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Visão geral da estratégia selecionada

PLTRBUSDT

Instrumento de ações · execução via Binance
PLTRB 0 +38005.71% 1TRAD-GJM8
Recomendado pela DevioLab · Core 2 iRecomendação mais agressiva da DevioLab: aceita maior risco e drawdowns mais profundos em troca de retornos potencialmente maiores.
85Operações
80.0%Taxa de acerto
+8.06%Operação média
+33.61%Melhor operação
-22.03%Pior operação
+2,452.9%Anualizado
Perfil analítico da estratégia · 5bd6b62c8e900ee9

Quantitative Analysis of Algorithmic Strategy for PLTR · Palantir Technologies Inc. on 15m Interval

A comprehensive quantitative analysis of a 15-minute trading strategy for Palantir Technologies Inc., ranked number 1 on DevioLab for this asset with a score of 79.88. The strategy demonstrates a profit factor of 5.0 and an 80% win rate across a 5.8-year historical dataset.

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Strategy profile

This quantitative trading model is engineered for Palantir Technologies Inc. (symbol PLTR) operating on a 15-minute chart timeframe within the stock market. In the DevioLab internal scoring system for this ticker, the strategy holds rank 1 with a DevioLab Score of 79.88. Over the evaluated historical timeline, the model completed 85 closed trades, generating a total cumulative gain of 38005.71% and an annualized performance metric of 2443.32%. The model achieves its results through selective entry conditions rather than continuous market exposure.

Trading rhythm and position duration

The execution frequency of this system is structured and low-volume, averaging 14.65 trades per year. Despite utilizing a 15-minute bar timeframe, the system does not engage in scalping or high-frequency turnover. Detailed metrics for average and median holding hours, as well as average and median days between exits, are not tracked in this dataset. However, an annualized rate of under 15 trades confirms that the algorithm spends extensive periods in cash waiting for specific technical setups.

Quality of historical results

The strategy exhibits high structural quality across its completed trades. Out of 85 total trades, 68 closed in profit and 17 in loss, delivering an 80% win rate. The profit factor stands at 5.0, reflecting a gross profit that is five times larger than gross losses. The average trade yield is 8.05%, with a median trade return of 7.46%. Profit distribution is exceptionally balanced across the sample: the top 3 winning trades account for only 11.81% of total gross profit, proving that returns are driven by consistent performance across many trades rather than isolated statistical outliers.

Risk, drawdown and losing behavior

Historical risk evaluation reveals a maximum peak-to-trough drawdown of 29.13%. The single best trade in the sample yielded 33.61%, while the worst trade incurred a loss of -22.03%. Streak dynamics show robust consistency, featuring a maximum winning streak of 13 consecutive trades compared to a maximum losing streak of just 3 trades. The controlled losing streaks help limit prolonged equity decay during adverse market phases.

Behavior through time and yearly stability

The backtest history spans 5.8 years, starting on October 26, 2020, and ending on August 14, 2026. While explicit breakdown stats by individual calendar year are not provided in this dataset, the multi-year history provides coverage across different structural market regimes for Palantir Technologies Inc. The total volume of 85 trades over almost six years highlights a strategy focused purely on high-conviction entries.

Strengths and limitations

Key strengths include a profit factor of 5.0, an 80% win rate, a short maximum losing streak of 3 trades, and a healthy profit distribution where the top 3 trades contribute only 11.81% of gross profits. Limitations stem from the modest sample size of 85 trades across 5.8 years and the absence of executed trades since June 2024. Additionally, a maximum drawdown of 29.13% underscores the need for disciplined risk management.

DevioLab analytical conclusion

With a top ranking for PLTR and a DevioLab Score of 79.88, this algorithmic strategy displays strong historical metrics, high trade precision, and a robust payoff ratio. Traders evaluating this model should take into account its low execution frequency and recent period of inactivity. It represents a highly selective quantitative approach to equity trading, though historical stats must always be interpreted with practical caution.

Data scope and methodology

All figures in this report are based on simulated historical closed trades for Palantir Technologies Inc. covering October 26, 2020, through August 14, 2026. These metrics represent backtested performance and do not guarantee future returns or reflect actual live brokerage execution. This content is provided for quantitative research purposes only and does not constitute financial advice.

Análise completa da estratégia