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Crypto market · BinanceEDEN · EDEN Historical Strategy Analysis: Evaluating a 100% Win Rate Swing Model Across a Compact 7-Trade Backtest
This quantitative research report evaluates the historical performance of the algorithmic strategy EDEN 215000 +625.14% 1TRAD-SQL4 operating on EDEN (EDEN) with a 15-minute candle evaluation interval. Backtested across a 0.21-year historical dataset spanning from September 30, 2025, to December 16, 2025, the strategy achieved a DevioLab score of 76.05 and a ranking of 2 for this asset. Over its 7 completed trades, the model recorded a perfect 100 percent win rate, zero losing trades, and a cumulative full-history simulated return of +625.14 percent. Position holding times average 93.04 hours, pointing to a patient swing-trading execution framework rather than high-frequency scalping. The analysis highlights key statistical traits, including an average trade gain of +35.12 percent, a median trade return of +27.11 percent, a profit factor of 6.88, and a profit concentration where the top 3 winning trades generated 69.30 percent of total gross profit. While these results exhibit remarkable trade efficiency, the small statistical sample size of 7 trades requires careful interpretation when assessing long-term robustness.
Analysis as of 22.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative strategy designated as EDEN 215000 +625.14% 1TRAD-SQL4 is a specialized algorithmic model configured for the EDEN (EDEN) crypto asset. Operating on a 15-minute timeframe interval, the system monitors short-term price developments while implementing a strategy designed to capture substantial intermediate price moves. Within the DevioLab quantitative evaluation framework, this model holds a score of 76.05 and ranks second among evaluated strategies for the EDEN asset. The underlying backtest dataset spans a history length of approximately 0.21 years, commencing on September 30, 2025, and concluding on December 16, 2025. Across this window of approximately 77 days, the system completed 7 trades, achieving a historical total closed-trade gain of +625.14 percent, which translates to an equivalent annualized return rate of +625.14 percent under the strategy's compounding history calculations. Notably, all 7 completed trades closed with positive returns, producing a 100 percent win rate and zero losing trades. This profile establishes a highly efficient statistical baseline, though one developed within a tightly focused historical observation window.
Trading rhythm and position duration
Although the strategy processes market data on a 15-minute price candle frequency, its operational behavior reflects a disciplined swing-trading cadence rather than high-frequency or scalping activity. The strategy registered a total of 7 trades across its active dataset, corresponding to an annualized rate of 33.11 trades per year and an average frequency of 2.33 trades per active month. Position holding durations show that positions remain open for extended multi-day periods. The average holding duration across all completed trades stands at 93.04 hours, equivalent to roughly 3.88 days, while the median holding duration is 29.0 hours. This structural variance between median and mean holding times indicates that while certain trades exit within slightly more than a single day, several positions are held for multi-day trends. Furthermore, the temporal spacing between closed trades demonstrates a deliberate pacing: the average time between trade exits is 12.46 days, with a median exit interval of 8.83 days. This confirms that the model selectively enters the market during specific technical conditions, maintaining long open periods relative to its low monthly execution frequency.
Quality of historical results
The statistical distribution of trade payoffs displays strong positive skewness and notable payoff magnitude. With 7 winning trades out of 7 total entries, the historical hit rate is a flawless 100 percent. The average trade gain across the backtest is +35.12 percent, whereas the median trade gain is +27.11 percent. This discrepancy between average and median returns highlights the presence of exceptional outlier gains that elevate the overall mean. The single best trade achieved an impressive return of +88.44 percent, while the worst trade in the dataset still produced a positive gain of +8.36 percent. The strategy recorded a profit factor of 6.88. Examining profit concentration reveals that the top 3 winning trades collectively accounted for 69.30 percent of the strategy's total gross profit. This concentration demonstrates that while every trade yielded positive results, a major portion of the absolute cumulative performance was generated by a minor cluster of high-impact winning positions. This dependency on standout trades is a typical characteristic of trend-following swing systems that allow profitable positions to run.
