CHTR · Charter Communications Inc.
Stock Universe · mercado global de açõesCHTR Quantitative Model Analysis: High Win-Rate Medium-Term Swing Architecture in CHTR Stock
This quantitative research report evaluates an algorithmic trading model developed for CHTR stock operating on a 15-minute execution interval. Spanning nearly seven years of simulated backtested history from September 2019 through September 2026, the strategy recorded a cumulative profit of 803.92% across 76 completed trades, achieving an overall win rate of 81.58% and a profit factor of 6.25. Driven by a selective trading rhythm of approximately 10.91 trades per year and an average holding duration of roughly 9.74 days, the model balances high hit-rate efficiency with controlled concentration risk. This study provides an in-depth statistical examination of position duration, trade payoff distributions, historical drawdowns, annual consistency, and recent execution performance, designed to inform the development of automated trading bots, trading robots, and quantitative decision agents.
Análise em 02.10.2026. Os dados da estratégia são atualizados separadamente.Ler análise completa
1. Strategy profile
The strategy designated as CHTR 0 +803.92% 1TRAD-TAV3 is a core quantitative model designed specifically for CHTR stock within equity markets. Operating on a 15-minute price bar interval, the logic functions as a high-conviction swing engine intended for deployment within automated trading bots, trading robots, or algorithmic trading agents. Across a backtest sample period of 6.96 years—spanning from September 16, 2019 to September 2, 2026—the model executed 76 completed trades. It achieved a total historical profit of 803.92% and an annualized return metric of 150.20%. With a DevioLab quality score of 84.49, this strategy ranks first among all evaluated algorithms for CHTR on the platform. The architecture relies on selective position taking rather than high-frequency churn, delivering an overall win rate of 81.58% (62 winning trades against 14 losing trades) and an impressive profit factor of 6.25. These core parameters establish the system as a disciplined, high-payoff swing model that prioritizes quality of entry over operational trade volume.
2. Trading rhythm and position duration
Analyzing the model's operational cadence reveals a distinct medium-term swing profile despite utilizing a granular 15-minute bar interval for entry and exit signals. Over the 6.96-year history, the strategy averaged 10.91 completed trades per year, which translates to roughly 1.38 trades per active month. The statistical spacing between position exits averages 31.64 days, with a median spacing of 26.97 days. This cadence confirms that trading activity is sparse and highly filtered. Individual trade holding times further illustrate this patient posture: the average holding duration is 233.72 hours (approximately 9.74 calendar days), while the median holding duration is 168.25 hours (exactly 7.01 calendar days). Position durations extend well beyond intraday horizons, allowing the system to capture multi-day price moves in CHTR while bypassing intraday market noise. For automated trading agents or execution algorithms, this low frequency significantly reduces theoretical operational turnover, though execution efficiency, fees, and slippage are not modeled in this theoretical sample.
3. Quality of historical results
The performance distribution of the strategy demonstrates robust symmetry between typical gains and typical losses, paired with a remarkably high hit rate. The average return across all 76 completed trades stands at +3.32%, while the median trade return is slightly higher at +3.46%. This close alignment between average and median returns indicates that overall cumulative profitability is not driven by an isolated handful of extreme upside outliers, but rather by a stable, repeatable payoff engine. The best individual trade generated a gain of +21.76%, while the worst trade resulted in a loss of -17.68%. Crucially, the top three largest winning trades collectively account for just 16.84% of total gross profit. This low profit concentration metric proves that the cumulative gain of +803.92% relies on a broad base of consistent winners rather than extreme luck. Combined with a 6.25 profit factor—meaning gross gains exceeded gross losses by more than six times—the historical quality of execution shows an exceptional balance between high accuracy and substantial payoff.
4. Risk, drawdown and losing behavior
Risk management characteristics within the strategy demonstrate disciplined loss confinement relative to cumulative equity growth. The maximum peak-to-trough historical drawdown recorded across the nearly seven-year backtest was 18.61%. Considering the total strategy return of 803.92%, an 18.61% drawdown reflects strong drawdown-to-reward ratio dynamics. The model's loss sequence behavior is equally notable: the longest recorded losing streak across 76 trades was just 2 consecutive losses, whereas the longest winning streak reached 14 consecutive trades. Out of 76 completed positions, only 14 closed at a loss. However, risk management analysis must highlight the worst single trade loss of -17.68%. This reveals that while losing trades are rare, individual adverse exits can be significant relative to the average win of +3.32%. For quantitative developers designing risk parameters for automated trading bots, this structural gap between average gain and maximum single-trade loss emphasizes the necessity of strict capital allocation rules to prevent rare adverse tail events from damaging long-term capital compounding.
