ARMBUSDT
Instrumento de ações · execução via BinanceHigh-Precision Swing Framework: Quantitative Analysis of the 15-Minute Strategy for ARM · Arm Holdings plc
This quantitative evaluation examines an algorithmic trading strategy evaluated on the 15-minute chart for ARM · Arm Holdings plc across a 2.94-year historical dataset spanning September 2023 to August 2026. Generating an 81.82% win rate across 55 completed trades, the strategy demonstrates a robust statistical profile characterized by an overall profit factor of 1.88 and a controlled maximum drawdown of 19.55%. Key structural findings include remarkable payoff symmetry—evidenced by an average trade return of +6.94% against a median trade return of +6.92%—and exceptional profit distribution, where the top three winning trades account for only 16.44% of gross historical gains. With 42 of its 55 trades occurring after June 1, 2024, the strategy provides substantial recent evidence for systematic deployment via automated trading bots and trading agents.
Análise em 20.09.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
Strategy profile
The evaluated quantitative architecture represents a specialized swing trading model built for ARM · Arm Holdings plc using a 15-minute execution interval. Assigned a DevioLab score of 71.56 and ranking 5th among strategies evaluated for the ARM ticker, this model operates on selective intraday bar signals to capture multi-day price moves. Over a historical backtest duration of 2.94 years—spanning from September 20, 2023, through August 27, 2026—the algorithm completed 55 closed trades. Out of these 55 total executions, 45 trades closed in profit while 10 trades concluded in loss, producing a high baseline win rate of 81.82%. In terms of total return generation, the strategy accumulated a aggregate closed-trade gain of +2671.50% across the full backtest window, translating to a nominal annualized rate of 921.49%. The strategy's overall profit factor stands at 1.88, reflecting a steady ratio between gross historical profits and gross historical losses. Designed as decision logic for automated trading bots, trading robots, or systematic execution agents, the model seeks to eliminate emotional bias by adhering to strict statistical rules for trade initiation and exit timing.
Trading rhythm and position duration
Despite utilizing a granular 15-minute bar chart for execution timing, the strategy's operational pacing aligns strictly with medium-term swing trading rather than high-frequency scalping. The model records an average transaction frequency of 18.73 trades per year, which corresponds to approximately 1.72 completed trades per active month. Trade exits occur with an average interval of 19.50 days, supported by a median exit interval of 16.58 days. This selective cadence indicates that the underlying algorithm filters out short-term market noise, waiting for high-probability setups before deploying capital. Position duration statistics reveal a right-skewed distribution in holding periods. The average holding time across all 55 completed trades is 229.96 hours (approximately 9.58 days), whereas the median holding duration is 147.75 hours (approximately 6.16 days). The divergence between the median and average holding times indicates that while most trades reach their completion criteria within roughly six days, a subset of trades remains open for extended multi-week periods to fully capture prolonged price trends. Consequently, trading robots deploying this logic must be calibrated to manage multi-day overnight market exposure rather than intra-day turnover.
Quality of historical results
An examination of trade-level returns highlights extraordinary mathematical symmetry within the strategy's payoff profile. The average trade return across all historical executions is +6.94%, which nearly mirrors the median trade return of +6.92%. In quantitative strategy evaluation, a tight alignment between mean and median expectations indicates that overall profitability is driven by a repeatable core distribution rather than skewed by extreme single-trade outliers. The single best trade produced a gain of +34.59%, while the worst single trade incurred a loss of -18.71%. Further proof of payoff quality is found in the concentration statistics: the three largest winning trades generated just 16.44% of the strategy's total gross profits. This exceptionally low concentration ratio demonstrates that the strategy's profit factor of 1.88 is built on broad-based performance across dozens of individual trades rather than luck from a few isolated market surges. For automated trading agents, this broad distribution lowers the structural reliance on any single market event.
Risk, drawdown and losing behavior
The strategy demonstrates disciplined capital protection metrics across its history, recording a peak-to-trough maximum drawdown of 19.55%. This drawdown level is particularly noteworthy when evaluated against the strategy's single worst trade loss of -18.71%, revealing that historical equity drawdowns were primarily driven by individual trade stop events rather than compounding sequences of severe losing streaks. The strategy's streak dynamics further reinforce its structural stability. The longest consecutive winning streak reached 11 trades, whereas the longest consecutive losing streak was restricted to just 2 trades. Achieving an 81.82% win rate alongside short losing streaks allows the simulated equity curve to recover rapidly from drawdowns. However, because a single worst loss can reach -18.71%, systematic risk controls and position-sizing parameters remain essential when configuring trading bots to execute this strategy.