Risk, drawdown and losing behavior
From a traditional drawdowns perspective, the historical statistics for this strategy display an ideal profile, with a recorded maximum equity drawdown of 0.00 percent. This zero-drawdown figure is a direct statistical consequence of experiencing zero losing trades across the entire 7-trade sample. Correspondingly, the strategy achieved a maximum winning streak of 7 consecutive trades and a maximum losing streak of 0 trades. In practical quantitative analysis, however, a zero-percent historical drawdown over a small trade sample should not be interpreted as absolute risklessness or immunity to market losses. Because the historical sample covers only 7 closed positions over 77 days, the model has not yet been subjected to adverse market regimes or extended sideways consolidation periods that typically generate drawdown in trend-following systems. The primary historical risk characteristic of this model lies not in realized trade losses, but in intra-trade equity volatility during the 93.04-hour average holding period, as well as the statistical fragility inherent in evaluating risk parameters across a limited sample size.
Behavior through time and yearly stability
Because the historical dataset covers an active window starting on September 30, 2025, and ending on December 16, 2025, all performance data is concentrated entirely within the 2025 calendar year. The yearly breakdown table reflects this precise focus: for 2025, the strategy completed 7 trades, secured 7 wins with 0 losses, reached a 100 percent win rate, and accumulated a summed trade return of +245.86 percent (uncompounded sum of individual percentage gains). Because there are no multi-year comparison points prior to late 2025, it is impossible to evaluate how the strategy performed across earlier macro cycles or differing crypto market regimes. The strategy's history shows concentrated, consistent profitability throughout its brief operational window, but lacks multi-year historical evidence to evaluate long-term temporal stability or performance drift over multi-year periods.
Recent period since 2024-06-01 versus full history
Analyzing performance from June 1, 2024, onward provides a direct window into recent market dynamics. In the case of this strategy, because history logging began on September 30, 2025, the recent period since June 1, 2024, encompasses 100 percent of the strategy's available trade record. During this recent window, the model completed all 7 of its total historical trades, generating a summed return of +245.86 percent with zero losing trades. Because the full history and the recent post-June 2024 history are identical, there is no divergence between long-term baseline behavior and recent strategy efficiency. Every statistical metric recorded by the system represents recent operational behavior under late-2025 market conditions.
Strengths and limitations
The primary statistical strength of the EDEN 215000 +625.14% 1TRAD-SQL4 strategy is its extreme operational trade efficiency, evidenced by a 100 percent historical win rate, an average trade yield of +35.12 percent, a profit factor of 6.88, and a DevioLab score of 76.05. The system's ability to capture multi-day trends is demonstrated by its best trade return of +88.44 percent and its long average holding period of 93.04 hours. Conversely, the principal analytical limitation is the strategy's small statistical sample size, comprising only 7 completed trades over a 0.21-year history. A sample of this size limits the statistical significance of metrics such as the 0 percent drawdown and 100 percent hit rate. Additionally, the heavy concentration of gross profits, where 3 trades generated 69.30 percent of total gains, indicates that future performance will remain highly dependent on the strategy's ability to consistently capture major outlier moves.
DevioLab analytical conclusion
The strategy EDEN 215000 +625.14% 1TRAD-SQL4 demonstrates an impressive quantitative profile for the EDEN asset on a 15-minute chart setup, taking second place in DevioLab rankings with a score of 76.05. Its historical performance reflects a highly selective swing-trading model that patience-filters entries to capture substantial percentage moves, resulting in a +625.14 percent compounded gain across 7 consecutive winning trades. The key takeaway for quantitative analysts is the balance between remarkable trade quality and statistical scope: while the strategy operated flawlessly across its 77-day history, ongoing monitoring across larger trade volumes and diverse market environments will be essential to establish the long-term persistence of its high win rate and exceptional profit factor.
Data scope and methodology
All metrics detailed in this evaluation are derived strictly from historical backtested execution data for the EDEN asset on the 15-minute timeframe between September 30, 2025, and December 16, 2025. Trade percentage returns describe simulated closed-position results and do not represent live account balances, active portfolio allocations, or execution slip management. The backtest statistics do not account for external exchange fees, funding rates, or order book liquidity constraints. Historical performance metrics are calculated exclusively from past simulated data and do not provide a guarantee or assurance of future strategy returns.