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Instrumento de ações · execução via BinanceQuantitative Evaluation of NFLX · Netflix, Inc. 15m Algorithmic Model: High Hit-Rate Position Dynamics and Outlier-Independent Yields
This analytical review examines a selective, core-designated 15-minute algorithmic strategy on NFLX (Netflix, Inc.) across a 6.9-year historical backtest window spanning from September 2019 to August 2026. Generating 101 closed trades over this period, the system achieves a cumulative closed-trade return sum of +6451.89% and an annualized metric of 277.18%, backed by a DevioLab score of 76.59 and ranking second overall for the ticker. The strategy exhibits a multi-day to multi-week holding posture, with positions remaining open for an average of 379.92 hours (approximately 15.8 days) and an average exit pacing of 25.21 days. Structurally, the strategy relies on a robust hit rate of 73.27% paired with a profit factor of 1.67 and a maximum peak-to-trough drawdown of 23.64%. Crucially, profit distribution is remarkably well-dispersed: the three largest winning trades contribute just 16.13% of total gross gains, demonstrating that historical performance is driven by repeatable statistical edge rather than extreme outlier dependence. Recent performance since June 1, 2024, reflects accelerated signal frequency, yielding 37 completed trades and a cumulative sum of +154.20%. This paper provides an institutional-grade breakdown of the strategy's statistical architecture, holding duration dynamics, yearly performance continuity, and risk metrics.
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Strategy profile
The evaluated quantitative trading model, identified under the strategy designation for NFLX (Netflix, Inc.) on a 15-minute candle interval, represents a selective trend-capturing system evaluated over a dataset spanning September 16, 2019, through August 11, 2026. Spanning approximately 6.90 years of backtested history, the strategy registered 101 closed transactions, earning a DevioLab score of 76.59 and placing second among strategies tracked for this ticker. Classified as a core strategy based on independent execution excellence, the system accumulated a sum of closed-trade returns equal to +6451.89%, translating to a annualized calculation of 277.18%. With 74 winning trades against 27 losing trades, the historical win rate stands at 73.27%. Despite utilizing a granular 15-minute price input grid, the model operates with low transaction density, averaging 14.63 completed trades per year or roughly 1.49 closed trades per active month. This structural separation between execution granularity and trade frequency indicates a filtering mechanism designed to isolate sustained price extensions while ignoring minor intraday oscillations.
Trading rhythm and position duration
A defining characteristic of this strategy is the contrast between its 15-minute timeframe execution grid and its extended holding profile. Positions are held for an average of 379.92 hours, which equals roughly 15.83 calendar days, while the median holding duration is 263.50 hours, or approximately 10.98 days. The spacing between trade exits reflects a similar pacing, with an average interval of 25.21 days and a median of 21.05 days between closed positions. Rather than engaging in rapid intraday scalping, the model functions as a multi-day position trading architecture that uses high-frequency price data for refined entry and exit precision. This moderate trade frequency—averaging 1.49 completed trades per month—results in long periods of market observation punctuated by held positions. The alignment between average holding time and exit spacing suggests the system rarely maintains simultaneous multi-position overlap, maintaining a measured operational cadency across market regimes.
Quality of historical results
The strategy demonstrates balanced return quality across its trade population, characterized by a profit factor of 1.67 and a positive trade expectancy. The average closed trade yields a gain of +4.80%, exceeding the median trade return of +3.45%. This positive gap between mean and median indicates a right-skewed payoff distribution where occasional large expansions elevate overall yield without distorting the baseline profitability. The single best trade achieved a return of +35.35%, whereas the single worst trade produced a loss of -14.53%. A key structural strength of this strategy lies in its profit concentration profile: the top three winning trades account for only 16.13% of aggregate gross profit. In quantitative research, a low top-three concentration ratio confirms that strategy performance is generated across a broad array of trades rather than being reliant on a few fortunate events. Combined with a 73.27% hit rate, the system exhibits consistency across varying market phases.
Risk, drawdown and losing behavior
Risk parameters within the historical backtest demonstrate effective downside containment relative to cumulative returns. The maximum drawdown recorded across the 6.9-year history reached 23.64%. While a 23.64% equity retracement represents meaningful interim exposure, it remains constrained when evaluated against the strategy's total cumulative closed trade return sum of +6451.89%. The losing behavior of the strategy is characterized by brief loss sequences; the longest consecutive losing streak was limited to 3 trades, whereas the longest winning streak extended to 11 consecutive profitable trades. The single worst trade of -14.53% illustrates that individual position risk is bounded, avoiding catastrophic single-trade drawdowns. The coexistence of a 73.27% win rate and a maximum 3-trade losing streak indicates that historical drawdowns were caused by a combination of minor losses and transient underperformance rather than prolonged periods of negative trade sequences.
