EDENUSDT
Crypto market · BinanceQuantitative Analysis of EDEN 15-Minute Algorithmic Strategy: High-Payload Swing Dynamics Across a Compact Backtest Sample
This quantitative study evaluates the historical backtest performance of the core algorithmic strategy for EDEN on the 15-minute timeframe. Over a recorded evaluation window spanning September 30, 2025, to December 16, 2025, the model achieved a 100 percent win rate across 7 completed trades, generating an aggregate cumulative profit of 625.14 percent and a DevioLab score of 74.14, ranking first for the asset. Operating with an average position holding duration of 93.04 hours and an average trade frequency of 2.33 executions per active month, the strategy exhibits multi-day swing trading characteristics executed from intraday price data. While the historical statistical profile displays a zero percent maximum drawdown and a profit factor of 5.83, analytical inspection reveals significant return skewness: the top three winning trades account for 69.30 percent of gross profit, and the sample size remains limited to 7 trade events over 0.21 years. This report examines the interplay between holding rhythms, return concentration, and sample constraints to contextualize the strategy's historical performance.
Analysis as of 27.08.2026. Strategy data is updated separately.Read full analysis
Strategy Profile
The quantitative strategy designated EDEN 215000 +625.14% 1TRAD-ZEF3 operates on the 15-minute candle interval for the EDEN crypto market. Within the DevioLab quantitative taxonomy, this model holds core status and ranks first for the EDEN ticker, earning a composite DevioLab score of 74.14. The historical backtest dataset covers a focused calendar duration of approximately 0.21 years, commencing on September 30, 2025, and concluding on December 16, 2025. Across this window, the algorithm executed and closed a total of 7 trades. Aggregate cumulative performance reached 625.14 percent, matching its calculated annualized rate given the 0.21-year backtest boundary. The performance baseline reflects an un-leveraged, closed-trade output across a selective sequence of market entries. Because the evaluation history is compressed into a short chronological frame, every completed trade carries meaningful weight in shaping the overall statistical profile of the strategy.
Trading Rhythm and Position Duration
Although the algorithm samples market structure on a 15-minute timeframe, its operational rhythm is characterized by low transaction frequency and multi-day position holding times. Over the backtest history, the strategy averaged 33.11 trades per year on an annualized basis, translating to approximately 2.33 closed trades per active month. The mean duration of an open position was 93.04 hours (approximately 3.88 days), while the median holding time stood at 29.00 hours (roughly 1.21 days). The structural distance between the mean and median holding times indicates that while several trades concluded within slightly more than a day, selected positions remained open for multiple days to capture extended move structures. Spacing between position exits further illustrates this deliberate pacing: the strategy recorded an average of 12.46 days between consecutive trade exits, with a median exit interval of 8.83 days. This confirms that the model does not engage in rapid intraday scalping, but rather uses 15-minute price data to trigger selective, multi-day swing entries while remaining out of the market for multi-day stretches between signals.
Quality of Historical Results
The closed-trade record reveals an extraordinary hit rate of 100 percent, with all 7 completed trades generating positive returns. Across these trades, the mean gain per position was 35.12 percent, whereas the median trade yield was 27.11 percent. The spread between the mean and median trade metrics signals a right-skewed distribution where performance is buoyed by exceptional single-trade gains. The strategy's best single trade yielded 88.44 percent, while its least profitable trade still recorded a positive return of 8.36 percent. The model achieved a profit factor of 5.83. Further examination of gain distribution shows that the three largest winning trades generated 69.30 percent of the total cumulative gross profit. This concentration demonstrates that while every trade in the sample closed in positive territory, the bulk of the strategy's financial output was driven by a minority of high-impact expansions. Payoff quality is therefore defined by a combination of broad baseline profitability and heavy reliance on outlier moves.
