ERIE · Erie Indemnity Company
Stock Universe · 全球股票市场ERIE Algorithmic Strategy Analysis: Evaluating a High-Win-Rate 15-Minute Swing Trading Model
This quantitative study evaluates the top-ranked algorithmic trading strategy for ERIE stock, designated as the primary core model for the asset with a DevioLab Score of 91.12. Spanning a seven-year evaluation history from September 16, 2019, to September 14, 2026, the strategy generated a cumulative return of 5,321.05% across 74 completed trades, translating to an annualized return metric of 219.05%. Built upon a 15-minute candle resolution, the quantitative logic functions as a highly selective swing trading agent designed for automated execution environments. Key performance characteristics include an 85.14% win rate, a Profit Factor of 7.50, and an exceptional maximum drawdown containment of 6.47%. This report provides a detailed examination of the strategy's statistical architecture, holding period distribution, drawdown dynamics, historical yearly stability, and recent performance consistency since June 2024.
分析日期： 25.10.2026. 策略数据单独更新。阅读完整分析
Strategy profile
The quantitative model evaluated for ERIE stock stands as the top-ranked algorithm for the ticker on DevioLab, achieving a DevioLab Score of 91.12 under the core selection criteria. Built to process price action on a 15-minute timeframe, the system operates not as a high-frequency scalper, but as a patient, multi-day swing trading logic intended to serve as decision rules for automated trading bots, trading robots, or quantitative trading agents. Over the full backtested evaluation window from September 16, 2019, to September 14, 2026—a span of seven full years—the strategy executed a total of 74 closed trades. The macro performance profile is characterized by a high cumulative return of 5,321.05% and an annualized rate of 219.05%. What makes this profile distinctive is the combination of extreme selectivity and asymmetric return dynamics: out of 74 completed transactions, 63 closed in positive territory while only 11 resulted in losses, yielding an overall win rate of 85.14%. By establishing position signals on intra-day 15-minute candles while allowing trades to develop over macro horizons, the strategy achieves a structural separation between signal frequency and holding duration, filtering short-term noise to capture larger equity swings in ERIE.
Trading rhythm and position duration
Despite taking entries from a 15-minute chart resolution, the strategy's operational rhythm reflects a low-frequency, medium-duration swing methodology. The system generates an average of 10.58 trades per year, which translates to approximately 1.48 trades per active month. This low entry frequency highlights a high degree of signal filtering, requiring specific structural conditions to be met before initiating exposure. Position duration statistics further reinforce its multi-day orientation: the average holding time across all completed trades is 333.90 hours (approximately 13.9 days), whereas the median holding duration is 139.25 hours (roughly 5.8 days). The divergence between average and median holding times indicates that while more than half of the positions are resolved within roughly six days, a subset of winning trades is allowed to run for several weeks to capture extended trend expansions. Spacing between position exits is similarly spread out, with an average of 34.94 days and a median of 21.02 days between closed trades. For an automated trading agent or robot executing this logic, trading activity is marked by extended periods of inactivity punctuated by selective, highly targeted market commitments.
Quality of historical results
The mathematical quality of the strategy's trade returns is evidenced by a high Profit Factor of 7.50, reflecting a substantial ratio of gross historical profits relative to gross historical losses. The expected value per trade is strong, with an average trade return of 5.89% and a median trade return of 4.24%. The proximity of the median trade to the average trade demonstrates that positive performance is not driven by an isolated windfall, but represents a consistent, positive shift in baseline expectations. The single best trade achieved a gain of 40.63%, while the worst trade resulted in a loss of -6.47%. A critical diagnostic metric for quantitative strategies with high returns is winner concentration: in this strategy, the three largest winning trades account for 21.69% of total gross profit. This relatively modest concentration ratio confirms that the overall cumulative gain of 5,321.05% is distributed broadly across the 63 winning positions rather than being disproportionately dependent on a few lucky spikes. The strategic architecture reliably harvests moderate gains ranging around 4% to 6%, while periodically capturing larger trend extensions.
Risk, drawdown and losing behavior
Risk control metrics represent one of the most prominent features of this strategy dataset. The maximum peak-to-trough drawdown recorded across the seven-year testing history is 6.47%. Remarkably, this maximum portfolio drawdown is virtually identical to the worst individual trade outcome of -6.47%. This statistical alignment implies that the system rarely expERIEnces compounding sequential losses or severe equity degradation during adverse market phases. The losing trade behavior is further substantiated by the streak parameters: the longest historical losing streak observed was just 2 trades, whereas the longest winning streak reached 19 consecutive profitable trades. Out of 74 closed positions, only 11 ended in negative territory, and their magnitudes were strictly capped. The structural ratio between the average gain and worst-case loss reveals an effective risk management profile where downside exits are triggered promptly before catastrophic capital destruction occurs. For quantitative operators utilizing trading bots, this tight downside boundary provides exceptional capital preservation characteristics relative to the total realized upside.
