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Öffentlicher Katalog · DEVIOLAB

STRATEGIEKATALOG

Algorithmische Strategien erkunden, Performance vergleichen und die vollständige Historie jedes Modells öffnen.
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Eine von DevioLab ausgewählte Sammlung von Krypto- und Aktienstrategien als primäre, ausgewogenere und stärker geschützte Startoption mit Fokus auf Risiko- und Drawdown-Kontrolle.
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Übersicht der ausgewählten Strategie

INJUSDT

Kryptomarkt · Binance
INJ 215000 +6502.39% 1TRAD-PJF0
Von DevioLab empfohlen · Core 1 iPrimäre DevioLab-Empfehlung: ein stärker geschütztes, ruhigeres und stabileres Profil mit Fokus auf Risiko- und Drawdown-Kontrolle.
Von DevioLab empfohlen · Core 2 iAggressivere DevioLab-Empfehlung: akzeptiert höheres Risiko und tiefere Drawdowns im Austausch für potenziell höhere Renditen.
25Trades
100.0%Trefferquote
+19.26%Ø Trade
+66.89%Bester Trade
+0.29%Schlechtester Trade
+566.9%Annualisiert
Analytisches Strategieprofil · 70087eadc0bfecd4

Algorithmic Strategy Analysis INJ · INJ: 100% Win Rate and 6502.39% Historical Return

An in-depth quantitative research report on an algorithmic model trading the INJ crypto asset on the 15-minute timeframe. Ranked #1 for the ticker with a DevioLab Score of 84.29, the strategy achieved 25 winning trades out of 25 across 5.81 years.

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Strategy profile

The algorithmic trading strategy named INJ 215000 +6502.39% 1TRAD-PJF0 is engineered for the INJ crypto asset operating on a 15-minute (15m) chart interval. Within the model universe for this asset, it holds the number 1 rank for the ticker with a DevioLab Score of 84.29. While not designated as a core strategy (is_core is false), it was selected as a top featured model due to its exceptional performance footprint. The dataset spans from October 21, 2020, to August 14, 2026, representing approximately 5.81 years of historical testing. Across this period, the system produced an all-history cumulative return of 6502.39% with an annualized return figure of 566.86%.

Trading rhythm and position duration

Despite relying on a 15-minute timeframe, the strategy exhibits extreme selectivity and low trading frequency. Over the 5.81-year sample, only 25 trades were executed, translating to roughly 4.3 trades per year. This model cannot be classified as scalping, as it does not perform frequent entries. Instead, it waits for highly specific market structure conditions before entering a position. Specific metrics regarding average holding hours or median days between exits are not available in this dataset, but the low trade count highlights a patient positioning approach.

Quality of historical results

The defining statistical highlight of the backtest is a 100% win rate across all 25 completed trades, with zero losing positions. The average trade gain stands at 19.26%, with a median trade return of 15.81%. The single best trade delivered a 66.89% gain, while the worst winning trade achieved 0.29%. The overall profit factor is recorded at 7.5. Profit distribution is well-balanced, with the top 3 winning trades generating 29.98% of total gross profit, confirming that performance was not driven by a single outlier.

Risk, drawdown and losing behavior

Because every closed trade in the historical sample ended in profit, the recorded maximum closed drawdown is 0%. The longest winning streak is 25 trades, and the longest losing streak is 0. However, traders must keep in mind that zero closed drawdown in historical simulations does not rule out unrealized equity dips during trade hold times or the possibility of losing trades occurring in live forward testing. A sample size of 25 trades requires realistic risk management.

Behavior through time and yearly stability

The dataset evaluates over 5.8 years of crypto market cycles. Averaging 4.3 trades per year, the model filters out daily price noise and operates only during rare alignment windows. While granular yearly breakdown tables are omitted from the raw feed, the macro metric stability reflects consistent adherence to strict entry filtering across market cycles.

Strengths and limitations

The primary strengths of this model include an unbroken 100% win rate, robust average gain per trade (19.26%), zero closed drawdown, and substantial cumulative historical profit (6502.39%). The principal limitation lies in the small sample size of 25 trades over 5.81 years. Long inactive spells require strict patience, and past perfection does not eliminate future market uncertainty.

DevioLab analytical conclusion

The analyzed INJ strategy is an exemplar of high-precision, low-frequency algorithmic design. Holding rank #1 for its asset with a DevioLab Score of 84.29, it demonstrates how selective signal generation can achieve remarkable historical stats. Nevertheless, users should evaluate the modest trade count and recognize that simulated historical metrics are not a guarantee of future returns.

Data scope and methodology

This analysis is based on simulated historical closed trades for INJ on the 15m timeframe from October 21, 2020, through August 14, 2026. Performance metrics describe simulated backtest results and do not represent actual Binance account returns or guarantee future performance.

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