BNY · Bank of New York Mellon Corp.
Stock Universe · globaler AktienmarktBNY 15-Minute Swing Strategy Analysis: High Win-Rate Execution Coupled with Outlier Winner Concentration
This analytical report evaluates a core 15-minute algorithmic trading strategy applied to BNY across a 7-year testing window from September 2019 to September 2026. Generating 212 completed trades with a high win rate of 75.94 percent and a cumulative historical return of +3429.07 percent, the strategy exhibits the classic profile of a trend-following swing model. While maintaining a steady stream of modest winning trades, the distribution of net return is heavily right-skewed, with the top three winning trades accounting for 82.91 percent of total gross profit. This research examines the strategy's operational rhythm, downside drawdown dynamics, yearly consistency, and recent performance since June 2024.
Analyse vom 24.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The strategy under review is a core quantitative model designed for BNY, operating on a 15-minute price candle resolution. Historical simulation spans a multi-year period from September 16, 2019 to September 24, 2026, totaling approximately 7.02 years of recorded market data. Over this horizon, the strategy completed 212 closed trades, achieving 161 winning trades against 51 losing trades. This yields an overall hit rate of 75.94 percent and a total cumulative return of +3429.07 percent. Within the DevioLab quantitative evaluation framework, the strategy achieves a score of 64.21 and holds the rank of number one for the BNY asset dataset. The underlying architecture is structured to function as automated trading logic suitable for deployment via trading bots, trading robots, or algorithmic trading agents. By operating on a 15-minute bar interval, the logic seeks to identify structural price imbalances while maintaining holding durations capable of capturing extended directional trends rather than micro-structure noise.
Trading rhythm and position duration
Despite utilizing a 15-minute chart interval, the strategy operates as a moderate-frequency swing trading model rather than a high-frequency or intraday scalping framework. Across the 7.02-year backtest, the strategy recorded an average of 30.18 completed trades per year, which translates to roughly 2.72 trades per active month. The pacing of position closures shows an average of 12.15 days between exits, with a median interval of 7.03 days between closed trades. Position duration statistics further reinforce this swing trading character. The average holding time for a trade is 254.41 hours, or approximately 10.6 days. The median holding duration stands at 143.13 hours, or roughly 6.0 days. This substantial gap between execution frequency and candle timeframe indicates that while signal filtering occurs on a granular 15-minute level, the strategy allows positions to run over multi-day and multi-week horizons when price action aligns with its internal entry and exit criteria.
Quality of historical results
The overall return profile presents a compelling distinction between win frequency and profit distribution. The strategy achieves a strong win rate of 75.94 percent, supported by a longest winning streak of 18 consecutive trades compared to a maximum losing streak of 5 trades. However, the cumulative profit factor stands at 1.25, indicating that gross historical gains exceed gross historical losses by a modest statistical margin when evaluated across the entire trade set. Further examination reveals significant positive asymmetry in trade returns. The average trade yield across all 212 transactions is +6.70 percent, whereas the median trade yield is +1.13 percent. This divergence is driven by an extraordinary best trade of +1272.07 percent, compared to a worst trade loss of -17.76 percent. Crucially, the top three winning trades contribute 82.91 percent of total gross profit. This statistical shape demonstrates that while the model routinely books small gains, the vast majority of its long-term equity growth stems from capturing rare, extreme upside market expansions.
Risk, drawdown and losing behavior
Historical risk metrics highlight the structural drawdowns experienced by the strategy during adverse market conditions. The maximum closed-equity drawdown reached 34.04 percent over the 7-year backtesting history. Given that the worst single losing trade was capped at -17.76 percent, the overall peak-to-trough decline was driven by a combination of sequential moderate losses and unrealized equity retracements during volatile swing phases. The losing side of the strategy's trade distribution remains bounded in frequency, with only 51 losing trades out of 212 completed positions. The maximum losing streak of 5 consecutive trades reflects reasonable containment of repeated entry errors. Nevertheless, the combination of a 34.04 percent maximum drawdown and a profit factor of 1.25 emphasizes that traders utilizing automated trading bots or trading agents must account for significant path dependency and portfolio variance during periods when market trends fail to materialize.
