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Crypto market · BinanceQTUM · QTUM Algorithmic Swing Strategy Analysis: 75.31% Win Rate Across 6.14 Years of 15-Minute Data
This quantitative evaluation analyzes a 15-minute timeframe algorithmic trading strategy configured for QTUM · QTUM over a 6.14-year historical backtest window from July 2020 through September 2026. Generating 239 completed trades, the strategy exhibits a high historical hit rate of 75.31% (180 winning trades versus 59 losing trades), accompanied by an average trade yield of +5.19% and a median trade yield of +5.80%. Despite executing on a 15-minute execution interval, position holding times average 116.79 hours with a median of 67.00 hours, establishing its operational profile firmly as a multi-day swing framework rather than a high-frequency or scalping algorithm. The top three winning trades account for only 7.25% of cumulative gross profit, reflecting broad-based performance rather than dependence on singular extreme outliers. However, the system's worst historical trade (-28.74%) and maximum drawdown of 34.45% highlight the statistical asymmetry between frequent small-to-moderate gains and occasional wide downside moves. This article provides an objective quantitative audit of the strategy's historical metrics, trading frequency, drawdown profile, and recent performance post-June 2024.
Analysis as of 04.09.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The evaluated algorithmic model operates on a 15-minute candlestick chart interval for QTUM · QTUM, designed to generate automated trade signals over extended market cycles. Over a historical evaluation span of 6.14 years—ranging from July 16, 2020, to September 04, 2026—the quantitative model recorded 239 completed trades. DevioLab assigns this model a DevioLab Score of 69.39, ranking it 6th among evaluated strategies for this specific crypto asset pair. The strategy is structured as automated decision logic intended for execution via trading bots, trading robots, or autonomous trading agents operating on systematic rules. Rather than relying on constant market exposure or high-turnover scalping, the algorithm selective filters market conditions, averaging approximately 38.94 completed trades per year or 3.46 trades per active month. Across the full 6.14-year backtest window, the strategy accumulated a cumulative simulated closed-trade profit sum of +3,311,539.88%, corresponding to an annualized rate of 1,592.81%. These metrics reflect historical compounding calculations across all closed trade sequences and serve as a baseline for understanding the structural behavior of the system under past market conditions.
2. Trading rhythm and position duration
Although the strategy utilizes 15-minute price data to determine entry and exit triggers, its position holding characteristics differ substantially from high-frequency intraday tactics. The average holding duration per trade stands at 116.79 hours (approximately 4.87 days), while the median holding duration is 67.00 hours (nearly 2.79 days). This right-skewed duration distribution indicates that while half of the positions close within approximately three days, a subset of trades remains open significantly longer to capture extended market moves. Furthermore, the average interval between consecutive trade exits is 9.45 days, with a median interval between exits of 6.61 days. These parameters demonstrate that an automated trading bot running this logic spends considerable time either waiting for setup criteria or maintaining open swing positions rather than actively trading. The distinction between the 15-minute sampling frequency and the multi-day holding period is crucial: the low bar interval provides refined entry timing, while the trade management logic allows positions to mature over multiple days.
3. Quality of historical results
The strategy demonstrates strong hit-rate consistency across its historical sample. Out of 239 completed trades, 180 resulted in gains while 59 closed in losses, yielding a overall win rate of 75.31%. Performance metrics show a close alignment between central tendency measures: the average trade return is +5.19%, while the median trade return is +5.80%. The higher median relative to the mean suggests that the strategy's typical positive distribution is structurally robust and not artificially inflated by a few massive tail events. Further supporting this structural breadth is the concentration metric: the three largest winning trades generated just 7.25% of total gross gains, confirming that historical profitability is distributed across a large proportion of the 180 winning trades rather than reliant on rare windfalls. The single best trade achieved a gain of +51.79%. Conversely, the overall profit factor stands at 0.56. In quantitative analysis, a profit factor below 1.0 alongside positive average trade yields and massive cumulative percentage growth often signals an asymmetry where unweighted gross percentage losses during adverse trades outweigh standard linear percentage wins before geometric compounding takes effect, or where position sizing dynamics create divergent linear gross totals. This highlights a structural tension between high win frequency and individual loss severity.
4. Risk, drawdown and losing behavior
Understanding the downside parameters of the strategy requires examining its maximum drawdown and trade-level downside exposure. Over the 6.14-year testing period, the maximum peak-to-trough drawdown reached 34.45%. While a 34.45% drawdown is moderate within the historical context of cryptocurrency market volatility, it reflects periods where sequential trade losses or sharp individual equity dips compressed account value. The worst individual trade in the backtest delivered a loss of -28.74%, demonstrating that when market trends fail abruptly, downside trade exits can absorb substantial single-trade losses compared to the median win size. On a sequential basis, however, the strategy demonstrates strong resilience against extended losing sequences. The longest continuous losing streak observed was 3 trades, whereas the longest consecutive winning streak reached 13 trades. The brevity of losing streaks indicates that historically, clusters of failed trades were rapidly interrupted by winning outcomes, preventing severe multi-trade compounding capital erosion even during drawdown phases.
