ACN · Accenture PLC Class A
Stock Universe · global stock marketQuantitative Strategy Analysis: ACN 15-Minute Automated Trading Logic Achieves 89.13% Historical Win Rate and 6.25 Profit Factor
This analytical report evaluates a top-ranked algorithmic trading strategy designed for ACN stock on a 15-minute timeframe over a 6.99-year backtest horizon from September 2019 to September 2026. Generating an aggregate net simulated return of +688.98% across 46 completed trades, the strategy demonstrates an exceptionally selective trading rhythm paired with remarkable distribution symmetry. With 41 winning trades out of 46, an 89.13% win rate, a Profit Factor of 6.25, and a maximum historical drawdown restricted to 12.93%, the algorithm exhibits rare statistical resilience. The strategy relies on multi-day position holding rather than high-frequency turnover, averaging 405.74 hours per trade. A detailed examination of profit distribution reveals that the top three winning trades account for just 17.21% of gross profit, confirming that historical performance is driven by consistent, broad-based positive expectancy rather than extreme statistical outliers. With a DevioLab Score of 81.89, ranking first for its ticker, the logic provides a robust quantitative model suitable for implementation within automated trading bots and algorithmic trading agents.
Analysis as of 11.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The evaluated algorithmic strategy, designated as ACN 0 +688.98% 1TRAD-YAO7, operates on a 15-minute chart interval for ACN stock within equity market dynamics. Evaluated across a dataset spanning nearly seven years from September 16, 2019, to September 11, 2026, the strategy achieved a cumulative historical profit of +688.98%, translating to an annualized return metric of 145.77%. Out of 46 completed trade cycles recorded during this nearly seven-year backtest, 41 closed in positive territory, yielding an overall win rate of 89.13%. The performance profile earns a DevioLab Score of 81.89, placing it at the number one rank among evaluated quantitative models for this specific asset symbol. The strategy is built around high selectivity, using granular 15-minute price action to pinpoint timing while executing a macro swing trajectory. As a framework intended for deployment via automated trading bots, trading robots, or quantitative trading agents, the core value proposition rests on minimizing market exposure while maximizing trade-level expectancy. Rather than seeking frequent intraday turnarounds, the algorithm acts as a patient filter, waiting for high-probability structural setups before entering positions.
Trading rhythm and position duration
Despite utilizing a 15-minute price bar interval for entry and exit signals, the statistical footprint of this strategy reflects a multi-week swing or position holding framework rather than intraday scalping. Over the 6.99-year evaluation window, the strategy completed only 46 trades, averaging approximately 6.58 trades per year or 1.18 trades per active month. The time spent in individual trades is substantial: the average holding duration stands at 405.74 hours (approximately 16.9 calendar days), while the median holding duration is 240.50 hours (roughly 10 calendar days). This divergence between mean and median holding duration indicates that while half of the positions resolve within ten days, select trades remain open for extended multi-week trends to capture larger price movements. The temporal spacing between trade completions further reinforces this patient rhythm. The average duration between closed exits is 52.98 days, with a median spacing of 39.99 days. For an automated trading robot or trading agent, this low-turnover cadence means prolonged periods of flat cash positions interrupted by deliberate, low-frequency position entries. The 15-minute timeframe serves solely to refine entry execution precision rather than to drive trade frequency.
Quality of historical results
The statistical distribution of trade returns indicates exceptional payoff stability and exceptional broad-based edge. The average trade yield across all 46 closed positions is +4.96%, which aligns almost perfectly with the median trade yield of +4.85%. This tight alignment between average and median expectations proves that the aggregate performance is not skewed by single unusual market spikes, but is instead built upon highly repeatable trade outcomes. The best single trade achieved a gain of +15.49%, while the worst trade resulted in a loss of -12.93%. The strategy delivered an overall Profit Factor of 6.25, reflecting gross profits that dwarf gross losses by more than six to one. Furthermore, concentration risk is remarkably low: the top three winning trades collectively generated only 17.21% of the strategy's total gross profit. In many quantitative models, a small fraction of outlier trades accounts for 40% to 60% of total gains; here, gains are distributed evenly across the 41 winning transactions. This structural symmetry suggests that the strategy's underlying logic captures systemic return patterns in ACN stock rather than relying on fortunate black-swan events.
Risk, drawdown and losing behavior
Risk containment represents one of the strongest statistical dimensions of this quantitative profile. The maximum peak-to-trough drawdown experienced throughout the entire 6.99-year backtest period was held to 12.93%. Strikingly, this maximum drawdown metric matches the exact percentage of the single worst historical trade (-12.93%), indicating that peak drawdown was directly driven by that isolated trade event rather than compounded by sequential losses. The strategy's losing behavior is characterized by extreme isolation of drawdowns: the longest winning streak reached 11 consecutive profitable trades, while the longest losing streak was capped at exactly 1 trade. Across 46 completed trades, the strategy never experienced back-to-back losses. Out of 46 attempts, only 5 positions ended in net losses. For trading agents and automated execution systems, single-loss isolation significantly reduces the psychological and equity stress typically associated with compounding drawdowns. However, because individual losing trades can reach -12.93%, position sizing must be calibrated to ensure that single-trade adverse movements do not disrupt capital preservation.