Behavior through time and yearly stability
A temporal analysis of the strategy's yearly breakdown reveals evolving market engagement across different market regimes between 2019 and 2026. In 2019, during the initial partial testing window, the strategy recorded 2 trades, both winning (100% win rate), for a combined return of 5.95%. The year 2020 represented an exceptionally lucrative regime, generating 14 trades, all 14 of which were profitable (100% win rate), producing a sum of returns equal to 177.52%. In 2021, the system expERIEnced a notable contraction in performance during a consolidation period, registering 6 trades with 3 wins and 3 losses (50% win rate) for a modest net gain of 1.20%. Performance rebounded sharply in 2022, yielding a 65.53% return across 4 trades (75% win rate, 3 wins, 1 loss), followed by 2023 with 8 trades yielding 32.56% (75% win rate, 6 wins, 2 losses). The final three years of the dataset—2024, 2025, and 2026—demonstrate both increased signal frequency and strong return stability. The strategy generated 9 trades in 2024 (+66.75%, 100% win rate), 16 trades in 2025 (+42.47%, 87.50% win rate), and 15 trades in 2026 (+43.56%, 80.00% win rate). Across eight separate calendar years, every single year closed with positive net sum performance, showing durable multi-year stability.
Recent period since 2024-06-01 versus full history
Analyzing the strategy's recent execution window from June 1, 2024, through September 14, 2026, provides valuable insight into its performance under modern market conditions. During this recent window, the algorithm completed 37 trades, generating a cumulative return sum of 128.60%. Notably, these 37 recent trades account for exactly 50% of the total 74 trades executed across the entire seven-year dataset. This indicates a clear uptick in trade frequency in the post-June 2024 environment compared to the earlier historical years. Importantly, this acceleration in signal generation did not degrade performance quality: the recent sub-period maintained a high win rate (with 2024 reaching 100%, 2025 at 87.50%, and 2026 at 80.00%) and contributed substantially to the baseline growth. The strong recent sample size of 37 closed trades confirms that the model's edge has remained robust and active rather than deteriorating into stagnant or unpopulated historical backtest artifacts.
Strengths and limitations
The primary quantitative strengths of this ERIE strategy include its exceptional win rate of 85.14%, an outstanding Profit Factor of 7.50, and a tightly controlled maximum drawdown of 6.47%. The structural balance of the returns, evidenced by the top 3 winners accounting for only 21.69% of gross profits, ensures that performance relies on broad statistical edge rather than individual outlier events. Furthermore, the maximum losing streak of only 2 trades limits psychological and capital strain during execution. Conversely, several inherent limitations must be acknowledged. First, the total trade sample across seven years is relatively compact at 74 completed trades, yielding a low annual trade frequency of 10.58 trades per year. Periods of low trade activity, such as 2022 with only 4 trades or 2021 with 6 trades, require significant patience from operators deploying automated trading robots. Additionally, because positions have an average holding period of 13.9 days, exposure spans across overnight market closes and weekend gaps inherent to stock market trading, exposing open positions to gap risk that cannot be actively managed intra-day.
DevioLab analytical conclusion
The mathematical profile of the ERIE strategy fully justifies its DevioLab Score of 91.12 and its designation as the rank #1 core algorithm for ERIE stock. The model successfully combines high win frequency with strict downside containment, achieving a total return of 5,321.05% while keeping peak historical drawdown to under 6.50%. Its design characteristics—low trade frequency, medium-term multi-day holding periods, and robust performance across both early backtest years and the recent post-2024 period—make it an exemplary candidate logic for automated trading agents seeking high expected value per trade without exposing portfolio equity to severe compounding losses. While operators must account for long periods of trade inactivity and overnight equity exposure, the empirical evidence demonstrates a highly disciplined quantitative strategy with exceptional risk-adjusted dynamics.
Data scope and methodology
The statistics and empirical findings presented in this report are derived strictly from simulated historical backtest results for the asset symbol ERIE on a 15-minute timeframe, spanning from September 16, 2019, through September 14, 2026. All metrics—including trade counts, win rates, holding durations, yearly breakdowns, and drawdown statistics—reflect closed historical transactions within this simulated framework. In accordance with standard quantitative research methodology, these historical figures do not represent live execution on exchange accounts, nor do they incorporate unsupplied market variables such as order book slippage, explicit broker commission fees, or short-borrow borrowing costs. The start date of September 16, 2019, defines the boundary of the backtest data sample and does not represent the listing date or corporate inception of the underlying financial asset. Past backtested performance is an analytical tool for evaluating strategic logic and does not constitute a guarantee of future live market returns.