CNP · CenterPoint Energy, Inc.
Stock Universe · global stock marketCNP Quantitative Strategy Analysis: Evaluating High Win-Rate Swing Performance and Structural Stability in CNP Automated Trading Logic
This comprehensive quantitative analysis examines the historical performance of the algorithmic trading strategy CNP 0 +741.73% 1TRAD-VTB7 on 15-minute price data for CNP. Spanning over seven years of backtested history from September 2019 to September 2026, the strategy compiled 129 completed trades with an overall win rate of 83.72 percent, generating an aggregate historical trade sum of +741.73 percent and an annualized rate of 72.98 percent. Designed as a selective swing-trading logic suitable for deployment in automated trading bots and trading agents, the system exhibits a profit factor of 2.50, a maximum historical drawdown of 22.69 percent, and an exceptionally low peak losing streak of just two trades. While historical net returns were heavily boosted by explosive performance in 2020 and 2021, recent operational data since June 2024 confirms ongoing profitability across 42 closed trades (+64.84 percent aggregate return). This article evaluates the strategy's statistical footprint, position duration, profit concentration, and trade distribution to establish a objective profile of its operational characteristics.
Analysis as of 06.10.2026. Strategy data is updated separately.Read full analysis
Strategy profile
The algorithmic trading strategy identified as CNP 0 +741.73% 1TRAD-VTB7 evaluates price action on a 15-minute bar timeframe for the stock symbol CNP. Over a backtested historical sample spanning 7.04 years—from September 16, 2019, through September 28, 2026—the quantitative model recorded 129 completed trades. Out of these historical trade events, 108 closed with positive returns, resulting in an exceptional win rate of 83.72 percent against 21 losing trades. The strategy achieved an aggregate full-history return sum of +741.73 percent, which translates to an annualized return metric of 72.98 percent. On DevioLab.com, the model holds a DevioLab Score of 77.25 and ranks second among evaluated quantitative strategies for this asset ticker. The structural framework is configured as a low-frequency, high-conviction decision logic that operates as an execution model for automated trading bots, trading robots, or autonomous trading agents looking to capture multi-day market swings without over-trading intraday market noise.
Trading rhythm and position duration
Although the system processes market data on a 15-minute chart resolution, its trading frequency is distinctly patient and disciplined rather than intraday or scalping-oriented. Over the 7.04-year evaluation period, the strategy averaged 18.34 completed trades per year, which corresponds to approximately 1.84 completed trades per active month. The temporal spacing between position exits is substantial, showing an average interval of 19.83 days and a median interval of 16.54 days between trade completions. Position holding times further reflect a swing-trading methodology: the average holding duration stands at 175.09 hours (roughly 7.3 calendar days), while the median holding duration is 123.00 hours (approximately 5.1 calendar days). This pronounced divergence between the 15-minute execution interval and multi-day holding periods suggests that the strategy utilizes intraday bar data primarily for precise entry and exit timing, while filtering out high-frequency fluctuations to ride broader price swings over several trading sessions.
Quality of historical results
A detailed inspection of trade distribution reveals strong underlying payoff quality balanced by an asymmetric win-loss profile. The mathematical expected value per trade is favorable, featuring an average gain of +1.99 percent and a median gain of +1.72 percent across all 129 closed positions. The proximity between the mean and median metrics indicates that typical trade outcomes are consistently positive rather than artificially inflated by a few extreme statistical outliers. Nevertheless, the single best historical trade yielded an impressive +40.91 percent, while the worst historical trade registered a loss of -11.70 percent. The strategy's overall profit factor of 2.50 demonstrates robust capital generation relative to gross losses. Crucially, the top three winning trades accounted for 22.58 percent of total gross profits. This moderate concentration factor indicates that while large winning trades make a meaningful contribution to equity growth, the strategy's overall success relies predominantly on its remarkably steady 83.72 percent hit rate across dozens of individual trades.
Risk, drawdown and losing behavior
Risk management metrics reflect a defensive structural architecture paired with occasional periods of equity pullbacks. The maximum historical drawdown recorded across the entire 7.04-year timeline reached 22.69 percent. When evaluated alongside the strategy's profit factor of 2.50 and annualized return of 72.98 percent, a peak drawdown below 23 percent represents a manageable equity curve fluctuation for high-return quantitative stock models. The strategy's risk profile is especially notable for its losing streak characteristics: the longest consecutive losing sequence throughout the 129-trade dataset was capped at just 2 trades. Conversely, the strategy achieved a peak winning streak of 25 consecutive profitable positions. Because losing clusters are exceptionally short, the model historically avoided prolonged equity drawdowns caused by repeated consecutive stop-outs. However, the magnitude of the worst individual trade (-11.70 percent) relative to the average trade (+1.99 percent) demonstrates that single losing events can exert a larger individual impact than standard winning trades, requiring a strong win rate to preserve long-term capital trajectory.
