DIS · The Walt Disney Company
Stock Universe · mercado bursátil globalDIS · DIS Equity Algorithmic Strategy: Analyzing a 77.14% Win Rate and 1416.31% Historical Gain Across Multi-Day Swing Durations
This quantitative research report evaluates an automated swing-trading algorithmic strategy for DIS (DIS) built on 15-minute price data. Over a nearly seven-year backtested historical period comprising 105 completed trades, the strategy achieved a cumulative simulated return of 1416.31% with a profit factor of 2.50 and a high hit rate of 77.14%. By combining granular intraday timing with extended holding periods averaging 238.2 hours, the strategy generates low-frequency, high-expectancy trade setups. Analysis reveals an exceptionally balanced DIStribution of gains, where the top three winning trades account for only 14.07% of total gross profit, and positive net historical performance was maintained across every calendar year analyzed.
Análisis al 19.10.2026. Los datos de la estrategia se actualizan por separado.Leer análisis completo
Strategy profile
The algorithmic trading strategy evaluated in this report, identified by its system structure DIS 0 +1416.31% 1TRAD-BOW6, operates on the equity ticker DIS (DIS) using a 15-minute chart resolution. Developed as automated decision logic designed to power algorithmic trading bots, autonomous trading robots, and quantitative trading agents, the system focuses on capturing directional intermediate trends rather than noise-driven intraday oscillations. Over a backtested timeline spanning 6.99 years from September 16, 2019, through September 11, 2026, the strategy compiled a cumulative return of 1416.31%, yielding an annualized metric of 145.20% across 105 completed trades. DevioLab evaluates this strategy with a composite score of 79.27, placing it at rank 2 among all analyzed models for DIS. The system operates with moderate trade frequency, executing roughly 15 trades per calendar year, which reflects a highly selective filtering process that emphasizes position quality over trading volume.
Trading rhythm and position duration
Although the underlying data feed is structured on a 15-minute timeframe, the operational holding characteristics of this strategy belong to a multi-day swing framework. The average trade duration spans 238.20 hours, or approximately 9.9 calendar days, while the median holding time stands at 174.00 hours, equivalent to 7.25 days. The strategy exits positions infrequently, exhibiting an average interval between trade exits of 24.45 days and a median exit interval of 19.79 days. Across active trading months, the strategy averages just 1.48 closed trades. This combination of intraday trigger evaluation with multi-day position holding allows automated trading agents to refine entry timing on lower-timeframe signals while allowing macro trends in DIS to unfold without premature exit pressure. The extended gap between exits confirms that the system is built to sit out non-trending or noisy market phases, preserving capital until high-probability structures emerge.
Quality of historical results
The quality of historical returns generated by this strategy is characterized by a strong win rate coupled with a balanced payoff DIStribution. Out of 105 total completed trades, 81 resulted in gains and 24 ended in losses, establishing a hit rate of 77.14%. The profit factor of 2.50 reflects a robust statistical advantage, meaning that gross gains exceeded gross losses by two and a half times. The average trade performance sits at +2.96%, closely aligned with the median trade result of +2.65%, indicating that individual return DIStributions are tight and predictable rather than driven by extreme statistical skewness. Furthermore, the strategy's top three winning trades account for only 14.07% of gross profit. This low profit concentration is a vital quality metric, demonstrating that the strategy does not depend on a handful of black-swan tail events, but instead derives its long-term equity growth from a repeatable series of solid, broad-based trade outcomes.
Risk, drawdown and losing behavior
Risk parameters within the historical backtest indicate controlled capital preservation alongside occasional exposure to outlier adverse price action. The maximum overall drawdown recorded across the nearly seven-year history was 17.16%, a modest peak-to-trough decline relative to the 1416.31% total return. The strategy demonstrated strong streak resiliency, achieving a maximum winning streak of 12 consecutive trades compared to a maximum losing streak of 4 consecutive trades. However, a noticeable structural tension exists between the average gain and the worst individual trade loss. While the average trade return is +2.96%, the single worst historical trade produced a decline of -16.49%. Conversely, the best individual trade yielded a gain of +21.88%. The presence of a -16.49% worst trade illustrates that while losing trades are infrequent, automated execution models must account for tail-risk events where gap risk or swift trend reversals exceed typical exit boundaries before risk mitigation triggers.
