TSM · Taiwan Semiconductor Manufacturing Company Ltd. ADR
Stock Universe · global stock marketTSM · Taiwan Semiconductor Manufacturing Company Limited: Quantitative Analysis of an Ultra-Selective 15-Minute Strategy with a 93.1% Win Rate and Concentrated Return Profile
This quantitative evaluation examines an ultra-selective trading model developed for Taiwan Semiconductor Manufacturing Company Limited (TSM) on a 15-minute execution interval. Over a backtested evaluation horizon spanning 6.92 years from September 2019 to August 2026, the strategy executed 29 completed trades, achieving a cumulative return of +4680.60% and an annualized performance metric of 830.16%. Featuring an exceptional win rate of 93.10% across 27 winning positions against only 2 losses, the strategy yields a Profit Factor of 9.38 and earns the top DevioLab rank for this asset with a score of 83.69. However, rigorous statistical scrutiny reveals key structural trade-offs: 54.43% of total gross profit is generated by just three trades, the median trade gain of +8.97% stands at less than half the +17.97% average trade, and zero trades have been recorded in the recent period since June 1, 2024. This analysis explores the tension between extreme historical accuracy, tail-gain dependence, single-loss drawdown mechanics, and sample size constraints.
Analysis as of 22.08.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The quantitative model designated for Taiwan Semiconductor Manufacturing Company Limited (TSM) operates on a 15-minute price bar interval within equity market data. Ranked first among strategies evaluated for this ticker on DevioLab with a score of 83.69, the strategy presents a highly specialized statistical profile. Across a total evaluated history of 6.92 years—beginning September 16, 2019, and running through August 18, 2026—the model logged a cumulative historical simulated profit of +4680.60%, corresponding to an annualized figure of 830.16%. The statistical foundation rests on 29 completed trade events, comprising 27 winning outcomes and 2 losing outcomes. This yields a win rate of 93.10% and an overall Profit Factor of 9.38. The strategy is characterized by an extreme degree of signal selectivity, generating an average of only 4.19 trades per year despite evaluating price action on a 15-minute chart. While the cumulative total return figure appears expansive, the historical evidence base is defined by a low transaction count, requiring analytical focus on trade distribution, tail payoffs, and historical risk parameters rather than raw percentage totals alone.
Trading rhythm and position duration
Analyzing execution pacing reveals a strategy that operates with distinct temporal restraint. Operating on a 15-minute execution timeframe typically invites high transaction frequency, yet this strategy produced just 29 completed trades over nearly seven full calendar years. An annualized trade frequency of 4.19 trades per year indicates that signal triggers are exceptionally rare. Although specific duration statistics such as average holding hours, median holding hours, and days between exits are not recorded in the source dataset, the low annual trade count demonstrates that the strategy remains dormant across vast stretches of market activity. Rather than engaging in frequent intraday turnover, the model acts as an infrequent participant, waiting for specific multi-bar statistical conditions before entering positions. Exits occur at wide intervals, reflecting a structural model designed to filter out the vast majority of short-term noise on the 15-minute chart in favor of highly isolated execution windows.
Quality of historical results
The quality of historical performance displays an intriguing divergence between hit rate efficiency and return distribution. On a pure accuracy basis, the strategy achieves an extraordinary hit rate of 93.10%, winning 27 out of 29 completed trades and limiting losing closed positions to just 2 events. This produces an overall Profit Factor of 9.38, indicating that total gross winning trade value significantly exceeded gross losing trade value. However, examining internal return metrics reveals substantial right-skewness. The average trade performance across all 29 trades stands at +17.97%, whereas the median trade performance is +8.97%. This wide gap demonstrates that baseline expected returns for typical trades are roughly half of the mathematical mean, driven upward by extreme outlier gains. The single best trade reached an extraordinary gain of +198.47%, while the top three winning trades collectively account for 54.43% of total gross profit. Consequently, while 9 out of 10 trades historical closed in profit, more than half of the strategy's total cumulative performance relies on a tight cluster of three exceptional outlier movements.