Behavior through time and yearly stability
Evaluating the strategy across individual calendar years reveals consistent performance across changing equity market conditions. In 2023 (covering the initial sample period from September 20 to December 31), the model completed 5 trades, achieving a 100.00% win rate and a cumulative return sum of +66.42%. During 2024, trade activity increased to 18 completed trades, yielding 14 wins and 4 losses for a 77.78% win rate and a net sum return of +103.12%. In 2025, the strategy sustained similar operational volume, completing 17 trades with 13 wins and 4 losses, resulting in a 76.47% win rate and a sum return of +69.93%. In the final segment of the backtest through August 2026, the strategy generated 15 trades with 13 wins and 2 losses, producing an 86.67% win rate and a net return sum of +142.32%. The yearly breakdown confirms stable execution cadence (15 to 18 trades per full year) and high win-rate consistency across multiple years, demonstrating that the strategy's performance is evenly distributed rather than concentrated in a single fortunate market regime.
Recent period since 2024-06-01 versus full history
A critical benchmark for evaluating quantitative strategies is their performance in recent market windows. Out of the 55 total historical trades in this dataset, 42 trades were closed on or after June 1, 2024. These 42 recent trades generated a aggregate return sum of +280.38%, demonstrating that recent execution quality aligns closely with the overall full-history trajectory. Because 76.36% of all recorded trades occurred within the recent post-June 2024 window, the strategy's statistical conclusions are overwhelmingly backed by modern market data. Rather than relying on historical artifacts from years past, the high win rate and controlled drawdown profiles are validated by recent price action in Arm Holdings plc. This high density of recent trade evidence offers systematic traders strong statistical relevance when configuring automated trading algorithms.
Strengths and limitations
The primary structural strengths of this trading strategy center on its exceptional win rate (81.82%), low winner profit concentration (top 3 winners = 16.44%), and high payoff symmetry (mean +6.94% vs. median +6.92%). Short losing streaks (maximum 2 consecutive losses) and steady annual trade generation (15-18 trades/year) further reflect a disciplined quantitative edge that avoids dependency on market windfalls. Conversely, material limitations exist within the strategy's statistical sample. With 55 total completed trades across 2.94 years, the sample size is relatively moderate, meaning future trade results may experience statistical variance. Additionally, the average holding duration of 229.96 hours requires position management across multi-day overnight gaps and market sessions. Finally, the worst single historical trade of -18.71% demonstrates that individual losing trades can be substantial, necessitating strict risk management when deploying the model via automated trading logic.
DevioLab analytical conclusion
With a DevioLab score of 71.56 and a ranking of #5 for the ARM ticker, this 15-minute swing trading strategy represents a highly disciplined framework for systematic exposure to Arm Holdings plc. The core strength of the algorithm lies in its ability to maintain a superior win rate of 81.82% without relying on extreme outlier gains, as confirmed by its 16.44% top-three profit concentration and symmetrical mean-to-median returns. For quantitative researchers and developers building automated trading bots, trading robots, or algorithmic trading agents, the strategy offers a compelling model for medium-term trend capture. The combination of controlled maximum drawdown (19.55%), brief losing streaks, and steady yearly efficiency suggests that the underlying rules effectively exploit structural price tendencies in ARM. Systematic traders should nevertheless maintain realistic expectations regarding holding times and individual trade risk.
Data scope and methodology
The statistical findings in this report are based strictly on simulated historical backtest data for ARM · Arm Holdings plc on a 15-minute bar chart covering the period from September 20, 2023, through August 27, 2026. All trade metrics—including win rates, holding durations, profit factors, drawdowns, and return sums—are calculated from completed closed positions within this exact historical window. This research is intended solely for educational and analytical context regarding automated strategy design. The calculations do not incorporate external factors such as variable order-execution slippage, broker commission structures, margin borrowing rates, or live execution delay. Backtested historical gains do not guarantee future live trading performance.