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Crypto market · BinanceNEAR · NEAR Protocol Quantitative Strategy Analysis: Evaluating a Multi-Year High-Win-Rate Swing Model
This quantitative analysis examines the historical performance of the NEAR algorithmic trading strategy across a 5.89-year evaluation window from October 2020 to September 2026. Executed on a 15-minute timeframe for NEAR, the strategy completed 249 trades, achieving a historical win rate of 72.29 percent and a profit factor of 2.31. Despite utilizing a 15-minute chart for execution signals, the strategy operates as a low-frequency swing trading framework with a median position holding time of 76.25 hours and an average trade frequency of 42.3 trades per year. The return distribution exhibits notable consistency, evidenced by a median trade return of 5.44 percent exceeding the average trade return of 4.31 percent, alongside low profit concentration where the top three winning trades represent only 7.17 percent of gross profits. While recent execution since June 2024 remains active with 81 closed trades generating a cumulative sum of 172.22 percent, risk metrics highlight notable downside exposure, including an individual worst trade loss of -47.67 percent and an unprofitable calendar year during the 2022 market contraction.
Analysis as of 02.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative trading model designated as NEAR 215000 +239952.40% 1TRAD-LGO7 provides a long-term historical record spanning 5.89 years of backtested market data from October 14, 2020 to September 2, 2026. Operating within the crypto market on NEAR (NEAR), the model generates execution triggers from 15-minute candlestick data. Over the complete sample history, the strategy recorded 249 completed closed trades, resulting in 180 winning operations and 69 losing operations. This performance yields an overall win rate of 72.29 percent and a profit factor of 2.31. Across all closed positions, the aggregate sum of percentage trade returns reached 239,952.40 percent, corresponding to an annualized return metric of 186.25 percent. Although the system monitors 15-minute price action, its historical trade frequency and position holding metrics distinguish it clearly from intra-day scalping, establishing it as a patient, swing-oriented algorithmic strategy designed to capture medium-term price trends in NEAR.
Trading rhythm and position duration
Analyzing the temporal pacing of the strategy reveals a measured execution rhythm tailored to multi-day price swings. Over the 5.89-year historical dataset, the strategy averaged 42.3 trades per year, which translates to approximately 3.89 completed trades per active month. The time elapsed between trade exits averages 8.56 days, with a median exit interval of 5.91 days, confirming that trade setups are selective rather than continuous. Position holding durations further emphasize this medium-term orientation. The average holding duration across all positions is 138.58 hours (approximately 5.77 days), whereas the median holding duration is 76.25 hours (approximately 3.18 days). The right-skewed divergence between median and average holding times indicates that while the majority of trades resolve within three to four days, a subset of positions remains open significantly longer to accommodate sustained trends. The combination of a 15-minute execution interval with multi-day holding periods suggests that granular candle data is utilized primarily for entry and exit precision rather than high-frequency portfolio turnover.
Quality of historical results
The statistical distribution of individual trade outcomes offers key insights into the operational stability of the strategy. The historical average trade return stands at 4.31 percent, while the median trade return is 5.44 percent. In quantitative research, a median trade return that exceeds the average trade return is a structural positive; it demonstrates that positive performance is driven by a strong baseline of typical trades rather than heavily reliant on extreme positive outliers. This broad distribution of gains is further verified by profit concentration metrics: the top three winning trades accounted for only 7.17 percent of total gross profit across the entire history. The strategy achieved a maximum single winning trade of 50.83 percent, compared to a maximum single losing trade of -47.67 percent. With a profit factor of 2.31, gross historical winning returns exceeded gross losing returns by more than two to one, supported primarily by the high 72.29 percent hit rate across its 249 execution events.
Risk, drawdown and losing behavior
A thorough evaluation of strategy risk requires examining drawdown dynamics, losing streaks, and adverse single-trade events. Maximum portfolio drawdown figures are not reported within the primary dataset metrics and remain uncalculated. However, risk parameters can be inferred from trade-level losses and historical sequence patterns. The strategy recorded a longest winning streak of 14 consecutive trades, contrasted with a longest losing streak of 5 consecutive trades. While the high win rate limits the frequency of extended losing sequences, individual trade risk remains substantial. The strategy experienced a single worst trade loss of -47.67 percent, indicating that during extreme market dislocations or trend reversals, positions can suffer severe drawdowns before exit criteria are satisfied. This combination of a 72.29 percent hit rate and a -47.67 percent worst-case trade highlights a structural tension: the system achieves high reliability across standard market regimes but relies on broad statistical expectancy rather than strict tight stop-losses to absorb rare tail-risk events.
