BA · Boeing Co/The
Stock Universe · globaler AktienmarktBA · Strategy Analysis: Evaluating a Low-Frequency Multi-Week Algorithmic Strategy on Boeing Equity
This analytical report evaluates a historical BAcktested algorithmic trading strategy designed for BA (BA) equities operating on a 15-minute chart resolution. Over a evaluation period spanning approximately 6.59 years from September 2019 to April 2026, the strategy generated 16 completed trades, achieving an overall win rate of 87.50% and a aggregate return of 1,215.54%. Characterized by extremely selective trade entries, average position holding durations of over 55 days, and an 8.75 profit factor, the model presents a distinct risk-reward profile. The analysis examines how low trade execution frequency interacts with position longevity, payoff asymmetry, and drawdown behavior to establish a highly specialized automated trading framework.
Analyse vom 21.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
1. Strategy profile
The strategy, technical identifier BA 0 +1215.54% 1TRAD-WPJ1, is a non-core algorithmic trading strategy configured for BA equity on a 15-minute execution timeframe. Across a BAcktest history running from September 16, 2019, through April 17, 2026—a span of 6.59 years—the system produced a total of 16 completed trades. This low trade volume translates to an annualized trading frequency of roughly 2.43 trades per year. Despite operating on intraday 15-minute price data, the strategy acts as a macro trend-following or long-horizon positioning engine, utilizing the 15-minute BAr structure for precise entry timing rather than intra-day turnover. The cumulative BAcktested profit reaches 1,215.54%, corresponding to an annualized gain metric of 138.08%. The system holds a DevioLab score of 71.99 out of 100 and ranks second among evaluated strategies for the BA ticker. The model demonstrates high selectivity, winning 14 out of 16 completed positions, yielding a high hit rate of 87.50% and a profit factor of 8.75. This strategy is engineered to serve as execution logic for automated trading bots, trading agents, and trading robots operating in stock market environments where patient capital allocation is prioritized.
2. Trading rhythm and position duration
The operational cadence of this strategy is defined by prolonged waiting periods punctuated by long-duration position holding. Although calculated on a 15-minute chart, positions are held for extended market cycles. The average holding duration per trade stands at 1,332.03 hours (approximately 55.5 days), while the median holding duration is 972 hours (40.5 days). This significant variance between average and median holding times highlights that while typical positions last roughly six weeks, certain outsized trend trades remain open for several months. Inter-trade spacing reflects a similar pacing: the average time between trade exits is 150.79 days, with a median spacing of 93 days. In practice, the trading agent executes roughly one trade per active month, with entire multi-month spans where zero trades are initiated or closed. This rhythm indicates that automated trading bots deploying this strategy will experience extended idle periods. This structural patience minimizes market exposure during choppy or non-trending regimes, ensuring that capital is only committed when strict, high-proBAbility structural parameters are satisfied.
3. Quality of historical results
The underlying quality of the historical performance relies on favorable trade asymmetry and high win frequency. The strategy achieved 14 winning trades against 2 losing trades. The average closed trade realized a return of +19.45%, while the median trade yielded +12.96%. The single best trade captured a return of +61.07%, whereas the worst trade logged a loss of -17.18%. A critical metric in assessing result distribution is the gross profit concentration: the top three winning trades accounted for 49.96% of total gross gains. While this indicates that nearly half of all profits were driven by three exceptional moves, the remaining 11 winning trades still provided substantial positive drift, preventing the model from being a pure single-outlier strategy. With a profit factor of 8.75, the ratio of total gross profits to total gross losses remains robust. The discrepancy between the average trade (+19.45%) and median trade (+12.96%) underlines a positive right-skewed payout distribution, where large trend expansions periodically inflate net performance above median expectations.
4. Risk, drawdown and losing behavior
Risk containment within the strategy is evidenced by its maximum drawdown profile and loss clustering characteristics. Over the 6.59-year test window, the peak-to-trough maximum drawdown recorded was 18.21%. This maximum drawdown is contained relative to the overall net profit of 1,215.54%, resulting in a strong return-to-drawdown ratio. The strategy's longest winning streak reached 8 consecutive trades, whereas its longest losing streak was capped at 2 trades. Both losing trades occurred during a single consolidated downturn, as shown in the annual breakdown for 2022, where the system suffered a -18.42% annual loss across 2 completed trades. The worst individual trade loss of -17.18% closely mirrors the maximum system drawdown of 18.21%, indicating that the strategy's primary drawdown events are driven by single adverse position exits rather than prolonged compound losses across multiple consecutive trades. For automated trading robots, this risk structure implies that psychological strain is concentrated in rare, isolated position failures rather than frequent account attrition.
