 7th for the asset accurately reflect the dual nature of its profile: exceptional historical profitability (+582.43% total closed profit) and exemplary risk containment (0.89% max drawdown), balanced against the statistical caution warranted by a 15-trade dataset. For quantitative observers, the strategy represents a compelling historical model of high-selectivity swing trading, though ongoing monitoring across a larger trade volume is required to confirm long-term statistical stability.
Data scope and methodology
This analysis is based entirely on historical backtested trade statistics generated for the cryptocurrency asset AI (AI) on a 15-minute timeframe over the historical period from January 4, 2024, to February 3, 2025. All reported percentages represent closed trade results from simulated quantitative strategy execution. The statistics evaluated do not include exchange trading fees, order book slippage, execution delays, or margin funding rates. Historical backtested performance is presented strictly for academic research and strategy evaluation purposes and does not guarantee future operational performance or constitute financial advice.