Behavior through time and yearly stability
Examining the strategy's yearly performance breakdown highlights significant evolutionary shifts across different market cycles. In its initial partial year of 2019, the system recorded 5 trades with a 60.00 percent win rate and a slight net loss sum of -1.82 percent. The strategy experienced its most explosive growth in 2020 and 2021: in 2020, 19 trades produced a 84.21 percent win rate and a +96.18 percent return sum, followed by 2021 where 24 trades yielded a perfect 100.00 percent win rate (24 wins, 0 losses) and a +75.03 percent return sum. The market environment in 2022 and 2023 presented more challenging conditions; trade volume remained steady at 17 trades per year, but win rates shifted to 76.47 percent in 2022 (+12.93 percent return sum) and 58.82 percent in 2023 (+4.82 percent return sum). Performance rebounded sharply in 2024, delivering 20 trades with a 95.00 percent win rate and +38.31 percent return sum. Performance remained solid through 2025 (16 trades, 81.25 percent win rate, +22.05 percent return sum) and into partial 2026 (11 trades, 90.91 percent win rate, +9.58 percent return sum). This temporal distribution demonstrates that while gross performance varies across changing volatility regimes, the trading logic maintained positive annual return sums in six out of seven full calendar years.
Recent period since 2024-06-01 versus full history
Evaluating recent execution data since June 1, 2024, provides vital context regarding the modern relevance and continuity of the quantitative strategy. Over this recent sample period, the trading logic generated 42 completed trades, representing nearly one-third (32.56 percent) of all historical trades recorded since 2019. The sum of trade returns in this recent window reached +64.84 percent, indicating strong contemporary profitability. The higher trade density in recent months—42 completed exits over approximately 2.3 years compared to the 7-year baseline—reflects an active period for the algorithm without compromising its underlying stability. Comparing recent outputs with full-history performance reveals that the model has maintained its core statistical advantages in contemporary equity markets, offering empirical evidence that the decision rules implemented in automated trading bots continue to capture persistent price structures in CNP.
Strengths and limitations
The quantitative profile of CNP 0 +741.73% 1TRAD-VTB7 reveals several distinct structural strengths along with specific operational constraints. Key statistical strengths include an exceptionally high historical win rate of 83.72 percent, a robust profit factor of 2.50, and a remarkably low maximum losing streak of 2 trades, which collectively limit psychological and financial strain during drawdowns. Additionally, the strategy's top three winners account for only 22.58 percent of gross profit, confirming that gains are well-distributed across the trade population rather than reliant on isolated luck. On the limitation side, the strategy exhibits an asymmetric trade risk profile, where the worst single trade (-11.70 percent) is substantially larger than the average trade gain (+1.99 percent). Furthermore, the model experiences periodic regimes of muted performance, as seen in 2022 and 2023 when annual return sums dipped to single digits. Finally, because total trade volume over seven years is modest at 129 trades, statistical inference relies on a select sample size per calendar year.
DevioLab analytical conclusion
With a DevioLab Score of 77.25 and a second-place ranking for the CNP symbol, this algorithmic strategy demonstrates a highly effective balance between trade selectivity and statistical edge. The combination of an 83.72 percent win rate, a 2.50 profit factor, and an annualized historical return sum of 72.98 percent places the model among the top-tier quantitative setups analyzed on 15-minute chart resolution for stock assets. Its multi-day holding period (median 123 hours) effectively bridges intraday precision with swing-trading target capture, making it a viable decision logic for automated trading robots and automated trading agents seeking measured market exposure. While potential users must account for historical drawdown periods of up to 22.69 percent and remain aware that single losing trades can exceed average gains, the strategy's historical consistency and strong recent performance (+64.84 percent across 42 trades since mid-2024) substantiate its position as a robust quantitative strategy.
Data scope and methodology
The figures, metrics, and temporal distributions presented in this analytical report are derived directly from backtested statistical model outputs for the asset CNP on a 15-minute candle interval covering September 16, 2019, to September 28, 2026. All trade statistics, including percentage gains, holding hours, drawdown percentages, and streak metrics, represent closed historical simulated transactions generated by the strategy logic. These metrics do not reflect live trading execution, order book slippage, brokerage commissions, account margin costs, or liquidity constraints. The start date of the dataset marks the commencement of backtest recording rather than the inception or listing date of the underlying stock. Historical backtest performance does not guarantee future financial results, and this research report is published strictly for quantitative analysis and educational purposes rather than personalized investment advice.