NVDABUSDT
Stock instrument · execution via BinanceNVDA · NVIDIA Corporation: Quantitative Backtest Analysis of a High-Precision, Low-Frequency 15-Minute Strategy
This quantitative evaluation analyzes a highly selective 15-minute algorithmic strategy applied to NVIDIA Corporation (NVDA) equity data across a 6.93-year test horizon from September 2019 to August 2026. Generating a DevioLab score of 82.10 and ranking second overall for the ticker, the strategy exhibits an exceptional historical win rate of 87.23% alongside a total backtested gain of 43,803.99% and a profit factor of 5.94. However, the statistical structure reveals a profound tension between overall hit rate and performance concentration: exactly 47 trades were executed in total, averaging only 6.78 trades per year, with the top three winning positions generating 60.58% of all gross profits. Furthermore, the strategy has recorded zero completed trades since June 1, 2024, emphasizing that while risk metrics such as a maximum drawdown of 10.59% appear remarkably tight, the total sample size remains constrained and heavily reliant on rare, outlier gains.
Analysis as of 21.08.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The quantitative strategy designated for NVIDIA Corporation (NVDA) operates on intraday 15-minute price bars within the stock market asset class. Across a multi-year historical dataset spanning 6.93 years between September 16, 2019, and August 21, 2026, the strategy generated an aggregate backtested profit of 43,803.99%, yielding an annualized return metric of 836.86%. Based on these output metrics, DevioLab assigns this model a proprietary quality score of 82.10, placing it at rank 2 among analyzed strategies for the NVDA ticker. The core statistical profile is defined by extreme entry selectivity: over nearly seven years of history, the algorithm executed only 47 completed trades. Out of these 47 total trade events, 41 closed as profitable trades, resulting in a high win rate of 87.23%, while only 6 trades ended in losses. This historical profile establishes a strategy that operates with low frequency but high accuracy, seeking distinct market conditions rather than continuous trading participation.
2. Trading rhythm and position duration
Despite operating on a 15-minute chart resolution, the strategy does not display the characteristics of high-frequency trading or rapid scalping. Instead, the pacing is characterized by prolonged periods of inactivity, recording a trade frequency of approximately 6.78 trades per year. Because specific metrics for average holding hours, median holding hours, and average days between exits are not present in the primary dataset, exact position durations cannot be reported. However, the low annual trade count demonstrates that signals are triggered very infrequently relative to the thousands of 15-minute candles evaluated over nearly seven years. The strategy's operational rhythm is therefore defined by long periods of market observation punctuated by occasional entry events. This structural rarity in signal generation directly shapes the empirical sample, meaning that each individual trade carries a high weight in the strategy's overall statistical distribution.
3. Quality of historical results
A detailed inspection of trade distribution reveals a significant statistical divergence between typical outcomes and top-tier gains. The average trade performance across all 47 completed transactions stands at 22.09%, whereas the median trade performance is substantially lower at 7.67%. This large gap between mean and median performance points directly to positive skewness caused by exceptional outlier gains. The single best trade achieved an impressive return of 556.53%, while the worst recorded loss was limited to -10.59%. Furthermore, the three largest winning trades accounted for 60.58% of the total gross profit generated throughout the entire 6.93-year evaluation period. While a overall profit factor of 5.94 underscores strong total gain generation relative to cumulative losses, the statistical dependency on a tiny cluster of massive winners is a critical consideration. The high win rate of 87.23% proves that small-to-moderate gains occur consistently, but the vast majority of net compounding stems from a very small subset of trades.
4. Risk, drawdown and losing behavior
From a downside perspective, the strategy demonstrates tight risk parameters within the historical backtest. The peak-to-trough maximum drawdown was constrained to 10.59%, matching closely with the worst individual trade outcome of -10.59%. Across the 47 trades, the strategy experienced a maximum consecutive losing streak of just 1 trade, contrasted against a maximum winning streak of 14 consecutive positive trades. The total number of losing trades remained low at 6 occurrences. This profile suggests that drawdown events historically coincided with isolated losing trades rather than compounding series of adverse outcomes. Nevertheless, because total trade frequency is sparse, the 10.59% drawdown reflects historical risk behavior across a small sample size. While the profit factor of 5.94 and low drawdown indicate strong capital preservation historically, the limited number of loss events means the strategy's resilience against prolonged choppy market conditions cannot be fully validated by a larger sample.
5. Behavior through time and yearly stability
An evaluation of multi-year performance consistency requires reviewing trade distribution across the entire 6.93-year timeline. Detailed annual breakdown data broken down by individual calendar years is not supplied within the source statistics. However, inferences can be drawn from the overarching timeline and trade totals. With 47 trades occurring across almost seven full years, the strategy averaged fewer than seven closed positions per calendar year. This sparse trade activity indicates that historical returns were not evenly distributed month-to-month, but rather accumulated during specific market regimes when entry conditions were met. The overall balance between 41 wins and 6 losses over a 6.93-year horizon shows sustained performance over multiple years, yet the exact year-by-year volatility and trade distribution remain unquantifiable due to the absence of granular yearly performance arrays.
7. Strengths and limitations
The primary historical strength of this strategy lies in its outstanding payoff metrics and low drawdown profile. An 87.23% win rate, a 5.94 profit factor, a maximum drawdown of 10.59%, and a 14-trade winning streak represent compelling historical risk-adjusted efficiency. Furthermore, the capacity to capture large upside moves, evidenced by a best trade of 556.53%, enabled dramatic overall growth of 43,803.99%. Conversely, the major limitation is sample size and payoff concentration. With only 47 trades generated over nearly seven years, statistical confidence is constrained. The concentration of 60.58% of gross profit within just three trades indicates that missing or mismanaging a minute fraction of entry signals would fundamentally alter historical strategy returns. Additionally, the median trade result of 7.67% reflects a far more modest baseline expectation for typical positions than the 22.09% average trade suggests, and the complete lack of trades since June 2024 introduces recent observation risk.
8. DevioLab analytical conclusion
The NVDA 15-minute algorithmic strategy ranks 2nd on DevioLab's ticker evaluation with a score of 82.10, earned through exceptional historical equity growth and disciplined loss control. Quantitatively, the strategy achieves a rare combination of an 87.23% win rate and a massive overall return profile. However, rigorous quantitative analysis must highlight that this performance is built upon extreme signal selectivity and structural concentration. With 60.58% of gross profits tied to three trades and only 47 total trade events in nearly seven years, the statistical distribution relies heavily on fat-tail upside capture. The total absence of trades post-June 2024 further underscores that this model functions as a sparse, high-conviction system rather than a steady compounding engine. Market participants studying this model should recognize that its historical success stems from strict filtering and occasional windfall trades, rather than frequent, high-volume trading edge.
9. Data scope and methodology
This quantitative review is based exclusively on backtested historical performance data provided for NVIDIA Corporation (NVDA) on a 15-minute bar timeframe across the evaluation period from September 16, 2019, to August 21, 2026. All reported metrics, including the total profit of 43,803.99%, profit factor of 5.94, win rate of 87.23%, and maximum drawdown of 10.59%, represent simulated closed-trade historical research. The calculations do not represent real-time brokerage account returns, live trading execution, or guaranteed future performance. Parameters such as execution slippage, commissions, borrow costs, and exact live order filling were not modeled in the dataset. Historical backtests serve as analytical tools to evaluate mathematical strategy characteristics under specific past price distributions and should not be construed as direct investment advice.