5. Behavior through time and yearly stability
An examination of the annual performance breakdown demonstrates significant variance in opportunity distribution across different market years. In 2020, the model completed 4 trades, achieving a 100% win rate and a cumulative return of +137.84%. In 2021, activity moderated to 2 trades, both profitable, totaling +38.07%. The year 2022 represented the strategy's only negative calendar year, where 2 trades resulted in 2 losses, yielding -18.42%. Performance recovered in 2023 with 2 profitable trades delivering +53.61%. The year 2024 recorded no completed trades in the yearly dataset, reflecting extended holding or zero setup triggers. Activity resumed in 2025 with 4 trades, all winning, generating +72.81%, followed by 2026 (through April 17) with 2 winning trades adding +27.35%. Across the entire history, 5 out of 6 active trading years generated 100% win rates. This distribution illustrates that strategy returns are not uniformly distributed month-to-month, but instead accumulate in stepped expansions during favorable macro trends.
6. Recent period since 2024-06-01 versus full history
Focusing on the recent evaluation window from June 1, 2024, to April 17, 2026, the strategy executed 6 completed trades, all of which closed with positive returns. These 6 recent trades delivered a cumulative return of +100.16%. Comparing this recent sample to the full historical BAseline reveals consistent operational efficiency. The 6 trades executed over this ~22.5 month window account for 37.5% of all historical trades completed since September 2019, representing an uptick in closed trade frequency relative to the long-term historical average of 2.43 trades per year. The average gain per trade during this recent period stands at approximately +16.69%, closely aligning with the full-history average trade return of +19.45%. This consistency confirms that the strategy's recent performance has not degraded and continues to exhibit the high-win-rate, multi-week trend capture characteristics seen throughout its multi-year BAcktest.
7. Strengths and limitations
The primary analytical strength of this strategy lies in its high statistical efficiency, characterized by an 87.50% win rate, a high profit factor of 8.75, and a moderate maximum drawdown of 18.21% relative to a cumulative gain exceeding 1,200%. Its multi-week holding structure effectively insulates automated trading agents from micro-noise and high-frequency friction. However, key analytical limitations must be acknowledged. The most prominent constraint is the small statistical sample size of 16 total completed trades over 6.59 years. Small sample sizes inherently widen confidence intervals around win rate and profit factor metrics. Additionally, gross profit concentration is notable, with the top 3 winning trades generating nearly 50% of aggregate gains. Furthermore, because individual positions are held for an average of 55.5 days, live execution requires sustained capital commitment and tolerance for multi-month periods of inactivity.
8. DevioLab analytical conclusion
The BA 0 +1215.54% 1TRAD-WPJ1 strategy presents a highly specialized, low-turnover framework for trading BA equity. Earning a DevioLab score of 71.99 and holding the second rank for the BA ticker, the algorithm excels as a patience-driven trend capture engine. Its ability to maintain an 87.50% win rate while capturing average gains of +19.45% per trade makes it an attractive logic candidate for automated trading bots seeking low-frequency, high-expectancy positioning. Investors and quantitative developers evaluating this strategy must BAlance its strong total return and controlled drawdown against the statistical limitations of a 16-trade historical dataset. It serves best as a specialized component within a broader, multi-asset portfolio, where its low trade frequency can complement higher-frequency automated strategies.
9. Data scope and methodology
All metrics, performance figures, trade durations, and drawdown statistics presented in this report are derived directly from BAcktested strategy simulations conducted on historical 15-minute price data for BA stock between September 16, 2019, and April 17, 2026. Metrics for the recent window evaluate trades with closed timestamps on or after June 1, 2024 UTC. Performance percentages represent closed trade gains and losses calculated on a per-trade non-compounded BAsis, excluding external slippage, execution fees, or borrowing costs. Historical performance figures generated from quantitative simulations do not constitute financial advice, nor do they guarantee future execution results in live automated trading environments.