Risk, drawdown and losing behavior
Risk parameters within the dataset highlight a compact yet meaningful downside profile. The historical maximum drawdown recorded for the strategy is 20.88%, a loss figure that perfectly mirrors the strategy's worst single trade loss of -20.88%. Because the maximum losing streak across all 29 trades was limited to 1 trade, the observed peak-to-trough equity decline was not generated by an extended sequence of compounding losses, but rather by a single severe adverse trade execution. Across the entire 6.92-year sample, the strategy recorded a maximum winning streak of 11 consecutive trades, punctuated by only two isolated losing trades in total. While a single losing trade of -20.88% is larger than the median winning trade of +8.97% by a factor of more than two, the ultra-high win rate of 93.10% allowed the strategy to quickly recover baseline equity levels following drawdown events. Nevertheless, the presence of a -20.88% worst-case loss demonstrates that despite high win precision, the portfolio remains exposed to substantial single-position downside when an entry fails to perform.
Behavior through time and yearly stability
Evaluating the temporal stability of the strategy involves analyzing performance patterns across its 6.92-year operational footprint from September 2019 to August 2026. Granular annual breakdowns are not explicitly detailed in the source data, but the overarching structural cadence is clearly defined by the total trade count relative to history length. Generating 29 completed trades over nearly seven years equates to an average pacing of 4.19 trades per year. This low transaction density implies that statistical performance is non-uniformly distributed over time; individual calendar years likely contained anywhere from a small handfulness of trades to periods of prolonged operational silence. Because low-frequency strategies accumulate statistical evidence slowly, annual returns were likely dominated by the precise timing of whether a given year captured one of the top three outlier trades (+198.47% maximum gain) or encountered one of the two historical losing trades (-20.88% worst loss).
Recent period since 2024-06-01 versus full history
A critical boundary condition in the historical data appears in the recent evaluation window beginning June 1, 2024. In the period elapsed between June 1, 2024, and the end of the historical dataset in August 2026, the strategy recorded exactly 0 completed trades. Consequently, no recent profit or loss summation metric exists for this window. Comparing this recent operational pause to the historical benchmark highlights the inactive nature of ultra-selective models. While the full 6.92-year history yields an annualized return metric of 830.16% based on historical trades, the total absence of completed positions over more than two years of recent data means there is zero recent empirical evidence to confirm or invalidate ongoing performance dynamics. The strategy's baseline parameters have simply not triggered a complete entry and exit sequence during this recent timeframe, leaving historical statistical conclusions reliant entirely on trades executed prior to June 2024.
Strengths and limitations
The primary analytical strength of this strategy lies in its outstanding directional accuracy and efficiency ratio, evidenced by a 93.10% win rate, a 9.38 Profit Factor, and an 11-trade maximum winning streak on TSM equity data. Its top rank among evaluated strategies for this ticker reflects robust historical performance metrics. Conversely, several material limitations temper these statistical achievements. First, the entire performance history is built upon a sample size of just 29 completed trades, which represents a low sample volume for establishing absolute statistical significance. Second, gross profit exhibits heavy concentration risk, with 54.43% of total gross returns generated by only three trades, making overall outcomes highly sensitive to tail events. Third, the median gain of +8.97% is substantially below the worst-case single loss of -20.88%, revealing negative payoff asymmetry on typical trades. Finally, the complete absence of completed trades since June 1, 2024, leaves recent live operational efficacy unverified by historical trade execution.
DevioLab analytical conclusion
The DevioLab score of 83.69 assigned to this strategy reflects a mathematically powerful backtested performance profile, distinguished by high total compounded returns (+4680.60%) and superior trade selection precision (93.10% win rate). However, an advanced quantitative reading requires contextualizing these headline figures against structural limitations. The model operates as an ultra-infrequent trader on a 15-minute timeframe, averaging roughly four trades per year and relying on rare, expansive price moves to generate the majority of its excess returns. The statistical reliance on three primary winning positions for over half of total gross profits, paired with a complete operational lull since mid-2024, highlights that this model is tailored for capturing rare, pronounced market dislocations rather than generating steady, high-frequency daily yields. Market participants examining this historical research should weigh the exceptional hit rate against the constraints of low sample size, potential multi-year inactivity, and individual trade downside risk.
Data scope and methodology
This quantitative evaluation is based strictly on historical simulated backtest results for the ticker TSM (Taiwan Semiconductor Manufacturing Company Limited) traded within stock market equity data using a 15-minute execution chart interval. The dataset covers an extended timeframe from September 16, 2019, through August 18, 2026, totaling 6.92 years of historical observation. All statistical outputs—including the completed trade count of 29, win rate of 93.10%, Profit Factor of 9.38, maximum drawdown of 20.88%, and concentration metrics—represent closed simulated trade calculations. Specific trade duration metrics were unavailable in the source data and are noted accordingly. This analysis serves exclusively as historical strategy research and does not constitute financial advice, live trading execution guarantees, or investment recommendations.