Behavior through time and yearly stability
An analysis of the yearly performance breakdown demonstrates consistent profitability across distinct market environments, with six out of seven calendar years yielding net positive returns. In 2020, the strategy completed 6 trades with a 100% win rate, yielding +39.57%. In 2021, activity moderated to 3 trades, again achieving a 100% win rate for +31.73%. The year 2022 generated 5 trades with 4 wins and 1 loss (80% win rate), accruing +27.98% despite broader equity market headwinds. In 2023, 5 trades produced a 100% win rate and +19.17% profit. Performance in 2024 saw 8 trades with 7 wins and 1 loss (87.5% win rate), generating +32.59%. The only net negative calendar year in the sample occurred in 2025, where the strategy logged 7 trades with 5 wins and 2 losses (71.43% win rate), ending with a slight deficit of -2.20%. This minor contraction was swiftly offset in 2026, which recorded 12 trades, 11 wins, and 1 loss (91.67% win rate), generating +79.54%. The annual data illustrate that the model's positive expectancy remains stable across varying volatility regimes without collapsing during difficult market cycles.
Recent period since 2024-06-01 versus full history
A dedicated analysis of the recent sample window from June 1, 2024, to September 11, 2026, reveals a substantial concentration of trade activity relative to the strategy's overall history. Exactly 23 of the 46 total completed trades—representing 50% of the entire historical sample—were closed within this recent window. During this period, the strategy generated a cumulative return sum of +107.66%. Comparing this to the full history shows that while the first 4.7 years generated 23 trades, the final 2.25 years matched that exact count. This acceleration in completed trades indicates an increase in signal triggering frequency under recent market volatility patterns for ACN stock. Crucially, this heightened frequency did not degrade performance quality; the recent window delivered over 100% in net gains while upholding the high win-rate characteristics observed in the full dataset. The presence of 23 recent closed trades provides a substantial, statistically meaningful sample confirming that the strategy's operational edge has remained fully intact in recent market conditions.
Strengths and limitations
The primary statistical strength of this strategy lies in its high win rate of 89.13% combined with a Profit Factor of 6.25 and a maximum drawdown limited to 12.93%. The close proximity of the average trade (+4.96%) to the median trade (+4.85%) and the low top-three profit concentration (17.21%) demonstrate high operational consistency and low vulnerability to outlier dependence. Additionally, the complete absence of multi-trade losing streaks (maximum losing streak of 1) highlights superior trade filtering. Conversely, notable limitations stem from the relatively small total sample size of 46 trades over nearly seven years. A trade frequency of 6.58 trades per year requires significant patience from operators deploying automated trading bots. Furthermore, because average holding durations extend to 405.74 hours (~16.9 days), market participants are exposed to overnight gap risk and macroeconomic announcements occurring while positions remain open. Finally, because single losses can reach -12.93%, strict risk management remains mandatory despite the high historical hit rate.
DevioLab analytical conclusion
The quantitative strategy ACN 0 +688.98% 1TRAD-YAO7 stands out as an exceptionally disciplined model for trading ACN stock. Ranking first among evaluated strategies for this ticker with a DevioLab Score of 81.89, its design successfully pairs intraday 15-minute execution timing with multi-day swing holding periods. The strategy demonstrates robust risk containment, evidenced by a 12.93% maximum drawdown that matches its single worst trade loss, alongside an 89.13% win rate and a 6.25 Profit Factor. The empirical evidence shows that gains are broadly distributed across trades rather than reliant on extreme statistical outliers. For quantitative developers seeking decision logic for automated trading robots, trading agents, or systematic algorithms, the strategy offers an appealing blueprint defined by low trade turnover, high payoff consistency, and sustained edge across multiple yearly market cycles.
Data scope and methodology
This analysis is based strictly on simulated historical backtest results recorded between September 16, 2019, and September 11, 2026, for ACN stock on a 15-minute chart interval. All reported figures, including total return (+688.98%), win rate (89.13%), holding times, drawdowns, and annual breakdowns, derive exclusively from closed trade performance metrics provided in the dataset. Historical execution data do not include live order book slippage, execution fees, broker commissions, or real-time liquidity constraints. The recent window metric accounts for positions closed on or after June 1, 2024. Historical backtested outcomes provide statistical insight into past model behavior but do not guarantee future performance. This publication is intended strictly for educational and analytical research purposes and does not constitute financial, legal, or investment advice.