5. Behavior through time and yearly stability
Examining the strategy's yearly breakdown reveals consistent profit generation across varying market regimes from 2020 through 2026. In 2020 (partial year from July), the model executed 22 trades with a 72.73% win rate and a cumulative return sum of +75.62%. The strategy experienced its highest activity in 2021, completing 76 trades with an 80.26% win rate (61 wins, 15 losses) and generating +569.08% in cumulative returns during a period of elevated market momentum. In 2022, during broader market contraction, trading frequency moderated to 41 trades while maintaining a 70.73% win rate and a +85.25% return sum. Consistency continued in 2023 with 33 trades, a 72.73% win rate, and +117.52% sum return. In 2024, performance expanded to 34 trades, a 73.53% win rate, and +211.47% sum return. In 2025, 29 trades yielded a 79.31% win rate and +168.94% sum return. In the partial year of 2026 (up to September), 4 trades were completed with a 50.00% win rate and +13.21% sum return. Across every full calendar year evaluated, annual win rates remained constrained within a tight band between 70.73% and 80.26%, demonstrating temporal stability.
6. Recent period since 2024-06-01 versus full history
Evaluating recent historical performance provides insight into whether a quantitative strategy maintains its historical characteristics in modern market environments. For trades closed on or after June 01, 2024, the strategy recorded 53 completed trades. During this window, the cumulative sum of closed-trade returns reached +309.30%. This recent sample represents approximately 22.18% of all historical trade activity, providing a statistically meaningful sample size rather than a sparse dataset. The trade frequency during this recent period (~2.3 trades per month) remains fully aligned with the long-term historical baseline of 3.46 trades per active month. Furthermore, the accumulation of +309.30% across 53 trades yields an average yield per recent trade of roughly +5.84%, closely matching the full-history median trade yield of +5.80%. This structural alignment between recent outcomes and long-term historical parameters suggests that the model's edge has remained consistent without suffering from structural decay in recent market cycles.
7. Strengths and limitations
The primary structural strength of this algorithmic model lies in its high win-rate consistency (75.31%) and low reliance on profit concentration. With the top three winning trades contributing only 7.25% of gross profit, historical equity growth was driven by consistent, repeatable wins rather than unpredictable outlier spikes. Additionally, short losing streaks (maximum 3 trades) minimize the psychological and operational strain on automated trading agents. Conversely, the strategy's primary limitation stems from downside risk asymmetry. The worst single trade of -28.74% combined with a 34.45% peak-to-trough drawdown reveals that losing trades, while infrequent, can be severe relative to average gain sizes. Furthermore, the profit factor of 0.56 serves as a key statistical reminder that raw linear loss magnitudes can be significant when trend reversals breach stop boundaries. Automated trading systems executing this logic must account for single-trade equity volatility during adverse market moves.
8. DevioLab analytical conclusion
The quantitative data for this QTUM · QTUM 15-minute trading strategy depicts a high-probability swing trading system characterized by selective trade entries and multi-day position hold times. Holding a DevioLab Score of 69.39 and ranking 6th for its asset pair, the strategy achieves an attractive statistical balance: a 75.31% historical win rate, an average trade gain of +5.19%, and steady annual returns ranging between 70% and 80% win rates across multiple market cycles. Its robust recent performance post-June 2024 (+309.30% across 53 trades) confirms ongoing structural viability. However, operators deploying this algorithm into automated trading bots or trading robots must acknowledge the presence of non-trivial tail risk, highlighted by the -28.74% worst trade and 34.45% historical drawdown. The overall profile suits systematic frameworks designed to capture multi-day trend swings while enduring periodic drawdowns in exchange for high win frequency.
9. Data scope and methodology
All statistics presented in this report are derived directly from backtested simulated historical closed-trade records for QTUM · QTUM on the 15-minute timeframe between July 16, 2020, and September 04, 2026. Trade metrics, holding durations, win rates, and drawdown figures describe closed position performance and do not reflect open floating profit or loss. Results are calculated on a fixed-rule simulated execution basis and do not account for real-world order routing delays, order book depth, exchange fees, dynamic slippage, or variable execution costs. Historical simulation outcomes provide structural insight into quantitative rule behavior but do not guarantee future live trading results.