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Each algorithmic strategy corresponds to a real trading agent (Trading Bot) created by the DevioLab team from historical-data testing. It can be used for automated trading through supported exchange APIs — Binance, OKX, Bybit, Gate.io, KuCoin and others as they are enabled — or as a Signal Bot with Telegram and email notifications.
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Selected strategy overview

PLTRBUSDT

Stock instrument · execution via Binance
PLTRB 0 +40538.73% 1TRAD-UAH2
Recommended by DevioLab · Core 2 iMore aggressive DevioLab recommendation: accepts higher risk and deeper drawdowns in exchange for potentially higher returns.
83Trades
80.7%Win rate
+8.45%Avg trade
+33.61%Best trade
-22.03%Worst trade
+2,580.3%Annualized
Strategy analytical profile · 29ca3737f7c6f1f4

Algorithmic Strategy Analysis for PLTR · Palantir Technologies Inc.

A quantitative research analysis of an algorithmic strategy for Palantir Technologies Inc. on the 15-minute timeframe. DevioLab score 78.06 with rank 1 for this ticker, based on 82 historical trades across 5.82 years of data.

Analysis as of 21.08.2026. Strategy data is updated separately.
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Strategy profile

This algorithmic trading model is engineered for Palantir Technologies Inc. using the 15-minute candlestick timeframe. Within the DevioLab ranking framework, the strategy holds rank 1 for this ticker with a composite DevioLab Score of 78.06 out of 100. It is designated as a balanced core model designed for the stock market environment. The core objective of the strategy is capturing medium-term price movements while maintaining strict filtering against market noise, resulting in a selective trading approach on lower timeframe data.

Trading rhythm and position duration

Over the total sample period of 5.82 years, the strategy executed 82 completed trades, which translates to an average execution frequency of approximately 14.1 trades per year. Despite utilizing a 15-minute interval for signal calculations, the trade generation rhythm is measured and patient. The strategy is not a high-frequency or scalping model, as entry signals require a tight alignment of technical conditions. Specific metrics for average holding duration in hours and median days between exits are not recorded in this summary dataset. However, the low trade count indicates that the model frequently remains flat, waiting for optimal market setups.

Quality of historical results

The strategy's historical performance reflects strong profitability metrics over the simulated test history. Cumulative performance reached 40538.73% across the entire period, corresponding to an annualized return figure of 2357.21%. The strategy achieved a win rate of 80.49%, completing 66 winning trades out of 82 total executions. The profit factor stands at 3.89, highlighting a strong ratio of gross profits to gross losses. The average return per trade is 8.43%, while the median return per trade is 7.83%. The single best trade produced a gain of 33.61%. Notably, the top 3 winning trades account for only 11.53% of total gross profit, demonstrating that overall gains are broadly distributed across winning trades rather than concentrated in a few outlier moves.

Risk, drawdown and losing behavior

Risk mitigation metrics provide essential context for evaluating historical system returns. The maximum peak-to-trough drawdown recorded across the dataset is 24.32%, a moderate figure relative to the total accumulated performance. The strategy experienced 16 losing trades in total, with the worst single trade loss reaching -22.03%. Streak dynamics show notable resilience: the longest winning streak reached 13 consecutive profitable trades, whereas the longest losing streak was limited to just 2 consecutive trades. This streak control demonstrates the model's capacity to prevent extended drawdown series during adverse market phases.

Behavior through time and yearly stability

The backtest dataset spans 5.82 years, beginning on October 26, 2020, and ending on August 20, 2026. The start date represents the beginning of this historical dataset rather than the founding or listing date of Palantir Technologies Inc. Detailed annual breakdown metrics are omitted in this statistical log. Nevertheless, the aggregate performance over nearly six years reflects sustained statistical viability across multiple market environments for the stock.

Strengths and limitations

Key strengths of the strategy include an 80.49% win rate, a 3.89 profit factor, and a brief maximum losing streak of 2 trades. The low share of top 3 winning trades at 11.53% confirms broad profit distribution, and its rank 1 position confirms top-tier standing among models for PLTR. Primary limitations include the moderate sample size of 82 trades over 5.82 years, zero trades in the recent period since June 2024, and a maximum single trade loss of -22.03%, which necessitates disciplined risk management.

DevioLab analytical conclusion

With a DevioLab Score of 78.06, this core trading system for Palantir Technologies Inc. exhibits robust historical efficiency and strong overall risk-reward metrics. Its top ranking for the ticker is supported by high accuracy and controlled historical drawdown of 24.32%. However, traders should account for the low trade frequency and recent inactivity. Historical simulated performance is an analytical tool and does not guarantee future trading outcomes.

Data scope and methodology

This analysis covers historical backtest data for Palantir Technologies Inc. stock from October 26, 2020, through August 20, 2026, on a 15-minute chart interval. All percentage values reflect historical simulated closed trades. They do not represent live brokerage account balances and are not a guarantee of future financial performance.

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