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Crypto market · BinanceLSK · LSK Quantitative Strategy Analysis: Evaluating a 79.17% Win Rate and 7.50 Profit Factor Swing System
This quantitative evaluation analyzes the historical backtest performance of the top-ranked LSK · LSK trading model across a 5.37-year data evaluation window. Operating on a 15-minute timeframe, the strategy demonstrates a distinctive low-frequency swing profile, completing 144 total trades with a high win rate of 79.17% and a profit factor of 7.50. Despite an elevated historical maximum drawdown of 47.07%, the system displays structural payoff balance, with the top three winning trades accounting for only 13.82% of gross profit. This research breaks down the strategy's operational rhythm, return distribution, drawdown characteristics, and year-by-year performance consistency.
Analysis as of 30.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy designated for LSK · LSK represents the primary Core 2 independent model for this digital asset, achieving a DevioLab score of 74.74 and holding the number one rank for its specific ticker. Formulated on a 15-minute price evaluation interval, the system was tracked over a history spanning from June 28, 2020, through November 11, 2025, representing a total analytical duration of 5.37 years. Across this historical window, the algorithm executed a total of 144 completed trades. Out of these, 114 trades generated positive returns while 30 resulted in net losses, establishing a historical win rate of 79.17%. The cumulative closed trade performance across the entire evaluation horizon reached an aggregate sum of 1,210,330.89%, translating to an annualized return figure of 591.26% under the standardized evaluation framework. With a overall profit factor of 7.50, the system displays a strong historical inclination toward maintaining upside gains relative to realized losses.
Trading rhythm and position duration
Although the algorithm evaluates market conditions using 15-minute bar data, its operational behavior reflects a multi-day swing trading model rather than a high-frequency or short-term intraday strategy. The trade frequency is highly selective, averaging 26.80 trades per calendar year, which corresponds to approximately 2.94 trades per active month. The strategy's position duration parameters emphasize patience over transaction volume. The average holding duration per position is 121.53 hours, or approximately 5.06 days, while the median holding duration stands at 64.63 hours, or roughly 2.69 days. This divergence between average and median holding periods indicates that while many positions resolve within three days, a subset of trades remains open for extended periods to capture longer-term price moves. The temporal spacing between trade exits confirms this measured cadence: the average time between completed trades is 13.40 days, with a median exit interval of 8.04 days. Consequently, the strategy spends considerable periods out of the market, waiting for specific structural conditions before initiating exposure.
Quality of historical results
The statistical distribution of trade returns reveals consistent payoff symmetry across the strategy's execution history. The average trade performance across all 144 completed positions is +7.88%, which aligns closely with the median trade return of +7.44%. This tight alignment between the mean and median indicates that the overall expectancy is not distorted by extreme tail events or a small number of artificial spikes. The single best historical trade generated a return of +101.55%, whereas the worst single trade incurred a loss of -27.15%. A key metric highlighting result quality is the concentration of gross profit: the three largest winning trades collectively account for just 13.82% of the total gross profit. In quantitative system analysis, a low concentration ratio confirms that profitability is broadly distributed across many successful trades rather than reliant on a few lucky outliers. Combined with a 79.17% win rate and a longest winning streak of 22 consecutive trades, the system's return engine reflects broad structural stability.
Risk, drawdown and losing behavior
Risk analysis requires evaluating the structural friction between hit rate and peak equity decline. The strategy recorded a maximum historical drawdown of 47.07%. When juxtaposed against the strategy's trade metrics, a notable statistical tension emerges. The algorithm demonstrates exceptional loss mitigation in terms of trade sequences, experiencing a maximum consecutive losing streak of only 3 trades throughout the entire 5.37-year history. Out of 144 total trades, only 30 closed in negative territory. However, the maximum trade loss reached -27.15%, demonstrating that individual adverse trades or clusters of moderate pullbacks can generate substantial drawdown when asset volatility expands. Because positions are held for an average of 121.53 hours, open trade equity is exposed to broader market swings over multi-day periods. The 47.07% peak drawdown underscores that despite high win rates and an impressive 7.50 profit factor, capital swings remain a core characteristic of the strategy's risk profile.
