DE · Deere & Company
Stock Universe · globaler AktienmarktDE Algorithmic Strategy Analysis: High-Conviction Macro Holdings and Asymmetric Return Profiles in 15-Minute Execution
This quantitative evaluation analyzes a high-conviction algorithmic trading strategy for DE on the 15-minute timeframe across a 7.08-year historical backtest. Operating with extreme selectivity, the strategy generated a cumulative return of 1,125.36% across just 16 completed traDEs, achieving an 81.25% win rate and a profit factor of 5.0. With an average holding duration of nearly 2,978 hours (approximately 124 days) and a maximum drawdown capped at 7.30%, the algorithm DEmonstrates how intraday polling can be harnessed to execute long-term, multi-month macro position traDEs. DEsigned to serve as DEcision logic for automated trading bots, trading robots, or quantitative trading agents, the strategy presents a compelling risk-adjusted profile characterized by asymmetric upsiDE and exceptionally low transaction frequency.
Analyse vom 16.10.2026. Die Strategiedaten werden separat aktualisiert.Vollständige Analyse lesen
1. Strategy profile
The quantitative trading strategy evaluated here focuses on the equity asset DE, operating on a 15-minute candlestick chart interval. Spanning a 7.08-year testing history from September 16, 2019, through October 16, 2026, the strategy generated an aggregate historical simulated profit of 1,125.36%, translating to an annualized return metric of 219.37%. With a DEvioLab Score of 77.70, it holds the number 2 ranking among strategies evaluated for DE. DEspite evaluating market structure on a relatively fine 15-minute resolution, the strategy is characterized by extreme traDE selectivity, completing only 16 traDEs over the entire 7-year evaluation window. This setup illustrates a distinct DEsign moDEl where intraday data are monitored continuously to iDEntify rare structural entry points rather than to generate high-frequency turnover. In automated execution environments, this rule set functions as DEcision logic for trading bots, trading robots, or autonomous trading agents looking to capture prolonged equity trends while avoiding intraday noise and over-trading.
2. Trading rhythm and position duration
The operational caDEnce of this strategy DEparts radically from typical intraday 15-minute strategies. Across the 7.08 years of simulated execution, the average holding duration per traDE was 2,978.12 hours—equivalent to approximately 124 calendar days—with a median holding time of 1,472.38 hours (roughly 61.3 days). This indicates that while entries are timed using 15-minute resolution, positions are allowed to DEvelop over multi-month horizons. The frequency metrics reinforce this macroeconomic position-trading style. The strategy completes an average of 2.26 traDEs per year, or approximately 1 traDE per active month. The spacing between traDE exits is similarly wiDE, featuring an average of 158.95 days between closed positions and a median interval of 85 days. This structural combination of high-resolution entry monitoring and long holding durations minimizes traDE churn, creating a rhythm focused on multi-month trend capture rather than short-term price oscillation.
3. Quality of historical results
The performance profile exhibits exceptional win-rate efficiency paired with favorable traDE payoff asymmetry. Out of 16 completed historical traDEs, 13 resulted in net gains while only 3 were net losses, establishing an 81.25% win rate. The strategy generated a profit factor of 5.0, meaning total gross profits were five times the total gross losses. The average closed traDE return reached 18.71%, with a median traDE return of 14.67%. The best historical traDE achieved a gain of 75.42%, whereas the worst traDE suffered a loss of -7.30%. The top three winning traDEs collectively generated 51.62% of the strategy's total gross profit. While this confirms a DEgree of concentration in outlier moves, the substantial median traDE return of 14.67% DEmonstrates that profitability was broadly distributed across multiple successful multi-month trends rather than being DEpenDEnt solely on a single fortunate entry.
4. Risk, drawdown and losing behavior
Risk containment represents one of the strategy's most distinct historical attributes. The maximum peak-to-trough drawdown recorDEd across the entire 7.08-year backtest was 7.30%. Notably, this drawdown figure matches the worst single traDE loss of -7.30%, indicating that historical account equity DEclines were primarily driven by individual traDE exits rather than compounding series of consecutive losses. This drawdown behavior is further supported by streak metrics. The strategy's longest winning streak reached 7 consecutive profitable traDEs, whereas its longest losing streak remained capped at just 1 traDE. Because the strategy enters positions infrequently and holds them for multi-month spans, exposure risk is concentrated in market price fluctuations during active holdings rather than exposure to frequent execution events or sequential stop-outs.
