BBY · Best Buy Co., Inc.
Stock Universe · globaler AktienmarktBBY · Best Buy Quantitative Strategy Analysis: Evaluating a Low-Frequency, High-Profit-Factor 15-Minute System
An in-depth quantitative examination of the top-ranked algorithmic trading strategy for Best Buy Co., Inc. (BBY) on the 15-minute chart, covering seven years of simulated backtest data from September 2019 to September 2026. The strategy demonstrates an exceptional win rate of 81.73% across 104 completed trades, paired with an impressive Profit Factor of 6.25 and a tight maximum drawdown of 10.40%. With an average trade duration of nearly 10 days and a low reliance on extreme outlier wins—evidenced by the top three winning trades generating only 13.71% of total gross profits—this model offers a structured, multi-day swing-trading framework suitable for automated trading bots, trading agents, and quantitative execution algorithms.
Analyse vom 22.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
1. Strategy profile
The subject of this quantitative evaluation is an algorithmic trading strategy engineered for Best Buy Co., Inc. (BBY stock) on the 15-minute timeframe. Over an extensive seven-year historical simulation window spanning from September 16, 2019, through September 14, 2026, the model logged 104 fully closed trade cycles. DevioLab has assigned this strategy an analytical score of 88.05, placing it as the number one ranked quantitative model for the BBY ticker within the platform's reference database. The core profile of this strategy is characterized by low trade frequency, deliberate multi-day position holding, and strong risk-adjusted returns. Across all completed historical trades, the strategy accumulated a total trade return sum of +5248.42%, reflecting an annualized figure of 233.51%. Designed for deployment within modern automated trading bots and trading agents, the strategy operates as a discrete execution model that converts 15-minute price action signals into disciplined, medium-term position entries and exits without requiring high-frequency manual intervention.
2. Trading rhythm and position duration
Although the underlying entry and exit signals are computed on a 15-minute candlestick chart, the strategy's operational rhythm is distinctly macro-oriented rather than scalping-based. On average, a position remains active for 247.80 hours (approximately 10.3 days), with a median holding duration of 180.38 hours (roughly 7.5 days). This substantial duration confirms that the algorithm uses intraday price data to pinpoint precise swing setups rather than seeking rapid intraday micro-profits. The strategy executes with noticeable restraint, averaging 14.86 trades per year, or approximately 1.53 trades per active month. Furthermore, the average interval between consecutive trade exits is 24.56 days, with a median spacing of 20.04 days. This sparse trade pacing means an automated trading robot following this system spends long stretches holding a single open position or waiting in cash for a high-probability trigger. By avoiding churn and over-trading, the historical pacing naturally isolates the system from excess market noise, though traders must account for multi-day market exposure and overnight gap potential inherent in stock holdings.
3. Quality of historical results
The statistical distribution of trade returns demonstrates consistent efficiency and balanced payoff quality. Out of 104 completed transactions, 85 yielded positive returns while 19 resulted in losses, translating to a high historical win rate of 81.73%. The overall Profit Factor stands at 6.25, indicating that total gross gains exceeded total gross losses by over six times. Across the dataset, the average trade return is +4.26%, compared to a median trade return of +3.24%. The proximity between the average and median figures suggests that strategy gains are steadily generated across standard trade setups rather than being artificially inflated by a single anomalous win. The best individual trade achieved a gain of +26.55%, whereas the single worst trade suffered a loss of -8.82%. Crucially, the top three winning trades accounted for only 13.71% of total gross profit. This low concentration metric highlights that performance reliability is distributed broadly across dozens of successful trades rather than dependent on rare outlier spikes.
4. Risk, drawdown and losing behavior
Risk containment is one of the strategy's most prominent statistical traits. Despite generating significant cumulative trade returns over the seven-year period, the historical maximum drawdown was restricted to 10.40%. This limited peak-to-trough decline indicates that losing trades were promptly contained and rarely clustered into deep equity contractions. The strategy's longest winning streak reached an extraordinary 32 consecutive profitable trades, whereas its longest losing streak was limited to just 3 consecutive losses. The structural asymmetry between the maximum positive trade (+26.55%) and the worst trade (-8.82%) further underscores a disciplined tail-risk boundary. When integrated into an automated trading agent, this low drawdown profile reduces the psychological and capital stress often associated with mechanical trading models, provided market conditions remain consistent with historical parameters.
