BEN · Franklin Templeton Inc.
Stock Universe · globaler AktienmarktBEN · BEN Algorithmic Strategy Analysis: Evaluating a High-Win-Rate 15-Minute Swing Trading Model with Extended Holding Horizons
This quantitative analysis examines the historical performance profile of the top-ranked algorithmic trading strategy for BEN on a 15-minute timeframe over a 7.02-year backtesting window from September 2019 to September 2026. Generating a total cumulative simulated return of 3816.94% across 125 completed trades, the strategy exhibits an exceptionally high win rate of 81.60% alongside a peak winning streak of 23 consecutive profitable positions. Intended to serve as the core decision logic for automated trading bots, trading robots, and quantitative trading agents, the strategy operates as a selective swing trading model with an average position holding duration of 204.90 hours (approximately 8.54 days) and an execution frequency of roughly 17.81 trades per year. Despite a modest profit factor of 1.25 resulting from an asymmetric payoff distribution where individual losing trades exceed average winning trades, gross profits remain remarkably well-distributed, with the top three winning trades contributing only 12.81% of total gains. Supported by unbroken yearly profitability across eight calendar years and strong post-June 2024 recent activity, the model represents a disciplined approach to systematic equity trading.
Analyse vom 22.09.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
Strategy profile
The primary objective of this systematic research is to evaluate the historical return and risk characteristics of the core algorithmic strategy for BEN on a 15-minute execution chart. Earning the top rank (Rank 1) for the ticker on DevioLab with a DevioLab Score of 81.86, the model was evaluated across a total backtesting history of 7.02 years, spanning from September 16, 2019, through September 22, 2026. Over this multi-year observation window, the strategy completed 125 closed trades, producing a total accumulated return sum of 3816.94% and an annualized equivalent of 237.08%. The quantitative structure is built upon an exceptionally high trade accuracy, recording 102 winning transactions against 23 losing transactions, which translates to a full-history win rate of 81.60%. Designed to function as the governing logic for automated trading bots, trading robots, and autonomous trading agents, the strategy applies disciplined rules to capture price swings in BEN while filtering out intra-day market noise.
Trading rhythm and position duration
Although the strategy evaluates technical conditions on a fine-grained 15-minute price chart, its execution pacing reflects a deliberate, patient swing trading framework rather than high-frequency or intra-day scalping. Across the entire 7.02-year sample, the strategy generated an average of 17.81 trades per year, corresponding to approximately 1.64 trades per active month. The temporal spacing between position exits averages 20.37 days, with a median interval of 17.05 days, indicating that trades are deployed selectively when specific mathematical criteria are met. Position holding durations further reinforce this macro-oriented behavior: the mean holding time per trade is 204.90 hours (roughly 8.54 calendar days), while the median holding time stands at 163.50 hours (approximately 6.81 days). This structural difference between entry chart granularity and holding duration allows deployment in automated trading agents that seek to capture extended multi-day price swings while relying on granular 15-minute bar updates for precise entry and exit execution.
Quality of historical results
A critical examination of trade returns reveals a distinctive statistical payoff structure characterized by high precision coupled with asymmetrical loss sizing. The overall average trade return across all 125 completed positions is 3.31%, which aligns closely with the median trade return of 3.02%. This close alignment between mean and median figures demonstrates that typical trade outcomes are tightly clustered around 3%, providing a consistent statistical baseline. Furthermore, gross profits are remarkably well-distributed across the dataset: the three best winning trades accounted for just 12.81% of total gross gains. This indicates that the strategy performance is not dependent on a few extreme windfall trades or lucky market spikes. However, a major tension exists between the high win rate (81.60%) and the profit factor of 1.25. The mathematical reason for this profit factor is an inverse win-loss payoff ratio: while the single best trade achieved a gain of 29.59%, the worst trade realized a loss of -16.66%. Because losing trades, on average, absorb a larger percentage than individual standard gains, the strategy relies on its high hit rate to overcome loss magnitudes and maintain net structural profitability.
