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Stock Universe · mercado bursátil globalCBOE · CBOE Quantitative Analysis: Evaluating High-Win-Rate Multi-Day Automated Trading Logic
This quantitative research study examines the historical performance profile of the CBOE 0 +2265.35% 1TRAD-ALX1 algorithmic strategy for CBOE (CBOE). Operating on a 15-minute timeframe engine with multi-day position holding, the strategy achieved an overall closed-trade return of +2265.35% across 98 completed trades between September 2019 and September 2026. Characterized by an extraordinary 81.63% historical win rate, a maximum drawdown of 19.32%, and a DevioLab score of 78.28, the algorithm demonstrates strong consistency across diverse equity market conditions. While the strategy exhibits low gross profit concentration with the top three winning trades accounting for just 14.25% of total gains, its profit factor of 1.25 highlights a statistical tension between high hit frequency and downside trade magnitude. This analysis explores how the strategy's low trade frequency and long holding durations shape its risk-reward profile for implementation in automated trading bots and quantitative execution agents.
Análisis al 27.09.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The CBOE 0 +2265.35% 1TRAD-ALX1 algorithmic strategy is a core quantitative model designed for automated trading in CBOE (CBOE) equity markets. Ranked second overall for the asset on DevioLab with a DevioLab score of 78.28 out of 100, the strategy evaluates price action on a 15-minute candle interval while executing selective, multi-day position logic. Across its historical testing period spanning 7.03 years from September 16, 2019, to September 26, 2026, the strategy generated a cumulative closed-trade return of +2265.35%, corresponding to an annualized model figure of 296.39%. Built as systematic decision logic for trading bots, trading robots, and automated quantitative agents, the model completed 98 trades during the seven-year sample. The statistical structure of the algorithm prioritizes entry precision and elevated trade accuracy over high trade frequency, creating a distinct statistical profile characterized by extended holding times, moderate total trade count, and consistent profit generation across varying market cycles.
Trading rhythm and position duration
Although the algorithm receives structural pricing data on a 15-minute chart interval, its operational rhythm is deliberate and low-frequency. Over the 7.03-year evaluation window, the strategy executed 98 closed trades, yielding an average trade frequency of 13.94 trades per year, or approximately 1.42 trades per active month. Position holding times reflect a patient swing-trading structure rather than short-term scalp behavior. The average holding duration per position stands at 348.54 hours (approximately 14.5 days), while the median holding duration is 263.00 hours (roughly 11.0 days). Spacing between trade exits reflects similar cadence, with an average of 26.46 days and a median of 21.93 days separating closed positions. This extended temporal footprint confirms that the strategy utilizes intraday 15-minute technical inputs primarily to refine entry and exit timing, while relying on intermediate macro trends to capture multi-week price movements.
Quality of historical results
The strategy demonstrates exceptional directional accuracy, closing 80 winning trades out of 98 completed cycles to achieve a win rate of 81.63%. The average trade yield across all completed positions is +3.62%, closely aligned with the median trade yield of +3.05%. This close proximity between average and median figures indicates a balanced distribution of outcomes without distortion from extreme positive outliers. The strategy's best individual trade reached a gain of +23.88%, while gross profit concentration remains exceptionally low: the top three winning trades contributed only 14.25% of total gross historical gains. This low concentration confirms that overall strategy performance is sustained by broad-based operational consistency rather than reliance on rare windfall trades. However, the profit factor registers at 1.25. The structural gap between an 81.63% win rate and a 1.25 profit factor reveals that while losing trades occur infrequently (18 total losses), their individual magnitude tends to exceed that of the average winning trade.
Risk, drawdown and losing behavior
Risk statistics across the 7.03-year backtested dataset demonstrate controlled maximum equity retracements alongside manageable streak dynamics. The maximum observed drawdown across the entire history was 19.32%. Notably, the worst single closed trade in the dataset resulted in a loss of -19.32%, matching the maximum overall drawdown figure. This statistical alignment suggests that the strategy's peak historical equity drop was primarily driven by a single adverse trade realization rather than an extended succession of compounding losses. The algorithm recorded a longest losing streak of only 3 consecutive trades, compared to a maximum winning streak of 24 consecutive winning positions. This limited losing streak duration reduces the risk of prolonged equity stagnation, though the potential for individual trades to reach double-digit downside excursions highlights the necessity for rigorous position sizing when configuring automated trading agents running this logic.