Behavior through time and yearly stability
An examination of the yearly breakdown demonstrates that the strategy achieved net positive closed returns in every single calendar year of its 5.37-year history, though trading volume and annual performance varied considerably. In 2020, across 15 completed trades, the strategy recorded 13 wins and 2 losses, achieving an 86.67% win rate and a cumulative return sum of +57.29%. The year 2021 marked the system's most active period, producing 50 trades with 41 wins and 9 losses (82.00% win rate) for a combined sum of +464.34%. During 2022, trading frequency moderated to 31 trades, yielding 20 wins and 11 losses; despite a lower win rate of 64.52%, the annual sum remained positive at +97.08%. In 2023, the model displayed high selectivity, executing 14 trades, all 14 of which closed as winners for a 100.00% win rate and a cumulative sum of +202.77%. In 2024, the strategy generated 29 trades with 22 wins and 7 losses (75.86% win rate) for +175.73%. In the partial period for 2025, 5 trades yielded 4 wins and 1 loss (80.00% win rate), contributing +138.00% in closed return.
Recent period since 2024-06-01 versus full history
Evaluating performance in the recent window beginning June 1, 2024, provides insight into the strategy's ongoing statistical alignment. From June 1, 2024, through the end of the dataset on November 11, 2025, the strategy completed 18 trades, generating a cumulative return sum of +238.06%. Comparing this recent operational window to the full history demonstrates robust continuity. Over the full 5.37-year timeline, the average return per completed trade was +7.88%. In the recent period, the average closed return per trade rose to approximately +13.23% (+238.06% sum across 18 trades). The trade frequency in the recent window—18 trades over approximately 1.4 years—reflects an annualized cadence of roughly 12 to 13 trades per year, which is somewhat lower than the long-term historical baseline of 26.80 trades per year. This suggests that while recent signal triggers have occurred less frequently, the individual return contribution per completed opportunity has remained strong.
Strengths and limitations
The primary structural strength of this strategy lies in its high win rate of 79.17% paired with a profit factor of 7.50, demonstrating a consistent ability to capture profitable swing moves on LSK · LSK. The strategy exhibits low profit concentration, with the top three winners accounting for only 13.82% of gross profit, confirming that historical performance is driven by a broad sample of trades. Furthermore, the system maintained positive cumulative returns across all evaluated calendar years. Conversely, significant limitations must be recognized. The total sample size of 144 completed trades over 5.37 years is relatively small, yielding a low average trade frequency of 26.80 trades per year. This low trade count increases statistical sensitivity to individual trade outcomes. Additionally, the maximum drawdown of 47.07% and a worst single trade loss of -27.15% reflect substantial historical equity volatility during adverse market phases.
DevioLab analytical conclusion
The strategy earns its top rank for LSK · LSK and a DevioLab score of 74.74 through a disciplined, low-frequency swing approach that yields favorable payoff statistics. By combining a 15-minute execution interval with multi-day position holding times averaging 121.53 hours, the algorithm successfully captures extended price trends while maintaining high selectivity. The tight alignment between the mean trade return (+7.88%) and median trade return (+7.44%), alongside a low winner concentration ratio of 13.82%, confirms that historical returns are robustly distributed. However, potential operators must account for the structural trade-off presented by the system: high win rates and long holding times are accompanied by periodic equity drawdowns as deep as 47.07%. Overall, the historical statistical profile shows consistent multi-year performance, supported by sustained performance in the recent post-June 2024 evaluation window.
Data scope and methodology
All metrics presented in this analysis are derived strictly from closed trade backtest data for LSK · LSK over the period covering June 28, 2020, to November 11, 2025. The dataset comprises exactly 144 completed trade events evaluated on 15-minute candlestick interval inputs. Performance figures, including annualized metrics, profit factor, drawdown percentages, and yearly trade sums, represent simulated historical closed trade results. Recent period calculations cover all completed trades with exit timestamps on or after June 1, 2024. Execution statistics do not account for external market variables such as unmodeled order book slippage, exchange commission structures, or variable liquidity constraints. Historical backtested performance provides empirical insight into quantitative model design but does not constitute investment advice or guarantee future trading performance.