5. Behavior through time and yearly stability
An examination of the strategy's yearly breakdown confirms consistent positive trade performance across diverse stock market regimes. In 2019, during the initial partial testing year, the model executed 4 trades, achieving a 100% win rate and a combined return of +16.38%. Performance expanded during 2020, with 14 trades generating a +84.14% return sum and a 78.57% win rate. In 2021, 15 trades produced +19.65% with a 73.33% hit rate. The strategy experienced its strongest calendar year in 2022, completing 16 trades with a 93.75% win rate (15 wins, 1 loss) and a cumulative sum of +108.16%. In 2023, the system maintained a perfect 100% win rate across 9 trades, adding +42.58%. In 2024, 16 trades yielded +66.03% with an 81.25% win rate. During 2025, trade frequency remained steady at 17 trades, though win rate moderated to 64.71%, generating +57.59%. Finally, in 2026, 13 trades generated +48.67% with an 84.62% win rate. Notably, the strategy registered positive aggregate returns in every single analyzed year, proving that its return stream is smoothly distributed over time rather than clustered in a single fortunate market cycle.
6. Recent period since 2024-06-01 versus full history
Focusing on the recent evaluation window starting June 1, 2024, provides valuable insight into the algorithm's contemporary efficacy. Out of the 104 historical trades, 37 trades were completed within this recent period, generating a cumulative return sum of +135.96%. Representing over a third of all recorded trades, this recent sample indicates a slight increase in trade execution frequency relative to earlier years. More importantly, the strategy generated substantial absolute returns during this window, confirming that its signal generation rules have remained functional and adaptable in recent stock market environments. The continuous accumulation of positive trade outcomes since mid-2024 offers empirical evidence that the algorithm's statistical edge has not suffered from decay or structural breakdown in contemporary trading conditions.
7. Strengths and limitations
The primary strengths of this strategy lie in its high historical win rate (81.73%), superior Profit Factor (6.25%), and strictly bounded maximum drawdown (10.40%). The low contribution of top winners to overall gross profit (13.71%) demonstrates that performance is broad-based and statistically resilient. Additionally, the moderate transaction frequency (~15 trades per year) minimizes total trade entries, making it highly practical for automated trading bots by mitigating execution friction. Conversely, material limitations exist. Because the model holds positions for an average of 10.3 days, trades are exposed to multi-day corporate news, earnings announcements, and overnight price gaps inherent in stock trading. Furthermore, with only 104 completed trades over seven years, the absolute sample size per calendar year is modest, meaning individual losing trades can temporarily impact annual percentage metrics.
8. DevioLab analytical conclusion
With a top ranking among BBY models and a DevioLab score of 88.05, this quantitative strategy represents a robust multi-day swing framework. The system achieves an advantageous balance between high reward expectancy (+4.26% average trade) and low structural drawdown (10.40%). Its low profit concentration confirms that positive results stem from systematic trade execution rather than isolated luck. For quantitative analysts and developers building automated trading agents or trading robots for stock equities, this strategy offers an exceptionally stable baseline model, combining low turnover demands with high risk-adjusted historical consistency.
9. Data scope and methodology
All analytical conclusions in this article are derived exclusively from historical backtested trade results for the BBY 15-minute strategy covering the period between September 16, 2019, and September 14, 2026. Trade statistics reflect completed, simulated transactions calculated on close prices. Percentage figures represent the arithmetic sum and statistical metrics of individual historical closed trades; they do not represent actual live brokerage account statements, nor do they account for slippage, transaction commissions, borrow fees, or market impact. Past performance in historical quantitative simulations is no guarantee of future trading performance. This research document is provided solely for educational and analytical purposes and does not constitute financial or investment advice.