Behavior through time and yearly stability
Examining performance on a calendar-year basis reveals how the strategy adapted to changing macro regimes within the crypto market from 2020 through 2026. In late 2020, the strategy completed 6 trades with a 66.67 percent win rate and a cumulative return sum of 76.58 percent. During the strong market expansion of 2021, trade frequency peaked at 64 trades, yielding 51 wins (79.69 percent win rate) and a peak annual cumulative return sum of 591.67 percent. Conversely, the 2022 market contraction posed a notable structural challenge; despite maintaining a 62.79 percent win rate across 43 trades, larger average loss magnitudes led to a net annual return sum of -66.95 percent. The system recovered in 2023, generating 216.31 percent across 31 trades with a 77.42 percent win rate. Performance remained positive in subsequent years: 2024 produced 143.06 percent across 54 trades (74.07 percent win rate), 2025 yielded 44.67 percent across 40 trades (65.00 percent win rate), and the partial year of 2026 recorded 68.24 percent across 11 trades (72.73 percent win rate). Overall, the strategy achieved net positive annual return sums in six out of seven calendar periods.
Recent period since 2024-06-01 versus full history
Evaluating recent performance from June 1, 2024 onward provides an essential window into the model's contemporary validity under modern market conditions. During this recent window, the strategy completed 81 closed trades, representing approximately 32.5 percent of all historical operations. These recent trades produced a cumulative return sum of 172.22 percent. The active trade frequency during this period confirms that the strategy has not stagnated or ceased generating setups in current market conditions. Furthermore, the positive return sum of 172.22 percent across 81 trades aligns consistently with the multi-year expectation established over the full 5.89-year backtest. The strong sample size in the post-June 2024 period reduces the likelihood that historical profitability was strictly an artifact of earlier crypto market cycles.
Strengths and limitations
The primary analytical strength of this strategy lies in its high statistical reliability and well-balanced payout profile. A 72.29 percent historical win rate combined with a 2.31 profit factor provides steady equity curve expansion during trending market phases. The low concentration of profit in top winners (7.17 percent) and a median trade return (5.44 percent) that exceeds the average trade return (4.31 percent) demonstrate that performance is systemic rather than dependent on lucky statistical outliers. Among limitations, the absence of reported maximum portfolio drawdown data represents a key analytical gap. Furthermore, the single worst trade loss of -47.67 percent highlights vulnerability to sudden adverse volatility. Finally, as evidenced by the -66.95 percent net return sum during 2022, high win rates do not fully protect the system from net annual losses when underlying asset trends experience prolonged structural weakness.
DevioLab analytical conclusion
The quantitative evidence for NEAR 215000 +239952.40% 1TRAD-LGO7 characterizes a patient swing-trading architecture operating effectively on 15-minute price data for NEAR. By maintaining an average holding duration of nearly six days and executing roughly 42 trades per year, the strategy avoids over-trading while capturing substantial directional moves. Its robust historical hit rate of 72.29 percent and broad profit distribution across 249 trades establish a solid statistical foundation. However, potential allocators must account for the structural trade-offs identified in the risk profile: specifically, the possibility of severe single-trade drawdowns approaching -48 percent and negative annual performance during severe multi-month bear markets. Risk management protocols should carefully account for position sizing to withstand these historical downside vectors.
Data scope and methodology
This analysis is based strictly on simulated historical backtest statistics covering 249 completed closed trades in NEAR on a 15-minute timeframe between October 14, 2020 and September 2, 2026. All reported performance figures, including percentage sums, averages, and win rates, reflect historical mathematical outputs of closed trades within the backtesting environment. The metrics do not account for live execution friction, variable order book slippage, exchange fee schedules, or dynamic portfolio compounding. Historical simulated results are analyzed for quantitative evaluation purposes only and do not constitute investment advice or guarantees of future performance.