CCI · Crown Castle Inc.
Stock Universe · marché actions mondialQuantitative Analysis of CCI Algorithmic Strategy: High Win Rate and Multi-Day Holding Structure in Stock Trading
This statistical analysis examines the performance profile of an algorithmic trading strategy evaluated on the stock asset CCI across a 6.97-year evaluation timeline from September 2019 to September 2026. Built on a 15-minute execution timeframe, the strategy demonstrates a selective, swing-oriented approach that generated a cumulative return of +580.22% across 103 completed trades. With a high win rate of 77.67%, a profit factor of 2.50, and an average position holding duration of 277.35 hours, the system offers an intriguing case study in low-frequency, multi-day systematic trade management. This study examines its trade distribution, risk characteristics, multi-year stability, and operational parameters for automated execution systems.
Analyse au 28.09.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
1. Strategy profile
The quantitative strategy evaluated under the identifier CCI 0 +580.22% 1TRAD-HVB5 operates as an automated algorithmic model designed for the equity market asset CCI. Operating on a 15-minute timeframe, the strategy utilizes lower-timeframe pricing data to identify entry and exit signals, while maintaining position durations that extend far beyond traditional intraday horizons. Across a backtest spanning nearly seven years from September 16, 2019, through September 3, 2026, the strategy recorded 103 completed trades. It achieved a DevioLab score of 78.09, placing it second overall among tested strategies for this ticker. The overall historical performance yields a cumulative net profit of +580.22%, corresponding to an annualized gain metric of 133.99%. A core structural attribute of the strategy is its exceptional trade selection precision: 80 out of 103 historical trades ended in positive territory, producing a overall win rate of 77.67%. The strategy achieves a profit factor of 2.50, meaning gross historical profits exceeded gross historical losses by two and a half times. Designed as algorithmic decision logic suitable for integration into automated trading bots, trading robots, or systematic trading agents, the strategy automates timing decisions to eliminate subjective discretion while exploiting multi-day price momentum in CCI.
2. Trading rhythm and position duration
Although the underlying market analysis takes place on 15-minute candle intervals, the strategy's operational pacing is surprisingly patient and selective. Over the 6.97 years evaluated, the model executed an average of 14.78 trades per year, which translates to approximately 1.49 completed trades per active month. This sparse cadence demonstrates that the algorithm does not engage in frequent intraday churn or high-frequency turnover. Instead, it filters out noise on the 15-minute chart to isolate higher-conviction multi-day positioning opportunities. Position duration statistics reinforce this longer-term holding behavior. The average holding period per trade stands at 277.35 hours (roughly 11.55 calendar days), while the median holding duration is 192.75 hours (approximately 8.03 calendar days). The gap between the median and average holding times indicates that while most trades resolve within a week to ten days, a subset of positions remains open significantly longer to capture extended directional moves. Furthermore, the average interval between position exits is 24.63 days (with a median of 20.91 days). For operators deploying automated trading agents or trading robots, this structural cadence results in low turnover requirements, minimizing trade frequency while emphasizing swing holding periods.
3. Quality of historical results
Analyzing profit distribution across completed trades reveals a healthy statistical structure characterized by consistent positive expectancy across individual trades. The average trade gain across all 103 historical executions is +2.21%, while the median trade yield is higher at +2.65%. The fact that the median trade return exceeds the mean return is a favorable sign; it indicates that individual strategy returns are not skewed by a tiny minority of statistical anomalies, but rather reflect a consistent distribution of moderate gainers. A particularly notable metric is the concentration of gross profits. The top three winning trades combined account for only 12.73% of total gross historical gains. In quantitative strategy evaluation, a low top-three profit concentration demonstrates that performance is broad-based across dozens of successful trades rather than reliant on two or three lucky tail-risk events. The best individual trade generated a gain of +15.54%, while the worst single trade incurred a loss of -21.58%. Because the worst loss exceeds the magnitude of the best gain, the strategy's overall profit factor of 2.50 is driven primarily by its high hit rate of 77.67% rather than an asymmetric payoff ratio on individual trades.
4. Risk, drawdown and losing behavior
Risk management metrics reveal both historical resilience and specific structural vulnerabilities. The strategy experienced a maximum drawdown of 24.51% over the nearly seven-year backtest window. When placed in context with the total net strategy return of +580.22%, a maximum peak-to-trough decline of sub-25% indicates a manageable drawdown profile for an equity strategy operating with full-position exposure. The algorithm's loss sequence characteristics further highlight its high-win-rate profile. The longest consecutive winning streak reached 9 trades, whereas the longest consecutive losing streak was capped at 3 trades. Across 103 historical trades, the model suffered only 23 total losses. However, the severity of individual losses represents the primary risk factor. The single worst trade recorded a decline of -21.58%, which closely approaches the maximum portfolio drawdown of 24.51%. This reveals that historical equity drawdowns were primarily caused by sharp single-trade adverse moves rather than long, grinding series of consecutive losing trades. Systems utilizing automated trading bots executing this strategy must account for this drawdown topology, recognizing that risk stems from occasional tail-loss events rather than frequent minor losses.
