BAC · Bank of America Corp.
Stock Universe · marché actions mondialBAC Quantitative Strategy Analysis: 82.61% Win Rate and Position-Trading Dynamics on 15-Minute Data
This analytical report evaluates a core algorithmic trading strategy designed for BAC (Bank of America) stock, operating on a 15-minute execution interval across a 7.02-year evaluation dataset from September 2019 to September 2026. Generating a cumulative closed-trade gain sum of +2311.19% across 46 total transactions, the strategy achieves a 82.61% win rate and a profit factor of 3.75. Despite analyzing 15-minute bar data, the model behaves as a patient position-trading framework, recording an average holding duration of 933.02 hours (approximately 38.9 days) and averaging just 6.56 trades per year. The strategy exhibits remarkably low profit concentration, with its top three winning trades contributing 24.96% of gross profit. With a maximum drawdown of 21.07%, a DevioLab Score of 73.16, and an absolute ranking of #2 among evaluated BAC strategies, this architecture provides a low-frequency, high-expectancy quantitative model suitable for deployment within automated trading bots and algorithmic trading systems.
Analyse au 22.09.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
Strategy profile
The algorithmic trading strategy designated for BAC (Bank of America) operates as a high-expectancy, trend-following position model engineered on a 15-minute price series. Over a historical testing horizon spanning 7.02 years—from September 16, 2019 through September 22, 2026—the quantitative model recorded 46 completed trade cycles. Within this dataset, the strategy achieved 38 winning trades against 8 losing trades, yielding an overall win rate of 82.61%. Cumulative returns across all completed transactions totaled +2311.19%, reflecting an annualized figure of 159.97% under compounding assumptions. The model earns a DevioLab Score of 73.16, placing it in the #2 position among all BACktested strategies for BAC and earning a core strategy classification. The system is designed to provide actionable decision rules for execution via automated trading bots, trading robots, and quantitative trading agents seeking structured exposure to equities.
Trading rhythm and position duration
A defining characteristic of this strategy is the structural contrast between its lower-timeframe execution interface (15-minute intraday bars) and its extended position holding times. Over 7.02 years, the strategy executed only 46 closed trades, translating to an average trade frequency of 6.56 trades per year, or 1.28 trades per active month. The mean duration per trade stands at 933.02 hours (roughly 38.88 days), while the median holding duration is 501.38 hours (approximately 20.89 days). Furthermore, the strategy registers an average time between exits of 53.16 days and a median exit spacing of 34.96 days. These metrics confirm that the 15-minute candle resolution is used strictly for granular entry and exit timing rather than high-frequency intraday churning. Automated trading agents utilizing this logic will experience extended periods of market exposure interspersed with long quiescent phases between execution triggers.
Quality of historical results
The overall return profile demonstrates balanced payout mechanics supported by a high win rate and a robust profit factor of 3.75. The average return per completed trade reached 7.86%, while the median trade return measured 5.43%. The largest single winning trade achieved a gain of +44.46%, whereas the single worst trade experienced a loss of -17.32%. Crucially, the top three winning trades accounted for 24.96% of total gross profit. This relatively low winner concentration indicates that historical performance was not generated by a single fortunate market anomaly, but rather accumulated across a consistent series of profitable position cycles. The positive spread between the average win and average loss, combined with the high hit rate, underpins the strategy's high expectancy.
Risk, drawdown and losing behavior
Risk metrics reflect effective containment of equity curve decline relative to total cumulative performance. The maximum recorded peak-to-trough drawdown over the 7.02-year history was 21.07%. During adverse market phases, the strategy suffered a maximum consecutive losing streak of 3 trades, compared to a peak winning streak of 15 consecutive positive outcomes. The maximum single trade drawdown of -17.32% demonstrates that individual position stops or exits allow for multi-week price swings before cutting exposure. Because position holding periods average nearly 39 days, drawdowns evolve gradually over multi-week windows rather than during sharp intraday crashes, requiring capital discipline from operators deploying trading robots based on this logic.
Behavior through time and yearly stability
An examination of yearly performance data reveals strong consistency across contrasting market regimes. In 2020, the strategy generated +89.42% across 6 trades with a 100% win rate. In 2021, performance remained exceptional with 7 trades, a 100% win rate, and +93.40% cumulative gain. In 2022, during a challenging macro environment for financial stocks, the strategy maintained profitability, delivering +39.57% over 9 trades (77.78% win rate). The single negative calendar year occurred in 2023, where the model recorded a slight loss of -3.03% across 4 trades (25.00% win rate), accounting for 3 of the 8 total historical losses. Performance rebounded strongly in 2024 (+47.51% across 4 trades, 75% win rate), 2025 (+60.32% across 8 trades, 87.50% win rate), and into 2026 (+34.57% across 8 trades, 87.50% win rate). The algorithm generated positive closed returns in 6 out of 7 full or partial calendar years.
Recent period since 2024-06-01 versus full history
Performance in the recent evaluation window beginning June 1, 2024, provides vital evidence regarding the ongoing validity of the strategy's logic. Between June 2024 and September 2026, the strategy completed 19 trades, accumulating a combined return sum of +130.22%. Notably, these 19 recent trades represent 41.3% of all completed transactions in the 7.02-year dataset. The elevated trade frequency during this recent window demonstrates that market conditions for BAC provided multiple valid signal triggers. The strategy's ability to generate +130.22% in recent history confirms that its quantitative edges have remained robust in recent market environments rather than decaying post-development.
Strengths and limitations
The primary analytical strengths of this BAC model include its exceptional 82.61% historical win rate, a strong profit factor of 3.75, low winner concentration (top 3 trades = 24.96%), and impressive persistence highlighted by a 15-trade win streak. Furthermore, recent data since mid-2024 shows strong operational continuity. Conversely, primary limitations center on sample size constraint: 46 total completed trades over seven years represents a relatively low sample count, increasing statistical sensitivity to individual trade outcomes. Additionally, average holding durations of nearly 39 days require automated trading agents to withstand extended holding periods and overnight equity volatility without premature manual intervention.
DevioLab analytical conclusion
Scoring 73.16 and ranking #2 overall for BAC, this quantitative trading strategy presents a robust framework for medium-term stock position trading. The strategy effectively blends high selectivity with patient trend-riding rules, avoiding over-trading while capturing substantial multi-week moves in Bank of America equity. While historical drawdowns of 21.07% and multi-week hold times require appropriate capital allocation, the statistical profile—BACked by 6 profitable calendar years out of 7—makes this system a compelling logic module for deployment within modern automated trading bots and algorithmic execution systems.
Data scope and methodology
This analysis is based strictly on simulated historical closed-trade data for BAC on a 15-minute price bar dataset spanning from September 16, 2019 to September 22, 2026. All statistics, including transaction counts, returns, drawdowns, and holding durations, represent BACktested execution outcomes. Recent window metrics cover completed trades with sell timestamps on or after June 1, 2024. These BACktested results do not account for real-world execution friction, slippage, order routing delays, or dividend adjustments, and historical performance provides no guarantee of future returns.