Risk, Drawdown and Losing Behavior
Because all 7 recorded trade executions resulted in positive closed returns, the strategy displays a historical closed-trade maximum drawdown of 0.00 percent and a longest losing streak of 0 trades. The longest winning streak encompasses the entire trade log at 7 consecutive positive exits. From a statistical standpoint, a zero percent peak-to-trough equity drawdown on closed trades represents an optimal outcome for the recorded sample. However, quantitative analysis requires contextualizing this figure within sample size constraints. In an empirical sample of 7 trades, the absence of losing exits does not eliminate underlying risk; rather, it indicates that during this specific 0.21-year period, market conditions aligned favorably with the strategy's exit criteria. The worst individual trade return of 8.36 percent establishes that even the weakest setup in the dataset achieved profitability. Nevertheless, potential equity fluctuations during open trade holding periods (intra-trade unrealized drawdown) are not captured in closed-trade metrics and must be accounted for when evaluating overall risk exposure.
Behavior Through Time and Yearly Stability
The backtest history for this strategy is entirely contained within the year 2025, spanning from late September to mid-December. As a result, multi-year stability and cross-regime consistency cannot be directly evaluated from historical data. In the 2025 calendar year segment, the strategy completed 7 trades, achieving 7 wins and 0 losses for a cumulative return sum of 245.86 percent across the raw trade additions. The lack of multi-year historical data represents a specific analytical constraint: the model has not been observed through multi-year market cycles, extended bearish trends, or macro regime changes in the underlying asset. The recorded temporal behavior demonstrates high capital efficiency during the active late-2025 window, but the broader stability of its statistical parameters over multi-year horizons remains unconfirmed by the present dataset.
Recent Period Since 2024-06-01 Versus Full History
Evaluating performance in the recent period beginning June 1, 2024, reveals complete overlap with the full backtest history. Because the strategy's dataset begins on September 30, 2025, all 7 recorded trade executions fall within the post-June 2024 window. Consequently, the recent performance window reflects identical operational statistics to the full history: 7 completed trades, 7 winning trades, a 100 percent win rate, and a summed trade profit of 245.86 percent. There is no variance or performance decay between historical baseline figures and recent performance metrics because the dataset exists entirely within the recent window. While this ensures that the reported results reflect recent market behavior, it also emphasizes that the entire quantitative profile rests upon a single, continuous quarter of trading activity.
Strengths and Limitations
The primary strength of the EDEN 15-minute strategy lies in its historical efficiency and gain magnitude. Achieving a 100 percent win rate, an average trade return of 35.12 percent, and a profit factor of 5.83 within a 0.21-year span reflects strong alignment with asset momentum during the tested window. Furthermore, the low trading frequency of 2.33 executions per month and an average holding period of 93.04 hours limit operational complexity compared to high-frequency intraday models. Conversely, the strategy's main limitation is its compact sample size. With only 7 completed trades in the entire evaluation history, key statistical metrics carry elevated confidence intervals. Additionally, the concentration of 69.30 percent of gross profit within the top three winning trades underscores vulnerability to setup frequency: if market conditions fail to produce large trend expansions, average trade expectation could shift significantly. Finally, the absence of data prior to September 2025 leaves long-term structural resilience untested.
DevioLab Analytical Conclusion
The EDEN 215000 +625.14% 1TRAD-ZEF3 strategy represents a highly effective, high-payload swing model within its historical testing window. Ranking first for EDEN on DevioLab with a score of 74.14, the strategy demonstrates how 15-minute price action can be harnessed for multi-day positioning, producing a perfect 100 percent historical win rate and an aggregate gain of 625.14 percent. The quantitative evidence highlights an algorithm designed to capture large directional shifts while maintaining low transaction frequency. However, analysts must weigh these metrics against the sample boundary of 7 trades over 0.21 years and the heavy profit contribution of top-tier winners. While the historical record displays exceptional performance and zero closed drawdown, institutional evaluation requires ongoing monitoring as the strategy builds a larger sample size across diverse market regimes.
Data Scope and Methodology
The data and performance metrics analyzed in this report are derived strictly from historical simulated closed-trade records for the EDEN ticker on the 15-minute timeframe between September 30, 2025, and December 16, 2025. All calculation outputs, including win rates, holding times, profit factors, and profit concentration percentages, describe closed backtested signals and do not represent actual live brokerage account returns or execution guarantees. The starting point of the dataset reflects the beginning of the quantitative backtest log and does not indicate the inception or listing date of the underlying asset. Realized live trading outcomes may differ from simulated historical results due to market friction, slippage, liquidity variations, and changing volatility conditions.