WLDUSDT
Crypto market · BinanceWLD Quantitative Backtest Analysis: Evaluating High Win Rate and Recent Inactivity in 15-Minute Strategy Performance
This analytical report examines the historical performance of the core 15-minute quantitative strategy for WLD across a 3.08-year sample period from July 2023 to August 2026. The strategy demonstrates a notable 74.41% win rate across 211 completed trades, accumulating a total simulated profit of 14,129.81%. The statistical profile reveals a highly distributed win distribution, where the top three winning trades account for only 9.33% of gross profit, and an average trade expectancy of 2.77%. However, severe analytical tensions exist within the statistical sample. The profit factor is recorded at 0.63, reflecting a structural disparity between uncompounded loss magnitudes and average trade gains, headlined by a worst-case single trade loss of -22.12%. Furthermore, the strategy exhibits complete inactivity in the recent observation window, recording zero trades since June 1, 2024. Ranking 6th for the asset with a DevioLab score of 74.33, this strategy presents a complex quantitative profile characterized by strong hit-rate consistency alongside major structural risk considerations.
Analysis as of 22.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative strategy designated for WLD on the 15-minute timeframe represents a core systematic model designed to capture directional moves in digital asset markets. Evaluated over a continuous historical backtest window of 3.08 years running from July 24, 2023, to August 20, 2026, the strategy generated 211 completed trades. Over this sample period, the strategy achieved a cumulative simulated profit of 14,129.81% with an annualized return figure of 2,913.95%. Within the DevioLab quantitative evaluation framework, the strategy receives an overall DevioLab score of 74.33, placing it 6th in rank among evaluated algorithmic configurations for WLD. The strategy is categorized under the core selection model, reflecting its position as an independent top-performing candidate within the ticker dataset. The primary performance engine relies on high trade consistency, evidenced by 157 winning outcomes against 54 losing outcomes. This statistical profile highlights a model built around frequent positive trade resolution, though full interpretation requires examining how individual trade payoffs and risk parameters interact over time.
Trading rhythm and position duration
Across its 3.08-year backtest history, the strategy completed 211 trades, translating to an average annual trade frequency of 68.58 trades per year. On a monthly basis, this equates to approximately 5.7 closed positions per month, indicating a selective trading cadence on the 15-minute chart rather than a high-frequency churning model. The quantitative dataset does not provide explicit numerical metrics for average holding hours, median holding hours, or days between trade exits, leaving specific duration bounds unquantified in the raw schema. However, the combination of a 15-minute execution interval and an annual pace of 68.58 trades indicates that position entries are highly filtered. The strategy does not constantly hold active positions, instead remaining dormant until specific quantitative threshold conditions are fulfilled. When trades are triggered, the selective frequency suggests positions are held long enough to realize multi-bar directional trends across short-term horizons before reaching exit criteria.
Quality of historical results
The strategy demonstrates robust statistical accuracy, achieving a win rate of 74.41%. Out of 211 completed trades, 157 ended in positive territory while 54 resulted in losses. Expectancy per trade is solid, with an average trade return of 2.77% and a median trade return of 2.98%. The proximity between the average and median trade metrics indicates a symmetric return distribution among typical trades, free from massive central skew. Furthermore, the concentration of profits is remarkably broad: the top three winning trades collectively generated only 9.33% of total gross profits. This confirms that the strategy historical growth was driven by repetitive, moderate gains across dozens of trades rather than a few lucky outlier spikes. The best single trade achieved a gain of 35.32%. However, a critical quantitative tension appears in the profit factor metric, which stands at 0.63. A profit factor below 1.00 in raw uncompounded percentage terms indicates that uncompounded gross losses exceed uncompounded gross gains, despite the high win rate and positive average trade. This mathematical divergence highlights that when losses occur, their percentage magnitude relative to capital allocation can be substantially larger than typical gains, creating asymmetric downside pressure during losing cycles.
Risk, drawdown and losing behavior
Risk management metrics show a maximum historical drawdown of 25.83%. In the context of a overall cumulative return of 14,129.81%, a maximum equity retracement under 26% demonstrates significant peak-to-trough resilience during adverse market phases. The strategy benefits from tight losing streak controls, recording a maximum consecutive losing streak of just 3 trades. In contrast, the maximum consecutive winning streak reached 15 trades, illustrating extended periods of equity curve expansion. However, tail risk is distinctly present in individual trade losses. The worst single trade in the backtest registered a loss of -22.12%. Because the average winning trade is far smaller than this worst-case loss, a single severe trade failure can erase the gains of multiple preceding winning trades. The low maximum losing streak of 3 trades is the key factor that prevented deep multi-trade drawdowns, as the strategy rarely experienced clustered losses.
Behavior through time and yearly stability
The overall backtest spans 3.08 calendar years starting in July 2023 and ending in August 2026. Within the supplied source data, granular yearly breakdown tables are omitted, meaning specific year-by-year trade counts, annual win rates, and yearly net sum percentage returns cannot be independently itemized. Consequently, evaluating temporal stability relies on comparing total full-history metrics against the recorded activity timelines. The execution history shows that the bulk of the 211 completed trades occurred in the earlier portion of the sample. The strategy demonstrated strong execution clustering during its initial active phases, generating the majority of its 157 winning trades during periods of aligned asset volatility. The absence of yearly sub-aggregations requires quantitative analysts to evaluate the strategy as a unified structural dataset rather than a consistent year-over-year annuity.
Strengths and limitations
The primary strength of the strategy lies in its elevated hit rate of 74.41% and a well-distributed win structure where the top three trades represent less than 10% of gross profits. A high median trade of 2.98% confirms consistent positive trade-level expectancy, while a low maximum losing streak of 3 trades limits severe equity compounding downside. Conversely, the strategy faces substantial limitations. The recorded profit factor of 0.63 underscores an underlying loss severity issue, exemplified by a worst single trade loss of -22.12%. This asymmetry means losses can be disproportionately damaging relative to average gains. Additionally, the primary limitation for live quantitative evaluation is the complete absence of trading activity since June 1, 2024. Without recent completed trades, the model lacks contemporary empirical validation. Finally, missing granular holding times and yearly sub-period breakdowns constrain deeper microstructural analysis.
DevioLab analytical conclusion
The strategy earns a DevioLab score of 74.33 and ranks 6th among WLD quantitative models. The historical data demonstrates an impressive capability to build capital through high-probability directional entries on the 15-minute chart, producing 14,129.81% total historical profit with a modest 25.83% maximum drawdown. Its non-reliance on mega-winner outliers makes the historical win rate statistically attractive. However, quantitative research demands caution regarding the strategy structural tradeoffs. The profit factor of 0.63 and worst trade loss of -22.12% reveal that loss management relies heavily on keeping consecutive losses rare rather than keeping individual loss sizes small. Furthermore, the complete trade dormancy since mid-2024 means the strategy historical success remains unverified in recent asset regimes. Institutional and systematic researchers should weigh these statistical tradeoffs carefully when evaluating the model historical backtest profile.
Data scope and methodology
This quantitative evaluation is strictly based on backtested performance statistics generated for WLD on the 15-minute timeframe over the sample period from July 24, 2023, to August 20, 2026. All reported metrics, including completed trades, win rates, drawdowns, and return percentages, reflect simulated historical trade outputs under static model logic. The metrics do not represent real-time live trading results, actual account equity curves, or guaranteed future returns. Execution variables such as order book slippage, exchange fee structures, market impact, and funding rates are not factored into the source metrics unless explicitly noted. Historical strategy testing provides empirical insights into systematic logic behaviors across past market regimes but should never be interpreted as financial advice or a forecast of future performance.