Behavior through time and yearly stability
An examination of the yearly performance breakdown reveals consistent historical profitability across diverse equity market environments. In 2019, during the initial sample period, 2 trades yielded a net return of +0.80% with a 50.00% win rate. Activity accelerated in 2020, delivering 20 trades, an 85.00% win rate, and a net cumulative return of +90.27%. Performance remained strong in 2021 with 16 trades, an 81.25% win rate, and +45.06% total return. During the broader market headwinds of 2022, the strategy logged 18 trades with a reduced win rate of 61.11%, yet still achieved a positive total gain of +33.19%. Subsequent years maintained this positive trajectory: 2023 saw 15 trades (+35.26% sum return, 80.00% win rate), 2024 produced 11 trades (+25.67% sum return, 63.64% win rate), 2025 delivered 10 trades (+29.83% sum return, 80.00% win rate), and 2026 recorded 13 trades (+50.23% sum return, 92.31% win rate). Crucially, the strategy avoided losing calendar years entirely, demonstrating adaptable multi-year stability.
Recent period since 2024-06-01 versus full history
Analyzing the recent dataset from June 1, 2024, onward offers valuable insight into the strategy's contemporary performance and persistence. During this recent window, the system completed 30 trades, generating a combined return sum of +95.56%. The completion of 30 trades in approximately two years indicates a slight increase in trade frequency compared to the historical full-period baseline of 15.02 trades per year. The historical output during this recent window proves that the underlying signal logic has maintained efficacy in recent trading environments without suffering from alpha decay or regime misalignment. The continuous generation of positive returns across the 2024–2026 historical horizon reinforces the conclusion that the model's performance is grounded in persistent structural features of DIS price action rather than historical over-fitting.
Strengths and limitations
The strategy DISplays several core quantitative strengths, balanced by specific structural limitations that institutional and systematic traders must evaluate. Key strengths include a 77.14% win rate, an equity curve unbroken by negative calendar years, an exceptional profit factor of 2.50, and low profit concentration where the top three trades represent only 14.07% of gross profits. Position holding times averaging nearly 10 days allow automated trading bots to avoid high-frequency turnover and micro-structure market noise. Conversely, the primary limitation lies in the total sample size of 105 trades over seven years; while sufficient for baseline statistical evaluation, low frequency demands prolonged patience. Additionally, the magnitude of the single worst trade (-16.49%) relative to the average trade gain (+2.96%) highlights that risk managers employing this algorithm must maintain strict capital allocation rules to withstand single-trade adverse excursions.
DevioLab analytical conclusion
DevioLab ranks this algorithmic strategy #2 for DIS with an overall composite score of 79.27, reflecting its excellent risk-adjusted return profile and long-term consistency. The combination of a high hit rate (77.14%), a moderate maximum drawdown (17.16%), and robust performance in recent history (+95.56% across 30 trades since June 2024) positions this algorithm as a top-tier core logic candidate for deployment within automated trading robots and quantitative execution frameworks. System designers seeking consistent swing-trading exposure on DIS will find this strategy's balanced trade DIStribution highly favorable, provided that trade-sizing protocols appropriately account for low trade frequency and occasional fat-tailed drawdowns on individual losing positions.
Data scope and methodology
All analytical conclusions presented in this article are derived exclusively from historical simulated trade data spanning September 16, 2019, through September 11, 2026. Metrics are calculated on a completed closed-trade basis for the asset symbol DIS on a 15-minute candle interval. Percentage gains, profit factor, drawdown figures, and holding durations reflect backtested performance results and do not represent live execution, actual brokerage account fills, slippage, or transaction fee structures. Past performance is non-deterministic and cannot guarantee future quantitative results. This evaluation is provided for systematic research and educational purposes only and does not constitute financial or investment advice.