Behavior through time and yearly stability
An analysis of the annual breakdown illustrates consistent trading activity alongside varying return magnitudes across different market regimes. In 2019, across 10 completed trades, the strategy recorded a 90.00 percent win rate and a sum return of +8.19 percent. Performance accelerated in 2020, with 36 trades yielding an 83.33 percent win rate and a combined return of +51.64 percent. In 2021, 33 trades produced a 78.79 percent win rate and +50.10 percent net sum. The strategy encountered its primary structural headwind in 2022, closing 36 trades with a reduced win rate of 63.89 percent and a negative yearly sum of -23.13 percent, corresponding to a broader challenging climate for trend-following models in equity markets. Profitability recovered in 2023 (+19.02 percent across 38 trades, 73.68 percent win rate) and remained stable through 2024 (+10.76 percent across 20 trades, 75.00 percent win rate) and 2025 (+10.46 percent across 23 trades, 73.91 percent win rate). In 2026, across 16 trades, the strategy generated +1292.58 percent with an 81.25 percent win rate, reflecting the execution of its primary outlier winning trade.
Recent period since 2024-06-01 versus full history
Evaluating performance in the recent sample window from June 1, 2024 to September 24, 2026 provides important context regarding recent strategy behavior. During this post-June 2024 window, the strategy completed 52 trades, generating a cumulative return sum of +1307.88 percent. This trade volume represents roughly 24.5 percent of total lifetime trades, executed over approximately 2.3 years. Comparing recent performance with the full historical baseline highlights that the recent period captures the strategy's primary historical outlier event. While the baseline period from 2023 through 2025 delivered steady yearly returns between +10 percent and +19 percent, the post-June 2024 sample encompasses the exceptional trend capture that elevated both the average trade yield and the total historical net return. This reinforces that recent performance has been exceptionally strong, driven by successful retention of macro trend movements.
Strengths and limitations
The strategy demonstrates clear quantitative strengths, most notably a high baseline win rate of 75.94 percent and a robust win-streak capability of up to 18 consecutive trades. Its signal logic successfully prevents frequent overtrading, maintaining an average exit spacing of 12.15 days and allowing profitable positions to expand significantly over time. Furthermore, single-trade risk is bounded, with the worst historical loss limited to -17.76 percent. Conversely, the primary analytical limitation is the strategy's high concentration of gross profit within a handful of trades. With 82.91 percent of gross profit originating from the top three winning positions, overall strategy efficiency relies heavily on remaining exposed during rare, large-magnitude price moves. Additionally, a profit factor of 1.25 and a historical maximum drawdown of 34.04 percent indicate that non-outlier operational periods require patience, as baseline trade returns typically yield closer to the median value of +1.13 percent.
DevioLab analytical conclusion
The quantitative evaluation of this BNY 15-minute strategy confirms its role as a high-probability swing trading system with asymmetric upside capture. Its top ranking for the asset and DevioLab score of 64.21 stem from a structural ability to maintain high win consistency while preserving upside participation for major market expansions. Automated trading robots or trading agents deploying this logic benefit from a systematic framework that avoids noise-induced overtrading. When evaluating this performance profile, quantitative practitioners must distinguish between high-frequency hit rate and gross return driver distribution. The strategy functions effectively as a steady baseline harvester punctuated by high-impact macro trend captures. Risk management parameters should be configured to withstand multi-month consolidation phases and drawdowns up to the historical 34.04 percent threshold to allow the strategy's trend-following mechanisms to fully operate.
Data scope and methodology
This analysis is derived entirely from historical backtested market statistics for the BNY asset on a 15-minute candle interval across the sample period from September 16, 2019 to September 24, 2026. All trade statistics, including completed trade counts, holding durations, win rates, drawdowns, and profit metrics, represent closed historical simulated transactions. These simulated results do not incorporate real-time order book execution dynamics, variable slippage, brokerage commissions, or liquidity constraints. Historical backtested performance does not guarantee future results, and this research is published solely for quantitative strategy analysis and technical evaluation.