ADP · Automatic Data Processing Inc.
Stock Universe · global stock marketADP · Automatic Data Processing Quantitative Analysis: Evaluating an 84.62% Win Rate and 6.25 Profit Factor Algorithmic Model
This quantitative study examines the multi-year backtested performance of an algorithmic trading strategy engineered for ADP (Automatic Data Processing) on a 15-minute price series. Spanning nearly seven years from September 2019 through September 2026, the strategy generated a cumulative non-compounded return of +802.74% across 52 completed trades. Defined by extreme selectivity, the algorithm averages just 7.44 trades per year while maintaining a winning rate of 84.62% and an exceptional profit factor of 6.25. Rather than relying on rapid intra-day turnover, the strategy operates as a patient swing model with an average position holding time of 669.69 hours (approximately 27.9 days). Risk is heavily constrained by a single-loss maximum losing streak, though the strategy's peak historical drawdown of 23.64% matches its worst single trade exit, highlighting a structural tail-risk profile where individual losses can be pronounced. This paper provides an institutional-grade breakdown of the strategy's statistical distribution, trade cadence, payoff symmetry, and recent performance consistency for automated deployment contexts.
Analysis as of 12.09.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative model designated for ADP (Automatic Data Processing) operates on a 15-minute candle interval, yet its behavioral output diverges significantly from typical high-frequency intraday systems. Evaluated across a 6.99-year backtest window from September 16, 2019, to September 12, 2026, the strategy recorded 52 closed trades, achieving a cumulative historical return of +802.74% and an annualized return metric of 111.42%. Within the DevioLab scoring framework, this performance yields an overall score of 74.63, ranking it second among evaluated strategies for the ADP asset. Designed as a rule-based logic suitable for automated trading bots and execution agents, the system prioritizes high-conviction setup identification over frequent market interaction. By executing only when precise structural conditions align on the 15-minute timeframe, the model filters out substantial market noise, resulting in an exceptionally refined transaction profile tailored for institutional or systematic capital allocation.
Trading rhythm and position duration
A defining characteristic of this strategy is the stark contrast between its tactical 15-minute sampling frequency and its prolonged position duration. Despite monitoring intraday price action, the algorithm logs a modest 7.44 trades per year, which translates to approximately 1.27 trades per active month. Positions are held for considerable durations: the average holding period is 669.69 hours (roughly 27.9 calendar days), while the median holding period stands at 468.13 hours (approximately 19.5 days). This positive skew in holding duration indicates that while many trades resolve within three weeks, select positions are maintained for well over a month to fully capture extended trend moves. Furthermore, the spacing between trade exits is wide, averaging 49.56 days with a median of 36.03 days. For developers deploying automated trading robots, these metrics emphasize that the system operates far outside the realm of scalping. Capital remains idle or bound in single ongoing trades for extended intervals, demanding patient operational management and low turnover expectations.
Quality of historical results
The structural quality of the strategy's returns is reflected in its profit factor of 6.25 and an 84.62% win rate (44 winning trades against 8 losing trades). Crucially, the return profile demonstrates remarkable payoff symmetry between central tendency measures: the average trade return is +4.82%, while the median trade return is +4.86%. This tight alignment proves that historical performance is not an artifact of a few extreme positive outliers inflating the mean. Instead, the strategy consistently captures reliable mid-single-digit gains per trade event. Furthermore, the top three winning trades account for only 20.83% of total gross profit. This low concentration ratio confirms that profitability is broadly distributed across the 44 winning execution cycles rather than dependent on lucky single-event spikes. The strategy's best individual trade reached +28.70%, demonstrating that while the system routinely banks modest gains, it retains the capacity to participate in larger directional expansions when trend persistence allows.
