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Stock instrument · execution via BinanceNFLX · Netflix, Inc. — Algorithmic Strategy Analysis on 15m Timeframe
A detailed quantitative review of the algorithmic strategy for Netflix, Inc. (NFLX), highlighting a 2997.35% cumulative backtest return over nearly 7 years with an 85% win rate and controlled drawdown.
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Strategy profile
The trading strategy designated as NFLXB 0 +2997.35% 1TRAD-JQC7 is engineered for Netflix, Inc. (NFLX) operating on a 15-minute price interval. It currently holds a DevioLab score of 83.98, ranking it number 1 among evaluated strategies for this specific ticker. The strategy focuses on selective market participation, targeting highly defined trade opportunities while filtering out intraday market noise. The historical dataset covers the timeline from September 16, 2019, through August 14, 2026, encompassing approximately 6.91 years of market history.
Trading rhythm and position duration
The system exhibits a highly conservative trading frequency. Over the entire 6.91-year evaluation period, the model executed only 40 completed trades. This translates to an average execution rate of 5.79 trades per year. Despite utilizing a 15-minute timeframe, the strategy cannot be categorized as scalping or high-frequency trading. Precise telemetry on position holding duration in hours is not recorded in the available statistical scope, but the low overall trade count confirms a long-horizon selective execution pattern.
Quality of historical results
The quality of the historical performance is underlined by a strong win rate. Out of 40 completed trades, 34 closed in profit and 6 ended in loss, yielding an 85% win rate. Cumulative net profit reached 2997.35%, which translates into an annualized return of 160.31%. The profit factor stands at 6.25. The average trade gain was 9.66%, while the median trade yielded 7.88%. The single best trade delivered a 31.77% gain. Notably, the top three winning trades accounted for 20.79% of total gross profits, reflecting a balanced distribution rather than reliance on isolated outlier events.
Risk, drawdown and losing behavior
The maximum historical equity drawdown recorded across the dataset was 18.33%. The worst individual trade loss was also -18.33%, demonstrating tight stop discipline and defined risk parameters. Streak analysis reveals strong momentum stability: the longest winning streak reached 10 consecutive profitable trades, whereas the longest losing streak was limited to just 1 trade. This highlights the algorithm's ability to prevent consecutive loss compounding.
Behavior through time and yearly stability
Due to the low overall trade count, granular calendar-year breakdowns are not available in the reporting metrics. However, the macro average of 5.79 trades per year indicates steady, low-frequency participation across the 6.91-year backtest. The strategy remains flat during uncertain market conditions and only commits capital when stringent criteria are satisfied.
Strengths and limitations
Key strengths of this strategy include its high win rate of 85%, robust profit factor of 6.25, and moderate peak drawdown of 18.33%. Gross profit generation is well distributed across winning trades. The primary limitation lies in the small sample size of 40 trades over nearly seven years. Furthermore, prolonged inactive periods, such as the period following June 2024, require patience and emotional discipline from an operator.
DevioLab analytical conclusion
Ranking first for Netflix, Inc. with a DevioLab score of 83.98, this strategy offers exceptional historical expectancy and drawdown protection. It operates as a precision instrument rather than an active generator of daily turnover. Investors and analysts should treat these findings as a study in low-frequency algorithmic filtering rather than a high-volume trading solution.
Data scope and methodology
All figures presented in this study reflect simulated historical performance of closed trades from September 16, 2019, to August 14, 2026. The start date marks the beginning of the evaluation dataset, not the asset listing date. Backtested metrics do not guarantee future live trading results, do not account for individual execution slippage, and do not constitute financial advice.