Risk, drawdown and losing behavior
Risk management metrics demonstrate that historical equity declines have remained tightly controlled relative to cumulative returns. The maximum peak-to-trough historical drawdown observed across the 7.02-year period was 16.66%. This drawdown figure matches the worst single trade loss in the dataset (-16.66%), illustrating that equity retrenchment was driven primarily by isolated stop-loss events rather than extended compounding drawdown cascades. The resilience of the equity curve is further highlighted by the strategy losing streak characteristics: the longest consecutive losing streak recorded across all 125 trades was just 2 trades. Conversely, the strategy achieved an extraordinary winning streak of 23 consecutive profitable trades. The brevity of losing streaks prevents severe capital erosion, enabling trading robots and execution algorithms to preserve balance integrity during adverse market conditions.
Behavior through time and yearly stability
Evaluating performance across individual calendar years demonstrates remarkable temporal consistency across changing market environments. The strategy delivered positive aggregate returns in every single calendar year from 2019 through 2026. Performance opened in late 2019 with 4 trades yielding a cumulative sum of +5.09% (75.00% win rate). The year 2020 represented the highest trade volume and total gain, generating +119.97% across 25 trades with a 96.00% win rate. Subsequent years maintained stable productivity: 2021 generated +59.08% across 20 trades (85.00% win rate), 2022 produced +50.94% across 19 trades (73.68% win rate), 2023 yielded +33.61% across 16 trades (75.00% win rate), and 2024 achieved +42.19% across 16 trades (75.00% win rate). In 2025, the model recorded +33.14% across 10 trades (70.00% win rate), while 2026 recorded +70.06% across 15 trades (86.67% win rate). This unbroken multi-year stability proves that the strategy logic is robust across varied volatility regimes rather than overfitted to a single historical bull run.
Recent period since 2024-06-01 versus full history
Analyzing recent trading history beginning June 1, 2024, provides an essential window into the current vitality of the model. Between June 1, 2024, and September 22, 2026, the strategy executed 33 completed trades, accumulating a combined return sum of +137.77%. Representing over 26% of all lifetime trades within approximately 2.3 years, this elevated activity indicates an active trading rhythm in recent market conditions. The average return per trade during this recent sub-period sits at approximately 4.17%, outperforming the long-term historical mean of 3.31%. This performance comparison confirms that the algorithmic parameters governing entry and exit timing have remained effective in recent equity environments, providing empirical confirmation that the strategy underlying mechanics have maintained statistical edge.
Strengths and limitations
The primary quantitative strength of this algorithmic strategy lies in its outstanding win rate (81.60%), exceptionally short losing streaks (maximum 2 consecutive losses), and broad profit distribution (top 3 winners contributing only 12.81% of gains). Its consistent annual performance across eight separate calendar years confirms structural reliability. Furthermore, the low trade frequency (17.81 trades per year) minimizes operational turnover for automated trading bots. Conversely, the central quantitative limitation is the modest profit factor of 1.25. Because individual losing trades can reach -16.66% while average winning trades cluster around 3.00% to 3.31%, the strategy requires a high hit rate to sustain profitability. Should market conditions shift such that the win rate declines meaningfully below 75%, net return accumulation would compress rapidly due to the asymmetric size of losing trades relative to average winners.
DevioLab analytical conclusion
Based on a rigorous analysis of 125 historical trades over 7.02 years, this strategy ranks as the premier algorithmic logic for BEN on DevioLab, supported by a DevioLab Score of 81.86. The strategy successfully combines a 15-minute execution frame with multi-day swing holding times, yielding a 3816.94% total cumulative backtested return and a maximum drawdown of 16.66%. For quantitative developers and traders deploying automated trading agents or trading robots, the strategy presents a highly stable, high-win-rate framework. However, risk managers must account for the structural trade-off between trade accuracy and payoff asymmetry, maintaining appropriate position sizing to withstand occasional drawdowns while BENefiting from the strategy high historical win rate and temporal consistency.
Data scope and methodology
All analytical conclusions presented in this article are derived exclusively from simulated historical backtest results for BEN on a 15-minute chart, covering the period from September 16, 2019, to September 22, 2026. The dataset encompasses 125 closed trade events evaluated under standardized algorithmic execution assumptions. Percentages reflect closed trade performance figures and do not account for live brokerage execution factors, slippage, commission costs, variable liquidity, or overnight margin fees. Historical performance metrics and backtested returns are purely educational and analytical tools; they do not represent real-time fund trading and constitute no guarantee of future trading performance.