Behavior through time and yearly stability
Annual distribution metrics reveal steady performance continuity across diverse equity market environments, including high volatility, prolonged market corrections, and strong trending periods. In 2019, during the initial partial testing window, the strategy executed 2 trades with a 50.00% win rate and a combined return sum of +11.35%. Subsequent full calendar years demonstrated enhanced consistency: 2020 delivered 13 trades with a 69.23% win rate (+79.32% sum); 2021 recorded 11 trades with a 72.73% win rate (+57.13% sum); 2022 generated 17 trades with a 64.71% win rate (+82.31% sum); 2023 produced 14 trades with a 78.57% win rate (+70.88% sum); 2024 generated 15 trades with an 86.67% win rate (+75.70% sum); and 2025 yielded 20 trades with an 80.00% win rate (+86.61% sum). In the partial 2026 period leading to August, the system registered 9 trades with a 55.56% win rate (+21.55% sum). Total trade volume grew from 11-13 trades per year in early periods to 15-20 trades per year in 2024-2025, while annual sum returns consistently ranged between +57% and +86% during full years.
Recent period since 2024-06-01 versus full history
Focusing on the recent evaluation window from June 1, 2024, to August 11, 2026, provides insight into recent strategy behavior. During this 26-month sub-period, the model completed 37 trades, generating a cumulative return sum of +154.20%. These 37 trades account for approximately 36.6% of all completed transactions in the 6.9-year dataset, indicating an increase in trade frequency during recent market conditions. Comparing this recent activity to full-history baselines shows that while the long-term trade frequency averaged 14.63 trades per year, the recent period averaged roughly 17.0 trades per year. The cumulative return sum of +154.20% over 37 recent trades yields a recent average return of approximately +4.17% per trade, close to the long-term historical mean of +4.80%. This confirms that recent profitability has remained consistent with full-history baseline norms.
Strengths and limitations
The primary analytical strength of this strategy is its high historical win rate of 73.27%, reinforced by an exceptionally low gross profit concentration of 16.13% across its top three winning trades. This combination demonstrates that performance is structural and distributed across dozens of trades over multiple years. Furthermore, a maximum losing streak of only 3 trades helps limit prolonged emotional or financial drawdowns. Conversely, a primary limitation stems from the absolute trade sample size: 101 closed trades over 6.9 years represents a relatively small statistical sample, averaging under 15 trades per year. Additionally, the average position duration of 379.92 hours subjects trades to overnight gaps, earnings announcements, and macroeconomic news events typical of individual equities like Netflix, Inc. The 23.64% maximum drawdown also highlights that high win rates do not eliminate the risk of equity pullbacks during adverse market regimes.
DevioLab analytical conclusion
The quantitative profile of this core-rated 15-minute NFLX strategy reflects a well-calibrated position trading framework designed for equity trend capture. Achieving a DevioLab score of 76.59 and a rank of 2 for the ticker, the system pairs a high historical hit rate (73.27%) with a modest profit factor of 1.67 and a reasonable drawdown profile of 23.64%. Its low concentration of gross profits among top winners (16.13%) demonstrates that historical returns were built on repeatable trade execution rather than single anomalous spikes. While the modest sample size of 101 trades requires careful risk allocation and monitoring, the strong performance stability observed across multiple calendar years—including robust returns since June 2024—supports its position as a high-quality quantitative trading candidate.
Data scope and methodology
This analysis relies exclusively on backtested simulated closed-trade statistics for NFLX (Netflix, Inc.) executed on a 15-minute price candle dataset from September 16, 2019, to August 11, 2026. All reported percentage gains, drawdown figures, holding times, and win rates are derived directly from closed-trade log records generated during historical simulation. Transaction costs, execution slippage, bid-ask spread friction, short-selling borrow fees, and order-routing delays are not modeled in these figures unless explicitly stated. The recent evidence window covering trades closed on or after June 1, 2024, reflects a precise subset of the complete historical record. Past backtested performance is inherently non-predictive of future live results, and these analytical findings are published strictly for quantitative research purposes rather than financial advice.