1INCHUSDT
Crypto market · Binance1INCH · 1INCH Quantitative Analysis: High-Expectancy Swing Strategy Driven by Extreme Trade Selection and Multi-Week Holding Horizons
An exhaustive statistical examination of the 1INCH algorithmic strategy across a 4.87-year historical testing window reveals a highly selective trading model operating on a 15-minute execution interval. Despite its granular chart resolution, the strategy executed only 52 completed trades between December 2020 and November 2025, generating an 82.69% win rate and a profit factor of 6.25. By combining an average holding duration of 322.36 hours with broad profit distribution—where the top three winning trades account for only 24.88% of gross profit—the system demonstrates strong per-trade expectancy (+16.64% average trade return) and structural resilience, balancing a 37.14% maximum drawdown with a streak architecture that includes 12 consecutive winning trades against a maximum losing streak of just 2.
Analysis as of 22.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative strategy evaluated here operates on the 1INCH · 1INCH pair using a 15-minute timeframe, yet its behavior contrasts sharply with conventional intraday trading models. Across a historical dataset spanning nearly five years, from December 25, 2020, to November 6, 2025, the strategy produced a total of 52 completed trades. This translates to an exceptionally low annual trade frequency of 10.69 trades per year, or approximately 1.79 trades per active month. The strategy achieves a high win rate of 82.69%, supported by 43 winning positions against only 9 losing positions. With an overall DevioLab score of 54.72 and a asset ranking of 8th among evaluated strategies for this token, the model relies on stringent entry filters that deliberately pass over transient price noise on the 15-minute chart in pursuit of pronounced, high-expectancy price moves. The non-compounded cumulative closed-trade return across the full evaluation history reaches +106,171.51%, reflecting an annualized figure of 170.85% under the simulated testing framework.
Trading rhythm and position duration
Analyzing the strategy's temporal structure demonstrates that its 15-minute execution chart serves strictly as an entry trigger rather than a high-frequency trading arena. The average holding duration per trade stands at 322.36 hours (roughly 13.43 days), whereas the median holding duration is 115.25 hours (approximately 4.80 days). This significant divergence between mean and median holding times indicates that while the median position resolves within a multi-day window, a subset of trades remains open for extended macro cycles, pulling the mathematical average significantly higher. Inter-trade pacing exhibits a similar statistical spread: the average duration between position exits is 34.74 days, whereas the median interval between exits is 12.92 days. This cadence confirms that the system experiences prolonged periods of market inactivity, during which no open exposure is maintained, punctuated by clusters of entry signals when market volatility and structural alignment align with the quantitative criteria.
Quality of historical results
The pay-off distribution of the strategy demonstrates strong expectancy driven by broad profit capture rather than isolated lucky trades. The average trade yield across all 52 closed positions stands at +16.64%, closely anchored by a median trade yield of +12.53%. This proximity between average and median returns indicates that positive expectations are broadly distributed across the dataset rather than inflated by extreme statistical outliers. The single best trade generated a return of +97.36%, while the worst trade resulted in a loss of -37.14%. The strategy's profit factor of 6.25 reflects an exceptional ratio of total gross gains relative to total gross losses. Crucially, the top three winning trades account for only 24.88% of total gross profits. In quantitative systems, a low concentration score in the top winners confirms that historical profitability is structurally distributed across the 43 winning trades, reducing vulnerability to single-event dependencies.
Risk, drawdown and losing behavior
Risk parameters within the backtested dataset reflect a pronounced imbalance between positive and negative trade sequences. The maximum peak-to-trough drawdown recorded across the 4.87-year evaluation window is 37.14%. Notably, this maximum drawdown figure mirrors the strategy's single worst trade of -37.14%, which occurred during the strategy's initial exposure in late December 2020. Across the remaining trade history, losing trades were sparse and isolated. The longest losing streak recorded by the system was 2 consecutive trades, whereas the longest winning streak extended to 12 consecutive profitable positions. Out of 52 total trade completions, only 9 closed in negative territory. The low occurrence of consecutive losses limited equity curve decay, allowing the high profit factor of 6.25 to defend historical equity gains following the initial severe trade drawdown.