5. Behavior through time and yearly stability
A detailed examination of the annual performance breakdown demonstrates impressive multi-year consistency across varying equity market regimes. The strategy recorded positive total net gains in every single full calendar year evaluated from 2020 through 2026. In 2020, the system generated +56.98% across 8 trades with a 100% win rate. Performance remained positive in 2021 (+22.16% across 9 trades, 77.78% win rate), 2022 (+23.27% across 16 trades, 75.00% win rate), and 2023 (+22.27% across 12 trades, 83.33% win rate). The year 2024 represented a standout surge in strategy productivity, yielding +85.18% across 12 trades without a single losing trade (100% win rate). In 2025, performance adjusted to +21.91% across 10 trades with a 60.00% win rate, while 2026 recorded +20.88% across 9 trades with a 77.78% win rate. Trade frequency remained remarkably stable, ranging between 8 and 16 completed trades per year, confirming that the underlying signal engine maintains stable behavior across diverse market environments.
6. Recent period since 2024-06-01 versus full history
Analyzing recent historical trade data provides valuable insight into the ongoing relevance of the model's logic. In the period evaluated from June 1, 2024 through September 2, 2026, the strategy executed 26 completed trades, accumulating a combined return of +105.44%. This recent window accounts for approximately 34.2% of all 76 trades completed across the entire 6.96-year dataset, indicating an increase in signal activity during recent market phases. The sum of returns during this recent period (+105.44%) represents a substantial portion of the overall +803.92% lifetime return. Comparing recent performance to full-history averages reveals that the strategy did not suffer from alpha decay or structural degradation; rather, recent execution delivered above-average trade density while maintaining strong profitability. This robust recent track record offers compelling statistical evidence that the model's entry and exit logic remained well-calibrated to CHTR price dynamics in recent trading cycles.
7. Strengths and limitations
The main analytical strengths of this CHTR quantitative model include an exceptional historical win rate of 81.58%, a high profit factor of 6.25, a moderate maximum drawdown of 18.61%, and highly distributed gross profits where the top three winners contribute only 16.84% of total gains. Furthermore, its continuous annual profitability from 2020 through 2026 underscores structural resilience. Conversely, clear limitations must be acknowledged. The absolute trade count of 76 trades over 6.96 years constitutes a relatively modest statistical sample size. The average frequency of ~10.91 trades per year means statistical confidence builds slowly over time. Additionally, the worst trade loss of -17.68% indicates that when losses do occur, they can be significantly larger than the average trade gain of +3.32%. Operators building automated trading robots around this logic must take into account that long holding times (averaging ~9.74 days) expose positions to overnight gap risk and multi-day market volatility.
8. DevioLab analytical conclusion
With a DevioLab score of 84.49 and a rank of #1 for CHTR stock, this algorithmic trading model represents a high-performing quantitative strategy characterized by disciplined swing execution and remarkable historical accuracy. Its combination of an 81.58% win rate, a 6.25 profit factor, and a controlled 18.61% maximum drawdown reflects a carefully balanced risk-reward architecture. The strategy successfully avoids dependence on erratic single-trade outliers, establishing a stable core of gains across multiple market years. For quantitative researchers and developers configuring automated trading bots or algorithmic trading agents, the strategy's primary requirement is patience, given its low turnover of approximately 1.38 trades per active month. Provided that position sizing and stop-loss parameters are properly implemented to manage occasional negative tail trades, the historical data suggests this model serves as a robust foundation for CHTR quantitative trading.
9. Data scope and methodology
The statistical findings presented in this report are derived entirely from backtested, historical simulated trade records for CHTR stock across a 15-minute timeframe between September 16, 2019 and September 2, 2026. All trade statistics, including win rates, holding durations, annual breakdowns, drawdowns, and profit factor calculations, represent closed simulated positions generated by the strategy logic. The start date of September 16, 2019 denotes the beginning of the available historical dataset for this specific model, not the inception, listing, or corporate creation date of CHTR stock. Execution metrics do not account for real-world order routing, variable exchange fees, margin interest, overnight borrowing costs, or price slippage. Historical backtested performance does not guarantee future results, and this analysis is provided strictly for quantitative research and educational purposes rather than personalized financial advice.