CRM · Salesforce, Inc.
Stock Universe · global stock marketQuantitative Analysis of CRM 15-Minute Algorithmic Strategy: High-Precision Swing Performance and Low Drawdown Characteristics
This comprehensive analytical report examines the top-ranked algorithmic trading strategy for CRM (Salesforce) on the 15-minute timeframe, which achieved a DevioLab score of 82.90. Over a test period spanning September 2019 to October 2026 (7.07 years), the strategy produced a cumulative backtested return of +3449.88% across 147 completed trades, maintaining a high win rate of 78.91% and a profit factor of 2.50. Crucially, the strategy contained its maximum drawdown to 14.18%, demonstrating exceptional risk containment while capturing multi-day swing movements. Designed as an execution model suitable for automated trading bots and trading agents, the system displays balanced gain distribution, with its top three winning trades contributing just 13.56% of gross profits.
Analysis as of 11.10.2026. Strategy data is updated separately.Read full analysis
1. Strategy profile
The strategy designated as CRM 0 +3449.88% 1TRAD-ZAW4 occupies the top ranking for CRM on DevioLab with an overall evaluation score of 82.90. Operating on a 15-minute intraday candle interval, the model functions as a selective swing trading algorithm engineered to capture medium-term price expansions in CRM equity shares. Over the evaluated 7.07-year backtest window from September 16, 2019, through October 11, 2026, the strategy completed a total of 147 closed trade cycles. The cumulative historical return reached +3449.88%, corresponding to an annualized metric of 345.05%. The core architecture demonstrates a pronounced high-probability posture, securing 116 winning trades against 31 losing trades for a aggregate win rate of 78.91%. With a profit factor of 2.50, gross closed profits exceeded gross losses by a factor of two and a half times. The underlying decision rules are designed to serve as the core decision logic for automated trading bots, trading robots, or quantitative trading agents operating on systematic stock execution platforms.
2. Trading rhythm and position duration
Despite operating on a granular 15-minute chart resolution, the trading rhythm of this strategy reflects a disciplined multi-day swing framework rather than rapid intraday turnover. The average holding duration per position is 171.62 hours (approximately 7.15 calendar days), while the median holding duration stands at 138.25 hours (approximately 5.76 calendar days). This divergence indicates that while most positions exit within five to six days, occasional extended trend-following positions remain open significantly longer to extract broader trend movement. Trade frequency is notably restrained, averaging 20.79 completed trades per year, or approximately 1.84 trades per active month. Exits occur at an average interval of 17.39 days, with a median spacing of 15 days between closed trades. This low transaction frequency combined with multi-day holding times demonstrates that 15-minute price data is utilized for refined execution timing rather than hyperactive scalping. For automated trading agents, this spatial cadence minimizes execution overhead and reduces exposure to short-term market noise.
3. Quality of historical results
The distributional profile of closed trade returns reveals robust, structurally sound profitability across the trade population. The average return per completed trade is +2.78%, anchored by a median trade return of +2.25%. The close proximity between the average and median values confirms that historical returns are not heavily skewed by isolated, unrepeatable windfalls, but rather represent consistent performance across standard trade setups. The single best trade achieved a gain of +33.31%, while the worst recorded trade generated a loss of -14.18%. A critical metric illustrating performance health is the gross profit concentration: the three largest winning trades collectively accounted for only 13.56% of total gross profit. This low concentration proves that the strategy's cumulative gain of +3449.88% was generated through systematic repeatability across its 116 winning trades rather than dependency on a handful of anomalous market spikes. The profit payoff structure, paired with a 78.91% hit rate, underpins the strategy's high profit factor of 2.50.
