CSGP · CoStar Group, Inc.
Stock Universe · global stock marketAlgorithmic Strategy Analysis: CSGP · CSGP 15-Minute Multi-Day Position Model
This analytical report examines the historical performance, structural risk profile, and trade distribution of the primary core algorithmic trading strategy for CSGP on the 15-minute chart. Spanning over 7 years of backtested execution history across 73 closed trades, the strategy demonstrates an extraordinary profit factor of 7.50 alongside an 82.19% win rate and a maximum drawdown capped at 17.44%. Rather than engaging in hyper-frequent scalping, the model operates with low frequency and extended position durations, averaging approximately 358 hours per trade. A key highlight of the dataset is its exceptional profit distribution, where the top three winning trades account for only 15.15% of gross profits, confirming that outsized returns are driven by consistent structural edge rather than single lucky tail events. Designed for deployment within automated trading bots and execution agents, this model represents the rank-1 strategy for CSGP in DevioLab strategy repository with an overall score of 80.04.
Analysis as of 12.10.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The evaluated algorithmic model, designated as CSGP 0 +2434.61% 1TRAD-CTN1, represents the top-ranked quantitative trading strategy for CSGP within the DevioLab framework, earning a score of 80.04 and holding the number one rank for this asset. Operating on a 15-minute timeframe, the strategy evaluates high-frequency pricing data to identify rare structural entry conditions, resulting in a selective multi-year history comprising 73 completed trades across a span of 7.07 years from September 2019 through October 2026. Over this total period, the cumulative sum of closed trade returns reached 2434.61%, reflecting an annualized figure of 120.93%. The strategy is engineered specifically to serve as the decision engine for automated trading bots and algorithmic trading agents, translating intraday 15-minute candlestick patterns into distinct multi-day swing positions. A high win rate of 82.19% combined with a profit factor of 7.50 highlights a strategy architecture that emphasizes high entry precision rather than high trade volume.
2. Trading rhythm and position duration
Despite relying on a 15-minute execution timeframe for market data filtering, the strategy exhibits the holding characteristics of a medium-to-long-term swing model rather than a short-term intraday system. On average, positions remain active for 358.16 hours, equivalent to roughly 14.9 calendar days, with a median holding duration of 287.00 hours (11.96 days). This disparity between sampling frequency and holding time demonstrates that the strategy utilizes 15-minute bars for fine-tuned entry timing while capturing multi-week structural price moves. Execution frequency is intentionally sparse: the system completes an average of 10.32 trades per year, which translates to approximately 1.26 trades per active month. Exits occur at an average interval of 35.74 days, with a median gap of 30.49 days between closed positions. For deployment in automated trading environments, this low transaction cadence means trading robots operate with substantial idle periods between active positions, drastically reducing exposure to choppy market noise and market-on-close volatility.
3. Quality of historical results
The quality of historical performance in this CSGP strategy is characterized by strong payoff metrics and broad profit distribution across its trade log. Out of 73 total closed trades, 60 concluded in positive territory, while only 13 resulted in net losses. The average closed trade generated a return of +4.93%, aligning closely with the median trade return of +4.35%. This tight alignment between average and median returns indicates that overall cumulative profitability is not skewed by isolated statistical anomalies, but rather supported by a consistent, repeatable baseline. Furthermore, the single best trade in the dataset delivered +22.49%, while the largest single losing trade was limited to -12.72%. A crucial metric regarding the health of this strategy is the concentration of gross profits: the top three winning trades collectively generated only 15.15% of the total gross profit. This remarkably low concentration percentage proves that the profit factor of 7.50 is built upon widespread trade success across the entire sample rather than relying on a handful of fortunate multi-standard-deviation outliers.
4. Risk, drawdown and losing behavior
Risk control and downside control form a cornerstone of the strategy historical track record. Over the 7.07 years of historical evaluation, the maximum portfolio drawdown recorded was 17.44%. When evaluated alongside a cumulative return sum exceeding 2400%, this drawdown envelope underscores a highly controlled equity curve. The strategy sustained a maximum consecutive winning streak of 20 trades, contrasting sharply with a maximum consecutive losing streak of only 3 trades. Given that the worst single trade loss was capped at -12.72%, the strategy exhibits effective containment of adverse excursions even during losing periods. The modest maximum losing streak of 3 trades indicates that strategy degradation during unfavorable market regimes has historically been brief, allowing automated trading agents to recover equity without experiencing deep compounding drawdowns or catastrophic series of consecutive stop-outs.
