DHI · D.R. Horton, Inc.
Stock Universe · global stock marketQuantitative Analysis of the Top-Ranked DHI 15-Minute Swing Trading Strategy
An in-depth statistical examination of the primary 15-minute algorithmic trading strategy for DHI equity reveals a highly robust multi-day swing profile. Over a 7.06-year historical dataset comprising 234 completed trades, the strategy achieved a 76.07% win rate, a profit factor of 4.00, and a cumulative gain of 14,268.21% with a controlled maximum drawdown of 18.86%. This analysis investigates the interplay between trade pacing, holding duration, distribution of returns, and multi-year consistency to evaluate its viability for deployment in automated trading systems.
Analysis as of 19.10.2026. Strategy data is updated separately.Read full analysis
Strategy Profile
The subject of this quantitative evaluation is the top-ranked algorithmic trading strategy designed for DHI equity, operating on a 15-minute candle interval dataset spanning from September 16, 2019, through October 6, 2026. Over this 7.06-year historical backtest, the model completed 234 trades, generating a total cumulative gain of 14,268.21% and an annualized return metric of 188.14%. Evaluated under DevioLab's scoring framework, the strategy earned a score of 82.40, ranking first among all strategies benchmarked for DHI and securing designation as the core independent best selection for the ticker. The system is engineered to serve as execution logic for automated trading bots, algorithmic robots, and quantitative trading agents, translating intra-day price action into structured multi-day positioning.
Trading Rhythm and Position Duration
Despite utilizing a 15-minute intraday chart interval for entry and exit decisions, the strategy operates as a low-to-moderate frequency swing trading system rather than a high-frequency or scalping framework. Across the full dataset, the algorithm executed an average of 33.16 completed trades per year, which translates to approximately 2.82 trades per active month. Position holding times reflect a distinct multi-day structure: the average holding duration is 144.95 hours (approximately 6.04 days), while the median holding duration sits at 115.00 hours (roughly 4.79 days). The temporal spacing between trade exits further highlights this deliberate pacing, with an average of 10.79 days and a median of 9.02 days between closed positions. This combination of 15-minute signal evaluation and multi-day holding periods enables the algorithm to filter out high-frequency noise while capturing substantial medium-term price swings in DHI.
Quality of Historical Results
The historical performance profile displays remarkable structural health across payoff and accuracy metrics. Out of 234 completed trades, 178 resulted in positive returns, yielding a win rate of 76.07%, while 56 ended in losses. The strategy achieved a profit factor of 4.00, meaning gross historical profits outweighed gross losses fourfold. Crucially, the return distribution is exceptionally balanced and resistant to single-trade dependency. The average trade gain stands at 2.46%, closely tracking the median trade return of 2.08%. While the best single trade achieved a gain of 26.77%, the three largest winning trades combined account for only 8.73% of total gross historical profit. This extraordinarily low concentration ratio proves that the strategy's profitability stems from a steady, repeatable edge across dozens of trades rather than reliance on isolated outlier events.
Risk, Drawdown and Losing Behavior
Risk metrics reveal controlled downside exposure relative to the total accumulated performance. The maximum recorded peak-to-trough drawdown across the 7.06-year test history was held to 18.86%. The worst individual trade in the dataset yielded a loss of -13.82%, which represents a contained downside outlier given the asset's historical volatility. The distribution of losing trades also highlights equity curve stability: the longest consecutive losing streak was capped at just 4 trades, whereas the longest winning streak extended to 16 consecutive trades. Because losing periods were brief and individual trade drawdowns were structurally bounded, the equity curve sustained compounding momentum without enduring deep structural impairment.
Behavior Through Time and Yearly Stability
Year-by-year examination demonstrates consistent positive performance across varying equity market environments. Following a single setup trade in late 2019 (+9.98%), the strategy delivered strong positive returns in every subsequent calendar year. In 2020, it recorded 32 trades with a 78.13% win rate (+155.92% sum of returns). In 2021, trade volume held steady at 33 trades with a 75.76% win rate (+86.31%). The year 2022 marked the highest activity window with 41 trades, an 80.49% win rate, and +132.40% sum of returns. Performance remained stable in 2023 (32 trades, 75.00% win rate, +39.97%) and reached a peak win rate in 2024 at 84.62% across 39 trades (+78.52%). In 2025, 32 trades yielded a 68.75% win rate (+51.58%), followed by 2026 with 24 trades and a 62.50% win rate (+21.47%). Trade generation remained stable between 32 and 41 trades per full year, reflecting consistent signal generation without regime disruption.
Recent Period Since 2024-06-01 Versus Full History
A specialized audit of closed trades with exit timestamps on or after June 1, 2024, confirms sustained statistical vitality in recent market conditions. During this recent window, the strategy executed 77 completed trades, generating a cumulative sum of returns equal to 128.56%. The trade pacing during this period represents a slight increase in operational velocity, accounting for nearly one-third of all historical trades completed over the 7-year sample. This high concentration of recent closed trades provides a robust contemporary sample size, confirming that the high win rate and favorable payoff ratios observed in earlier years have persisted into modern trading regimes rather than decaying over time.
Strengths and Limitations
The primary quantitative strengths of this strategy include its high win rate (76.07%), superior profit factor (4.00), low maximum drawdown (18.86%), and near-zero dependency on top winners (top 3 winners account for just 8.73% of gross profit). These attributes make it highly suitable as decision engine logic for automated trading bots. Conversely, limitations stem from its holding structure. With an average holding duration of nearly 6 days and positions open across multiple calendar days, the system is exposed to overnight gap risk and macroeconomic announcements occurring outside regular trading hours. Additionally, while annual returns remained positive across all years, annual sums of trade returns fluctuated between +21.47% and +155.92%, illustrating that annual yield varies with market regime and trend persistence.
DevioLab Analytical Conclusion
With a DevioLab Score of 82.40 and the #1 rank for DHI, this 15-minute swing strategy represents a premier algorithmic model for the asset. Its key structural achievement is the combination of high win frequency with balanced payoff distribution, ensuring that statistical expectancy is distributed smoothly across many trades. For developers building quantitative execution infrastructure or traders configuring automated trading agents, the strategy offers a robust historical blueprint characterized by manageable drawdowns, strong multi-year stability, and high operational continuity through recent historical testing.
Data Scope and Methodology
This analysis is based strictly on historical backtest performance metrics for asset symbol DHI on a 15-minute candle interval between September 16, 2019, and October 6, 2026. Metrics are calculated from 234 closed historical trade events. All return calculations represent non-compounded sums and averages of closed percentage trade outcomes. This research represents simulated historical evaluation for algorithmic design purposes and does not account for execution slippage, broker commissions, order routing delays, or cash management constraints. Historical simulation statistics do not guarantee future live trading results and must not be construed as personal investment advice.