EFX · Equifax Inc.
Stock Universe · 全球股票市场EFX · EFX Algorithmic Strategy Analysis: High Win-Rate Model Achieves 6.25 Profit Factor Across Multi-Week Holding Horizons
This quantitative evaluation analyzes the historical backtest performance of the top-ranked algorithmic trading strategy for EFX (EFX) on the 15-minute chart, covering nearly seven years of data from September 2019 to August 2026. Designed as an automated decision mechanism suitable for algorithmic trading bots and autonomous agents, the strategy achieves a 75.93% win rate and an exceptional Profit Factor of 6.25 across 54 closed trades. By pairing selective entries with extended position durations averaging over 22 days, the system generated a cumulative return of +2493.42% while capping maximum drawdown at an unusually mild 8.65%. The analysis evaluates position duration, historical profit distribution, risk metrics, and recent execution since mid-2024.
分析日期： 23.10.2026. 策略数据单独更新。阅读完整分析
1. Strategy profile
The strategy analyzed in this report represents the primary algorithmic quantitative model for EFX stock on the 15-minute bar frequency, recorded under DevioLab selection logic as the top-ranked core strategy for the ticker with a DevioLab Score of 86.00. Evaluated across a dataset spanning September 16, 2019, through August 7, 2026—a duration of 6.89 years—the system completed 54 trade cycles. Across this history, it secured 41 winning trades against 13 losses, producing a win rate of 75.93%. The strategy's aggregate cumulative gain reached +2493.42%, translating to an annualized return metric of 67.05%. Developed to function as the core decision-making script for automated trading bots and execution agents, the framework filters price noise on short intraday timeframes to isolate high-probability structural swings in EFX, balancing high directional accuracy with stringent risk boundaries.
2. Trading rhythm and position duration
Although the decision logic operates on a 15-minute chart resolution, the structural cadence of the strategy is decidedly macro-oriented rather than high-frequency. The model executes an average of 7.83 trades per year, or approximately 1.15 trades per active month, establishing a highly selective operational pace. Position holding periods reflect a multi-week commitment: average holding time stands at 537.74 hours (roughly 22.4 days), while median holding duration is 358.13 hours (approximately 14.9 days). Exits occur infrequently, with an average interval between closed trades of 44.66 days and a median spacing of 33.70 days. This statistical profile demonstrates that while 15-minute candle data are utilized for entry precision, automated trading robots executing this logic remain exposed to directional market trend lines over multi-week horizons, avoiding over-trading and short-term whipsaws.
3. Quality of historical results
The overall return profile exhibits robust quality characteristics driven by favorable reward-to-risk asymmetry and consistent baseline performance. The average return per completed trade is +6.83%, while the median trade yield is +4.62%, indicating that positive expectancy is not dependent on extreme speculative spikes. The strategy achieved a Profit Factor of 6.25, meaning gross profits exceeded gross losses by more than sixfold. Additionally, the strategy's peak winner gained +47.39%, while the worst single trade closed at -8.65%. A crucial measure of structural health is winner concentration: the top three trades accounted for 25.81% of total gross profit. Because nearly three-quarters of gross profit was distributed across the remaining 38 winning trades, the historical outcome demonstrates broad-based stability rather than fragile dependency on a handful of lucky outliers.
4. Risk, drawdown and losing behavior
Risk containment is one of the strategy's most prominent analytical features. Across the entire 6.89-year execution record, the maximum drawdown reached only 8.65%, matching the depth of its single worst closed loss (-8.65%). This alignment between maximum single-trade loss and total peak-to-trough equity decline points to effective risk-capping mechanisms. Consecutive losing sequences were minimal, with the longest losing streak limited to 2 trades, whereas the longest winning streak extended to 8 consecutive positive trades. The high win rate of 75.93% combined with constrained single-trade losses provides a comfortable psychological and mathematical margin for automated trading agents, preventing deep capital impairment even during adverse market conditions.
5. Behavior through time and yearly stability
An examination of annual performance shows remarkable year-over-year consistency across varying equity market environments. In 2020, the model completed 7 trades with an 85.71% win rate, yielding +89.60%. The following year, 2021, generated 5 trades with a perfect 100% win rate and a combined return of +67.92%. Performance remained positive through challenging macro conditions in 2022 (7 trades, 71.43% win rate, +45.53% sum) and 2023 (8 trades, 75.00% win rate, +79.95% sum). In 2024, trade activity increased to 11 closed trades (72.73% win rate, +40.36% sum). The strategy experienced a temporary consolidation in 2025, recording 8 trades with a 50.00% win rate and a modest net gain of +8.05%, before rebounding strongly in 2026 with 8 trades, an 87.50% win rate, and +37.44% in net returns. This unbroken multi-year sequence of positive annual net returns reinforces the structural durability of the underlying logic.
6. Recent period since 2024-06-01 versus full history
Evaluating recent trade history from June 1, 2024, to August 7, 2026, provides insight into current model efficacy. During this recent window, 22 trades were completed, generating a cumulative return sum of +56.88%. This subset accounts for nearly 41% of all historical trades recorded since 2019, reflecting an increase in qualifying trade setups in recent market cycles. The average trade yield during this recent phase was approximately +2.59%, which sits below the full-history average of +6.83%. However, the consistent accumulation of positive gains across 22 executions demonstrates that automated trading bots utilizing this script continued to generate positive statistical edge without experiencing elevated drawdown.
7. Strengths and limitations
The primary strength of this algorithmic model lies in its exceptional Profit Factor (6.25), robust win rate (75.93%), and low peak drawdown (8.65%), complemented by a broad profit distribution where the top 3 winners represent only 25.81% of gains. The extended position duration (median ~14.9 days) shields the model from micro-slippage and execution noise. Conversely, the strategy's primary limitation stems from its low sample size: 54 completed trades over nearly seven years translates to roughly 7.8 trades annually. Traders deploying this logic via autonomous trading agents must exercise patience, as long intervals between trades (averaging 44.66 days) require extended periods of capital inactivity and disciplined resistance to manual intervention.
8. DevioLab analytical conclusion
The quantitative profile of this EFX strategy demonstrates how lower trade frequency can be leveraged to capture significant equity appreciation while suppressing portfolio volatility. Achieving a total backtested yield of +2493.42% with a peak drawdown under 9% places this system in an elite performance tier among equity-focused automated strategies on DevioLab. The strategy is well-suited for automated trading bots designed for medium-term position trading where minimizing turnover and capping downside exposure take precedence over high-frequency activity. While historical metrics do not guarantee future outcomes, the consistency displayed across distinct market regimes from 2019 through 2026 validates the statistical robustness of the model.
9. Data scope and methodology
The statistics analyzed in this document are derived exclusively from historical simulated trade logs generated for asset symbol EFX on 15-minute price history from September 16, 2019, through August 7, 2026. All trade metrics—including win rates, holding durations, yearly breakdowns, and recent performance totals—are computed based on closed trade events. This research is intended solely for educational and quantitative analysis contexts regarding automated decision rules for trading agents and trading robots. It does not reflect live execution slippage, broker fees, or capital management constraints, and must not be construed as financial or investment advice.