Behavior through time and yearly stability
An examination of the yearly performance breakdown demonstrates remarkable multi-year consistency across fluctuating equity market regimes between 2019 and 2026. In 2019, the model recorded 2 trades with a 100.00% win rate and a cumulative return sum of +9.48%. Performance expanded during 2020 and 2021, generating 17 trades each year with win rates of 70.59% (+38.64% sum) and 88.24% (+78.41% sum), respectively. During the broader market headwinds of 2022, the strategy maintained a 70.59% win rate across 17 trades, yielding +33.73%. In 2023, 9 trades produced an 88.89% win rate (+38.05% sum). Subsequent years maintained strong delivery: 2024 generated 12 trades at a 75.00% win rate (+23.49% sum), 2025 delivered 12 trades at a 100.00% win rate (+50.23% sum), and 2026 produced 12 trades at an 83.33% win rate (+83.04% sum). Across all full operational years, annual win rates remained consistently above 70%, reflecting stable underlying quantitative mechanics.
Recent period since 2024-06-01 versus full history
Performance in the recent evidence window beginning June 1, 2024, provides vital insight into the contemporary efficacy of the strategy logic. Between June 1, 2024, and September 26, 2026, the strategy completed 31 trades, representing nearly one-third of the total 98 trades recorded across the full 7.03-year history. The cumulative sum of closed-trade returns during this recent period reached +145.39%. This sample size of 31 completed trades confirms active operational engagement in modern market conditions without evidence of decay in signal quality. Comparing recent activity to the full historical record reveals that the algorithm's core parameters have adapted effectively to recent market structural shifts, maintaining robust trade generation and high return density relative to its baseline historical averages.
Strengths and limitations
The principal statistical strength of the strategy lies in its outstanding hit rate of 81.63% combined with exceptional profit distribution consistency, as evidenced by a low top-three winner gross profit concentration of 14.25%. Additional strengths include a modest historical maximum drawdown of 19.32%, a brief maximum losing streak of 3 trades, and consistent multi-year profitability across all annual periods tested. Conversely, the strategy's primary limitation is its profit factor of 1.25, which reflects asymmetrical downside risk on individual trade exits, as demonstrated by a worst trade loss of -19.32%. Furthermore, the low trade frequency of approximately 14 trades per year and long average holding duration of 14.5 days require long-term capital allocation patience and expose positions to potential overnight or gap risks inherent in single-stock equity trading.
DevioLab analytical conclusion
The CBOE 0 +2265.35% 1TRAD-ALX1 strategy presents a highly compelling systematic model for investors and quantitative traders seeking high win-rate execution on CBOE. With a DevioLab score of 78.28 and a historical return profile of +2265.35% over 7.03 years, the algorithm offers a balanced combination of entry precision, low trade concentration, and controlled drawdown history. The strategy is well-suited for integration into deployment architectures for automated trading bots, trading robots, and quantitative trading agents designed to execute low-frequency swing trades. While operators must account for the profit factor of 1.25 and the potential for occasional deep individual stop exits, the model's consistent annual tracking and strong recent post-2024 results affirm its status as a robust core asset strategy.
Data scope and methodology
All analytical conclusions in this report are derived strictly from backtested historical closed-trade data for CBOE (CBOE) on a 15-minute candle interval across the period from September 16, 2019, to September 26, 2026. Cumulative percentage figures represent sum totals of completed closed trades over the evaluation window and do not constitute direct live account statements. The analysis excludes unstated external variables such as brokerage commissions, slippage, margin interest, or live execution infrastructure overhead. Past backtested performance is inherently historical and provides no guarantee or guarantee of future trading performance. This document is published solely for educational and quantitative research purposes and does not constitute financial, legal, or investment advice.