Behavior through time and yearly stability
Examining performance across individual calendar years reveals significant variation in trade opportunity and return distribution over time. In 2020, the strategy recorded a single trade that resulted in a loss of -37.14%. The following year, 2021, represented the strategy's highest activity period, generating 28 trades with an 82.14% win rate (23 wins, 5 losses) and a sum of trade yields totaling +396.59%. In 2022, trade frequency contracted dramatically to 6 positions, yet performance remained positive with 5 wins and 1 loss (83.33% win rate) yielding +56.94%. In 2023, the model achieved a 100% win rate across 4 completed trades, generating +226.11%. Activity expanded moderately in 2024 to 10 trades, returning +125.25% with an 80.00% win rate (8 wins, 2 losses). Through the partial dataset in 2025, 3 trades were executed, all profitable (100% win rate), producing +97.62%. This annual progression illustrates that while trade frequency fluctuates significantly depending on prevailing volatility conditions, per-year win rates remained consistently above 80% in every full calendar year following inception.
Recent period since 2024-06-01 versus full history
Evaluating trade performance from June 1, 2024, to November 6, 2025, offers insight into the model's performance in recent market environments. During this recent window, the strategy executed 7 completed trades, yielding a combined sum of trade returns equal to +119.94%. These 7 trades represent approximately 13.46% of the strategy's lifetime trade volume (52 trades), executed across approximately 1.4 years. The average return per trade during this recent period was approximately +17.13%, which aligns closely with the full-history lifetime average trade yield of +16.64%. This alignment indicates that the model's structural pay-off profile and high expectancy per entry remained active and consistent during recent market cycles, maintaining its characteristic low-frequency, high-yield trade resolution without experiencing degradation in trade quality.
Strengths and limitations
The strategic profile presents distinct analytical advantages alongside structural trade-offs. Primary strengths include a high historical win rate of 82.69%, a profit factor of 6.25, and a well-balanced gross profit distribution where the top three winning trades contribute under 25% of overall gains. Furthermore, the substantial difference between win count (43) and loss count (9), paired with a short maximum losing streak of 2, demonstrates strong trade filtering efficiency. Conversely, the model's primary limitation stems from its small statistical sample size. A total of 52 trades over nearly five years reduces the statistical power of the dataset, with over half of all lifetime trades (28) concentrated within a single calendar year (2021). Additionally, the large magnitude of the single worst trade (-37.14%) highlights that when losses do materialize, they can be substantial relative to individual position sizes, requiring strict adherence to systemic risk boundaries.
DevioLab analytical conclusion
The DevioLab score of 54.72 and rank of 8 for 1INCH reflect a highly specialized quantitative model that trades execution frequency for statistical edge. By enforcing strict entry rules on a 15-minute timeframe, the strategy operates effectively as a macro swing trading framework with multi-week holding periods. Its ability to maintain high win rates across differing annual market regimes—supported by an average trade yield of +16.64% and robust top-winner dispersion—confirms the historical integrity of its expectancy model. However, potential operators must account for long periods of inactivity, wide holding-time variance, and the statistical constraints imposed by a 52-trade lifetime sample size when evaluating this system within a broader quantitative portfolio context.
Data scope and methodology
This analysis is based entirely on historical backtested trade logs for the strategy operating on 1INCH · 1INCH across 15-minute price data between December 25, 2020, and November 6, 2025. All statistics presented—including total returns, annual breakdowns, drawdown percentages, holding durations, and trade counts—derive exclusively from closed historical trade records using non-compounded return metrics. The recent performance window is defined as all trades with sell timestamps on or after June 1, 2024, UTC. These figures represent simulated historical research results and do not incorporate live order book slippage, execution delays, transaction fees, or margin costs. Historical performance within backtested datasets does not guarantee future financial results.