Risk, drawdown and losing behavior
Risk metrics for the strategy reveal a unique dynamic where trade frequency loss clustering is minimal, but individual trade drawdown can be sharp. The maximum historical drawdown recorded across the 6.99-year dataset is 23.64%. Significantly, this figure exactly mirrors the strategy's worst single trade loss of -23.64%. The alignment of maximum drawdown with the worst trade metric, combined with a maximum losing streak of just 1 trade, illustrates that the strategy rarely experiences consecutive portfolio losses. In fact, its longest winning streak reached 15 consecutive positive exits. However, the magnitude of the single worst trade indicates that when an exit signal is triggered on a failing position, the realized loss can be substantial relative to the average winning trade magnitude of ~4.82%. Systematic trading agents utilizing this logic must account for this asymmetric tail risk, ensuring account leverage and risk parameters are structured to withstand occasional deep single-trade adverse movements.
Behavior through time and yearly stability
An examination of the strategy's annual performance breakdown demonstrates consistent profitability across varied market regimes. The strategy recorded positive net gains in every single calendar year from 2019 through 2026. Perfect 100% win rates were achieved in four distinct years: 2019 (2 trades, +10.93%), 2021 (4 trades, +54.35%), 2023 (3 trades, +15.48%), and 2024 (7 trades, +33.21%). Even during challenging macroeconomic environments, such as 2020 and 2022, the model maintained high efficiency, generating +50.69% across 9 trades (88.89% win rate) in 2020 and +29.85% across 9 trades (66.67% win rate) in 2022. Performance remained robust in 2025 with 8 trades yielding +29.51% (87.50% win rate) and in 2026 with 10 trades contributing +26.75% (70.00% win rate). Trade frequency remained low throughout, ranging between 2 and 10 trades per year, proving that annual performance stability stems from trade selectivity rather than volume.
Recent period since 2024-06-01 versus full history
Evaluating recent performance from June 1, 2024, to the end of the historical sample provides critical insight into current model efficacy. During this window, the strategy executed 24 completed trades, accumulating a combined return of +85.58%. Comparing this recent period to the full history reveals a noticeable increase in trade completions, as nearly 46% of all 52 historical trades closed within this ~2.25-year window. The average return per trade during this recent phase stands at approximately +3.57%, slightly below the full-history average of +4.82%, but accompanied by sustained high win rates. The continuous generation of positive returns in recent trading validates that the underlying structural patterns exploited by the algorithmic logic remain functional in modern equity market conditions rather than suffering from alpha decay.
Strengths and limitations
The primary strength of this quantitative model lies in its exceptional expectancy and payoff stability, highlighted by an 84.62% win rate, a 6.25 profit factor, and low gross profit concentration (20.83% in top 3 winners). The strategy's low trade frequency (7.44 trades/year) minimizes exposure to market noise and execution friction. Conversely, the model's primary limitation is its sparse sample size of 52 total trades over nearly seven years, which reduces statistical certainty compared to higher-frequency models. Additionally, the worst-case loss of -23.64% represents a severe single-event drawdown that contrasts with the modest average win size of +4.82%. Traders automating this strategy must accept long periods of market inactivity paired with the necessity to absorb infrequent but sizable stop-outs.
DevioLab analytical conclusion
With a DevioLab score of 74.63 and a rank of 2 for the ADP asset, this algorithmic strategy represents a highly disciplined, long-horizon swing trading framework built on 15-minute price data. Its combination of a high profit factor (6.25), steady annual growth, and robust recent performance (+85.58% across 24 trades since mid-2024) makes it a strong candidate for systematic deployment. However, the strategy's risk profile—characterized by an occasional large single-trade loss matching its 23.64% max drawdown—requires strict capital allocation controls. For quantitative trading bots and automated agents optimized for low turnover and high win probability, the model offers a historically robust logic.
Data scope and methodology
The metrics presented in this analysis are derived from backtested, historical simulated closed trades for ADP starting on September 16, 2019, and concluding on September 12, 2026. Cumulative returns represent the sum of individual closed trade percentages without dynamic compounding or reinvestment assumptions. The recent evaluation window specifically isolates trades with exit timestamps on or after June 1, 2024. All statistics reflect strict historical simulation outputs and do not represent live account trading, real-time order execution quality, slippage, or commission costs. Past backtested performance is strictly non-indicative of future financial results.