4. Risk, drawdown and losing behavior
Risk containment is one of the most defining characteristics of this CRM quantitative strategy. Throughout the 7.07-year historical evaluation, the maximum observed drawdown was capped at 14.18%. This drawdown magnitude is particularly noteworthy when contrasted against the strategy's overall net return of +3449.88%, demonstrating a high degree of downside control relative to historical upside capital accumulation. The strategy's risk mitigation profile is further illustrated by its losing streak characteristics: the longest consecutive sequence of losing trades across the entire 147-trade dataset was just 2 trades. Conversely, the model achieved a peak winning streak of 11 consecutive profitable trades. The worst individual trade loss of -14.18% mirrors the maximum historical drawdown percentage almost exactly, suggesting that peak drawdown events typically coincided with isolated trade exits rather than prolonged compounding loss sequences. For algorithmic trading bots, this profile minimizes capital degradation during adverse market regimes.
5. Behavior through time and yearly stability
An examination of the annual performance breakdown demonstrates broad temporal stability across varying stock market environments. Following a single initial trade in late 2019 (+2.20%), the strategy generated positive cumulative trade returns in every full calendar year from 2020 through 2026. In 2020, the system produced +82.36% across 19 trades with an 89.47% win rate. Performance remained consistently positive during subsequent years: 2021 generated +31.55% (18 trades, 72.22% win rate); 2022 recorded +41.95% (20 trades, 65.00% win rate); 2023 yielded +51.87% (17 trades, 82.35% win rate); 2024 produced +63.14% (21 trades, 85.71% win rate); 2025 delivered +53.52% (26 trades, 76.92% win rate); and 2026 accumulated +82.37% (25 trades, 80.00% win rate). Trade volume remained extraordinarily steady across full years, ranging between 17 and 26 trades per annum. This structural consistency across both bull market phases and volatile corrective years highlights an adaptive underlying logic that does not rely on specific macro environments to generate alpha.
6. Recent period since 2024-06-01 versus full history
Evaluating recent sample data beginning June 1, 2024, provides a vital window into contemporary strategy effectiveness. In this recent period, the system completed 63 trades, generating a cumulative return sum of +178.73%. Notably, these 63 trades represent 42.86% of the entire 147-trade historical dataset, reflecting an increase in completed trade frequency during the 2024–2026 trading cycles compared to earlier years. Despite this higher operational frequency, performance quality remained intact, averaging approximately +2.84% per trade in the recent window—virtually identical to the full-history average trade return of +2.78%. This robust recent participation demonstrates that the strategy's entry and exit logic has maintained its structural efficacy in recent market regimes without experiencing decay or regime shift degradation.
7. Strengths and limitations
The primary strength of the strategy lies in its combination of a high win rate (78.91%), low historical maximum drawdown (14.18%), and low concentration of gains among top winners (13.56%). Its steady annual performance across 7 years provides strong empirical backtest backing for automated trading agents. Furthermore, the moderate trade frequency (20.79 trades per year) prevents over-trading. However, limitations must be noted. The strategy's average loss magnitude, as evidenced by a worst trade of -14.18% versus an average win reflected in the +2.25% median trade return, indicates that losing trades—though infrequent—can be substantial relative to typical individual gains. Additionally, because the total dataset consists of 147 closed trades over 7 years, statistical sample size considerations apply, meaning that prolonged periods of market regime shifts could test the algorithm's exit mechanisms in ways not fully captured in historical simulations.
8. DevioLab analytical conclusion
With a DevioLab score of 82.90 and a rank of 1 for the CRM ticker, this 15-minute algorithmic strategy represents a highly refined swing trading model. It successfully balances robust capital growth (+3449.88% total simulated return) with conservative risk management (14.18% maximum drawdown and a maximum losing streak of 2 trades). The structural stability observed across individual calendar years and the continued strong performance in the recent period post-June 2024 validate its candidacy as an execution logic framework for deployment in automated trading bots, trading robots, and quantitative trading agents targeting CRM stock.
9. Data scope and methodology
All figures and metrics cited in this report are derived directly from backtested historical trade logs for CRM on the 15-minute timeframe covering the period from September 16, 2019, to October 11, 2026. Trade statistics reflect completed, closed positions. Percentage returns represent simulated historical outputs and do not account for active execution factors such as real-time order slippage, broker commission structures, borrow fees, or dividend adjustments. Historical simulated results are not indicative of guaranteed future performance.