5. Behavior through time and yearly stability
An examination of year-by-year performance demonstrates how the strategy navigated varying market regimes between late 2019 and late 2026. The initial incomplete year of 2019 saw 4 trades, all 4 of which were profitable (100% win rate), delivering a combined return of +8.34%. In 2020, the strategy recorded its highest volume of activity with 19 trades, achieving a 84.21% win rate (16 wins, 3 losses) and a sum return of +78.20%. Subsequent years reflected shifts in trade frequency and net yields. In 2021, 14 trades produced an 78.57% win rate and +21.23% net return. In 2022, during a tougher equity market environment, 15 trades resulted in 10 wins and 5 losses (66.67% win rate) with a +16.85% return. Performance slowed noticeably in 2023 and 2024, generating +5.68% (13 trades, 69.23% win rate) and +5.72% (10 trades, 60.00% win rate), respectively. However, the strategy re-accelerated sharply in 2025 and 2026. In 2025, 15 trades yielded 13 wins (86.67% win rate) and +39.60% cumulative yield, while 2026 added 13 trades with 11 wins (84.62% win rate) and +51.90% sum profit. This multi-year record shows persistent profitability across all eight calendar years, with variance in annual magnitude.
6. Recent period since 2024-06-01 versus full history
Evaluating recent execution statistics from June 1, 2024, through the end of the sample in September 2026 provides critical insight into current strategy efficacy. During this recent window of roughly 27 months, the strategy completed 34 trades, generating a cumulative return sum of +96.21%. This represents roughly one-third of all historical trade exits (34 out of 103) occurring in the final 32% of the testing timeframe. Comparing recent performance against full-history averages shows a marked increase in trading frequency and return velocity. While the overall multi-year average trade frequency was 14.78 trades per year, the post-June 2024 period maintained an annualized pace exceeding 15 trades per year with accelerated aggregate gains. The average gain per trade in the recent period stands near +2.83%, outperforming the full-history mean of +2.21%. This strong recent sample confirms that the model's structural edge has remained active and robust in recent market conditions, showing no signs of decay or performance stagnation.
7. Strengths and limitations
The primary statistical strength of this strategy lies in its high win rate of 77.67% combined with a broad distribution of profits. Because the top three winners represent only 12.73% of gross profits, performance is not dependent on sporadic outliers. Furthermore, its modest trading frequency (14.78 trades/year) and multi-day holding periods (median 192.75 hours) make it structurally efficient to execute through automated trading agents or trading robots without incurring excessive turnover. Conversely, the principal limitation of the strategy is its asymmetric downside tail risk. The worst historical trade loss (-21.58%) was significantly larger than the best historical trade gain (+15.54%). While losing trades are rare (only 23 out of 103), an individual losing trade can offset multiple average winning trades (+2.21% mean gain). Additionally, because holding times routinely extend beyond a week, market participants must tolerate multi-day exposure to overnight gaps and macroeconomic news events inherent in stock trading.
8. DevioLab analytical conclusion
The quantitative evaluation of CCI 0 +580.22% 1TRAD-HVB5 establishes it as a highly disciplined, swing-oriented algorithmic strategy for CCI stock trading. Achieving a DevioLab score of 78.09 and ranking second among strategies evaluated for this asset, the system strikes an effective balance between selective entry timing and multi-day trend capture. Its historical profit factor of 2.50 and overall net gain of +580.22% are grounded in high hit-rate reliability rather than extreme trade leverage or outlier reliance. For practical implementation via systematic trading bots or execution algorithms, the strategy provides clear structural parameters: infrequent exits (averaging 24.63 days apart), long holding durations (averaging 277.35 hours), and short losing streaks (maximum 3 trades). Quantitative risk managers should focus primarily on mitigating adverse tail events, as individual trade drawdowns present a greater historical risk than prolonged strategy stagnation. Overall, the statistical profile demonstrates sustained robustness across multiple market cycles.
9. Data scope and methodology
This analysis is based strictly on historical backtested transaction records for the asset CCI on a 15-minute chart interval covering the period from September 16, 2019, through September 3, 2026. All statistics, including the 103 completed trades, 77.67% win rate, 24.51% maximum drawdown, and yearly breakdowns, are derived exclusively from closed position data generated within this historical simulation environment. Percentage gains and drawdown metrics reflect simulated strategy calculations and do not account for real-world execution factors such as order slip, variable broker commission structures, overnight interest/borrow fees, or liquidity constraints. Historical performance metrics serve solely for quantitative model assessment and do not constitute financial advice or guarantee future trading results.