5. Behavior through time and yearly stability
An examination of the yearly performance breakdown reveals consistent historical stability across varying market environments between 2020 and 2026: - 2020: 3 traDEs (2 wins, 1 loss), 66.67% win rate, sum of traDE returns +94.40% - 2021: 1 traDE (1 win, 0 losses), 100.00% win rate, sum of traDE returns +45.18% - 2022: 3 traDEs (3 wins, 0 losses), 100.00% win rate, sum of traDE returns +35.90% - 2023: 1 traDE (0 wins, 1 loss), 0.00% win rate, sum of traDE returns -3.78% - 2024: 1 traDE (1 win, 0 losses), 100.00% win rate, sum of traDE returns +19.30% - 2025: 3 traDEs (3 wins, 0 losses), 100.00% win rate, sum of traDE returns +33.73% - 2026: 4 traDEs (3 wins, 1 loss), 75.00% win rate, sum of traDE returns +74.59% Out of 7 calendar years, 6 produced positive net traDE outcomes. The only unprofitable year was 2023, which recorDEd a single traDE loss of -3.78%. Across all other years, traDE counts ranged between 1 and 4 per annum, DEmonstrating consistent performance stability without relying on localized cluster spikes in trading activity.
6. Recent period since 2024-06-01 versus full history
The recent eviDEnce window, DEfined by traDEs exiting on or after June 1, 2024, comprises 8 completed traDEs generating a cumulative traDE return sum of 127.62%. This means that 50% of the strategy's 16 total historical traDE exits occurred within this recent window spanning mid-2024 through late 2026. Comparing this recent period to the full dataset reveals an increase in traDE exit frequency during the final two years of the backtest. The strategy maintained high win rates in 2024 (100%), 2025 (100%), and 2026 (75%), generating 74.59% in cumulative traDE gains across 4 exits in 2026 alone. This indicates that the algorithm's multi-month position logic remained highly active and aligned with broaDEr market trends in recent years.
7. Strengths and limitations
The primary strength of this DE strategy lies in its outstanding return-to-drawdown efficiency. Combining an 81.25% win rate, a 5.0 profit factor, an 18.71% average traDE, and a maximum drawdown of just 7.30%, the historical profile DEmonstrates powerful asymmetric risk management. Furthermore, the low traDE count (2.26 traDEs per year) drastically limits exposure to market noise. Conversely, the principal limitation is the small statistical sample size. Generating only 16 completed traDEs over 7.08 years creates a wiDE statistical margin of error for backtested expectations. Additionally, long average holding times (~124 days) require patience and expose active positions to overnight gap risk, earnings releases, and macro shifts over multi-month periods. Finally, with the top three winning traDEs responsible for 51.62% of gross profits, overall performance relies on capturing large structural macro trends.
8. DEvioLab analytical conclusion
With a DEvioLab Score of 77.70 and a rank of #2 for DE, this quantitative strategy highlights the potential of using 15-minute price evaluation to direct long-term position trading. By filtering out day-to-day market volatility and executing only unDEr highly selective structural conditions, the algorithm historically captured major equity moves with minimal drawdown. For traDErs utilizing automated trading bots, trading robots, or quantitative trading agents, the strategy offers a low-turnover, macro-oriented DEcision mechanism. However, automated DEployers must weigh the impressive 5.0 profit factor and 81.25% win rate against the statistical constraints of a 16-traDE historical sample size and the operational discipline required to hold positions across several months.
9. Data scope and methodology
The metrics presented in this analysis are DErived from backtested, historical simulated trading results for DE on a 15-minute execution chart between September 16, 2019, and October 16, 2026. All figures, including win rates, average returns, holding periods, drawdowns, and profit factors, are computed strictly from completed closed traDE data. Simulated performance percentages represent mathematical backtest calculations and do not account for unstated live execution parameters such as slippage, broker commissions, borrow costs, diviDEnd adjustments, or exchange execution DElays. Historical results are non-predictive and do not guarantee future performance or live profitability.