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Instrumento bursátil · ejecución mediante BinanceIREN Algorithmic Strategy Analysis: Evaluating a Multi-Day Swing Model on IREN Limited
An in-depth quantitative examination of a 15-minute algorithmic trading strategy applied to IREN Limited (IREN) across a 4.63-year backtested period from December 2021 to July 2026. With 91 completed trades, the model demonstrates a high hit rate of 81.32% paired with a profit factor of 1.67 and a cumulative historical return of +161,194.59%. Despite operating on 15-minute price data, the strategy functions as a medium-term swing trader, holding positions for an average of 205.45 hours (approximately 8.56 days). This evaluation explores the structural dynamics between high win probability, holding duration, downside loss asymmetry, and broad profit participation across market cycles.
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Strategy profile
The quantitative trading model evaluated under the identifier IRENB 0 +161194.59% 1TRAD-LPW8 targets IREN Limited (IREN) within the equity asset class. Operating on a 15-minute bar chart, the backtest encompasses a dataset spanning 4.63 years from December 13, 2021, to July 30, 2026. Across this historical horizon, the strategy generated a total of 91 closed trades, attaining a DevioLab performance score of 58.21 and placing 8th in overall rank among strategies analyzed for this ticker. The dataset records a total cumulative trade return of +161,194.59%, reflecting the mathematical compounding of individual trade performance over the complete sample size. Characterized by low trade frequency and selective market engagement, the profile combines sub-hourly chart sampling with multi-day position holding periods.
Trading rhythm and position duration
Although built on a 15-minute chart resolution, the strategy exhibits the behavioral characteristics of a multi-day swing system rather than a fast intraday model. Over the 4.63-year evaluation window, the strategy executed 91 trades, translating to an annual frequency of approximately 19.66 trades per year, or roughly 1.78 trades per active month. The temporal spacing between position exits averages 18.75 days, with a median interval of 16.11 days, confirming that position exits occur on a measured bi-weekly cadence rather than in rapid clusters. Position duration statistics further illustrate this swing-trading profile. The average holding duration per trade stands at 205.45 hours (approximately 8.56 days), while the median holding duration is 144.50 hours (roughly 6.02 days). The right-skewed relationship between median and average holding times indicates that while most positions exit within six days, select trades remain open for extended multi-week periods to capture larger trend moves. The strategy utilizes 15-minute price action not for high-frequency turnover, but to fine-tune trade management across multi-day holding periods.
Quality of historical results
The core performance driver of this strategy is its win rate of 81.32%, derived from 74 winning transactions against 17 losing outcomes. Individual trade returns show consistency, with an average trade gain of +9.66% and a median trade return of +10.30%. The alignment between average and median returns indicates a symmetrical profit distribution among winning positions, minimizing reliance on extreme positive outliers. This broad distribution is highlighted by profit concentration metrics: the three largest winning trades generated +56.34% for the best individual trade, yet together the top three winners account for only 11.87% of total gross profit. This confirms that historical gains were generated across a broad spectrum of successful trades rather than being concentrated in a few isolated events. However, the profit factor of 1.67 presents an analytical contrast to the high 81.32% win rate. This divergence stems from downside payoff asymmetry: while 81.32% of trades close in profit with a median return of +10.30%, the 18.68% of losing trades carry greater magnitude, anchored by a worst single trade drawdown of -38.53%. The overall profit factor reflects the ongoing dynamic between frequent moderate gains and larger individual losses.
Risk, drawdown and losing behavior
Evaluating historical drawdown reveals the structural risk characteristics of the algorithm. The strategy recorded a maximum peak-to-trough drawdown of 42.20%. When viewed alongside the worst historical trade loss of -38.53%, it becomes evident that portfolio equity drawdowns are heavily influenced by severe individual trade losses rather than extended series of consecutive failures. Consecutive trade behavior supports this conclusion. The strategy experienced a maximum winning streak of 13 trades, compared to a maximum losing streak of just 2 trades. The brevity of losing streaks highlights that cluster risk—the risk of multiple consecutive failed entries—remains low historically. Instead, drawdown risk is driven by loss severity. Because losing positions can extend to negative magnitudes nearly four times larger than the median winning trade, risk management relies heavily on controlling individual loss tail-risk during adverse market movements.
