BRO · Brown & Brown Inc.
Stock Universe · globaler AktienmarktBRO Algorithmic Strategy Analysis: Evaluating a High-Hit-Rate Positional Model on 15-Minute Data
This quantitative evaluation inspects a top-tier algorithmic trading model designed for BRO stock. Tested across a 7.03-year backtesting window on 15-minute asset price data, the strategy generated a cumulative historical trade return of +1915.44% across 103 completed trades, achieving an 83.02 DevioLab Score and a rank of 1 for the asset. With a win rate of 79.61%, a profit factor of 8.75, and a maximum peak-to-trough drawdown of 14.69%, the strategy balances multi-week position holding with controlled historical downside. The analysis examines trade distribution, payoff metrics, yearly consistency, and recent performance since June 2024.
Analyse vom 25.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
1. Strategy profile
The quantitative strategy under review, designated as core configuration BRO 0 +1915.44% 1TRAD-FWO0, is an algorithmic model developed for equity market trading in BRO stock. Evaluated over a continuous historical dataset spanning 7.03 years from September 16, 2019, through September 25, 2026, the strategy ranks first for this asset ticker on DevioLab with a composite DevioLab Score of 83.02. Operating on a 15-minute candle bar interval, the underlying logic identifies structural swing opportunities rather than rapid intraday fluctuations. Across the backtest duration, the strategy executed 103 completed trades, resulting in a cumulative closed-trade gain sum of +1915.44% and an annualized performance metric of 64.22%. Designed as decision logic capable of steering automated trading bots, trading robots, or systematic trading agents, the strategy emphasizes high trade selectivity over high trade volume.
2. Trading rhythm and position duration
Although the decision logic operates on a 15-minute price chart, the strategy's operational cadence reflects a multi-week swing or positional approach. Over the 7.03-year testing period, the system closed 103 positions, yielding an average trade frequency of 14.66 trades per year, or approximately 1.66 trades per active month. The time between position exits averages 23.63 days, with a median exit interval of 18.93 days. Position holding times are similarly extended: the average holding duration per trade is 376.89 hours (approximately 15.7 calendar days), while the median holding duration stands at 239.75 hours (nearly 10 calendar days). This duration profile demonstrates that the 15-minute timeframe is utilized for refined entry and exit execution timing rather than rapid flipping. For an automated trading bot or execution agent, this low-frequency, longer-duration rhythm limits operational turnover while requiring endurance through multi-day stock price movements.
3. Quality of historical results
The historical performance profile is characterized by exceptional trade accuracy paired with robust payoff metrics. Out of 103 completed trades, 82 ended in positive return territory, producing a win rate of 79.61%. The gross profit factor reached 8.75, meaning that aggregate gains exceeded aggregate losses by nearly nine to one. Across all closed positions, the average trade return was +3.32%, compared to a median trade return of +2.42%. The strategy's single best trade achieved a gain of +35.90%, whereas the worst individual trade registered a loss of -11.57%. Crucially, profit concentration analysis reveals that the three largest winning trades collectively accounted for 16.44% of total gross profit. This low concentration figure indicates that overall returns were generated by a BROad, consistent base of successful positions rather than being artificially skewed by a few extreme outlier wins.
4. Risk, drawdown and losing behavior
Risk management metrics show notable historical restraint relative to overall return generation. The maximum historical drawdown recorded across the entire 7.03-year history was 14.69%. When measured against the cumulative simulated growth of +1915.44%, this peak-to-trough decline indicates moderate historical downside equity variance. The strategy experienced a maximum consecutive losing streak of 3 trades, compared to a maximum consecutive winning streak of 17 trades. However, an examination of individual position risks reveals an asymmetric payoff structure: the single worst trade (-11.57%) is significantly larger in magnitude than the average winning trade return (+3.32%). This trade structure implies that the strategy relies primarily on its high hit rate (79.61%) and short losing streaks to maintain equity curve stability, rather than relying on large reward-to-risk ratios on individual positions.
5. Behavior through time and yearly stability
A yearly breakdown of trade activity demonstrates persistent positive outputs across every full calendar year in the evaluation period. In 2020, the strategy generated +103.28% across 13 trades with a 92.31% win rate. In 2021, performance logged +49.48% across 10 trades (80.00% win rate). The volume expanded in 2022 to 22 trades, yielding +56.34% with a 72.73% win rate. In 2023, 16 trades produced +40.64% (87.50% win rate), followed by 2024 with 13 trades producing +49.73% (92.31% win rate). In 2025, 18 trades delivered +25.17% (77.78% win rate). During the partial year of 2026, 11 trades accumulated +17.28%, although the win rate moderated to 54.55%. Annual trade frequency remained stable throughout, fluctuating between 10 and 22 trades per full year, demonstrating that return generation was distributed evenly across multi-year market cycles rather than concentrated in a single speculative surge.
6. Recent period since 2024-06-01 versus full history
Evaluating recent activity provides insight into model performance under current market conditions. In the period starting June 1, 2024, through the end of the dataset in September 2026, the strategy recorded 35 completed trades with a cumulative trade return sum of +61.74%. Representing over a third of total historical trades (35 out of 103), this window exhibits an elevated trade completion frequency compared to earlier years. The continued accumulation of positive returns (+61.74%) during this recent interval suggests that the strategy's core entry and exit logic remained functional across recent market regimes. The presence of 35 fully resolved trades ensures that recent statistical observations are based on a viable sample size rather than isolated trade events.
7. Strengths and limitations
The primary analytical strengths of this BRO algorithmic strategy include a high historical win rate (79.61%), an exceptional profit factor (8.75%), a contained maximum drawdown (14.69%), and low profit concentration (16.44% in top 3 winners). Additionally, the model demonstrated steady annual profitability across all recorded years. Conversely, material limitations exist. With only 103 completed trades over more than seven years, the sample size per year is relatively small, which can increase statistical variance in short-term sub-samples. Furthermore, position holding times averaging ~15.7 days expose positions to overnight market gaps and BROader macroeconomic market risks inherent to equity holdings. Finally, the lower win rate observed in 2026 (54.55%) highlights that trade accuracy can fluctuate substantially during shifting market dynamics.
8. DevioLab analytical conclusion
With a top ranking for BRO stock and an overall DevioLab Score of 83.02, this strategy presents a robust quantitative model for systematic stock trading. Its structural design effectively pairs granular 15-minute candle analysis with multi-week position holding times, allowing automated trading bots and trading agents to participate in equity trends without incurring excessive transaction volume. The combination of a high profit factor (8.75) and moderate peak-to-trough drawdown (14.69%) demonstrates strong historical capital efficiency. Quantitative analysts and automated trading developers should evaluate the strategy's reliance on high win rates against its potential downside gap risk when considering implementation within automated trading frameworks.
9. Data scope and methodology
All statistics presented in this report are derived from simulated backtest results generated for asset symbol BRO using 15-minute candlestick price data from September 16, 2019, to September 25, 2026. Trade performance metrics describe historical closed positions calculated on a compounding or aggregate basis as indicated. These statistical outputs represent hypothetical backtested results and do not account for real-world execution friction, BROker commission structures, bid-ask spreads, overnight borrow costs, dividend adjustments, or slippage. Historical backtested performance is not a guarantee or reliable indicator of future live trading results. This analysis is provided strictly for quantitative research and educational purposes and does not constitute financial advice or investment recommendations.