5. Behavior through time and yearly stability
An analysis of the yearly performance breakdown demonstrates remarkable multi-year consistency across varying equity market cycles. Every single calendar year evaluated from 2019 through 2026 produced positive aggregate returns. In 2019, across 4 completed trades, the model registered a 100% win rate and a +20.28% return sum. The strategy experienced its highest execution volume in 2020, closing 16 trades with a 93.75% win rate and an exceptional return sum of +128.88%. Performance stabilized in subsequent years: 2021 generated +28.52% across 8 trades (75.00% win rate); 2022 produced +58.86% across 9 trades (77.78% win rate); 2023 yielded +24.89% across 7 trades (71.43% win rate); 2024 generated +35.96% across 11 trades (81.82% win rate); 2025 delivered +32.90% across 8 trades (87.50% win rate); and 2026 achieved +29.78% across 10 trades (70.00% win rate). The annual win rate remained within a tight band of 70.00% to 100% across all eight calendar years, proving that the algorithm underlying entry logic does not depend on a single bullish regime to achieve profitability.
6. Recent period since 2024-06-01 versus full history
To evaluate recent strategy efficacy, performance data from trades closed on or after June 1, 2024, was isolated and compared against the full historical baseline. During this recent window, the strategy completed 24 trades, accounting for approximately 32.9% of all trades in the 7-year sample. The cumulative sum of returns during this recent period reached +83.62%. With 24 trades closed over a period of roughly 28 months, the execution pace averaged approximately 0.86 trades per month, which remains consistent with the full-history average of 1.26 trades per active month. The successful generation of +83.62% in post-June 2024 market environments confirms that the system logic has retained its mathematical edge in contemporary trading conditions, demonstrating that the strategy performance is not merely an artifact of early historical periods.
7. Strengths and limitations
The primary structural strengths of this algorithmic model include an exceptional profit factor of 7.50, an overall win rate of 82.19%, a modest maximum drawdown of 17.44%, and a well-distributed gross profit profile where the top three trades contribute just 15.15% of total gains. Additionally, its yearly stability is underscored by uninterrupted positive annual returns from 2019 through 2026. Conversely, key limitations center on the total trade sample size. With 73 closed trades across 7.07 years, the sample size is statistically lean, producing an average of only 10.32 trades per year. This low transaction frequency means that operators deploying automated trading bots must exercise significant patience, as extended holding times averaging 358 hours require enduring long periods of open position float without frequent exit signals. Furthermore, because backtested performance does not incorporate execution factors like real-world order routing slippage or transaction fees, real-world deployment metrics may experience slight variations.
8. DevioLab analytical conclusion
In conclusion, the CSGP 0 +2434.61% 1TRAD-CTN1 algorithm represents an outstanding quantitative model for CSGP trading automation. By combining 15-minute precision entries with multi-week swing holding times, the strategy achieves a rare balance of high win rate (82.19%), low drawdown (17.44%), and high profit factor (7.50). Its top ranking on DevioLab with a score of 80.04 is fully validated by its multi-year annual consistency and well-balanced profit distribution. For quantitative traders and operators seeking logic for automated trading bots or decision-making agents, this strategy offers a robust, low-frequency framework that has demonstrated sustained resilience across multiple market regimes over a seven-year historical backtest.
9. Data scope and methodology
All analytical conclusions presented in this article are derived exclusively from historical simulated closed-trade statistics generated between September 16, 2019, and October 12, 2026, on 15-minute price data for CSGP. Performance figures represent the uncompounded simple sum of percentage returns across 73 closed trades. Recent metrics since June 1, 2024, capture all positions closed on or after that timestamp UTC. These backtested results do not account for external execution costs such as slippage, exchange commissions, or borrow fees, nor do they reflect real-time live trading capital execution. Historical backtest performance provides a statistical framework for strategy evaluation but does not guarantee future financial results.