Behavior through time and yearly stability
An examination of yearly performance metrics demonstrates consistency across changing market environments from late 2021 through mid-2026. Following an initial launch period in 2021 containing 1 winning trade (+5.46%), full-year performance stabilized with high win rates and positive return sums across all subsequent calendar years. In 2022, across 20 completed trades, the strategy achieved a 70.00% win rate (14 wins, 6 losses) and a cumulative trade sum of +107.42%. Performance strengthened in 2023, recording 18 trades with an 88.89% win rate (16 wins, 2 losses) and a cumulative trade sum of +258.71%. The year 2024 maintained similar results, delivering 21 trades, an 85.71% win rate (18 wins, 3 losses), and a trade sum of +202.33%. In 2025, the strategy executed 20 trades with an 80.00% win rate (16 wins, 4 losses) and a trade sum of +198.05%. The partial year of 2026 shows 11 trades through July 30, recording an 81.82% win rate (9 wins, 2 losses) and a trade sum of +107.37%. Annual trade volume remained steady at 18 to 21 trades per full year, reflecting stable entry criteria across varying market conditions.
Recent period since 2024-06-01 versus full history
Analyzing recent market behavior from June 1, 2024, to July 30, 2026, provides insight into the strategy's current effectiveness. During this window, the model completed 43 closed trades, representing 47.25% of its entire historical trade count. The cumulative sum of trade returns during this recent period reached +424.97%. Comparing this recent window to the complete 4.63-year dataset demonstrates sustained trading activity. With 43 trades occurring over approximately 26 months, the recent trading frequency of ~1.65 trades per month closely matches the full-history baseline of 1.78 trades per active month. The ongoing trade generation and positive cumulative trade returns confirm that the strategy's statistical edge has remained active in recent market cycles.
Strengths and limitations
The primary strength of the strategy lies in its high hit rate of 81.32% and the stability of its profit distribution. With the top three winning trades contributing just 11.87% of gross profits, performance does not rely on rare outlier gains. Additionally, a short maximum losing streak of 2 trades and steady annual output across multiple years demonstrate historical consistency. Conversely, the primary limitation centers on negative payoff asymmetry. The average trade gain of +9.66% and median gain of +10.30% contrast sharply with a maximum single trade loss of -38.53%. This tail risk dampens the overall profit factor to 1.67 despite high accuracy, and contributes directly to a maximum equity drawdown of 42.20%. Additionally, the low annual trade frequency of 19.66 trades requires patience, as multi-day holding durations mean capital remains committed to single positions for an average of 8.56 days.
DevioLab analytical conclusion
With a DevioLab score of 58.21 and a ticker ranking of 8th for IREN Limited, this algorithmic strategy offers a distinctive quantitative profile. It successfully pairs sub-hourly chart analysis with multi-day swing execution, delivering high accuracy and consistent annual trade generation across a 4.63-year backtest horizon. The strategic trade-off is clearly defined by its metrics: exceptional accuracy (81.32% win rate) and broad profit distribution are balanced by downside tail risk during losing trades. The resulting 1.67 profit factor and 42.20% maximum drawdown reflect a model where losses, though infrequent, exert a noticeable drag on overall compounding efficiency. Investors and quantitative researchers evaluating this model must weigh the benefit of frequent winning trades against the management of infrequent but sizeable drawdowns.
Data scope and methodology
This analysis is based entirely on historical simulated backtest statistics for the strategy IRENB 0 +161194.59% 1TRAD-LPW8 applied to IREN Limited (IREN) on a 15-minute timeframe from December 13, 2021, to July 30, 2026. All reported metrics—including win rates, holding durations, trade returns, profit factors, and annual breakdowns—are computed strictly from the 91 closed trades contained in the sample dataset. Recent period performance reflects trades exiting on or after June 1, 2024. These statistics represent historical backtested research only and do not constitute financial advice, live trading records, or guarantees of future performance.