OPEN-SOURCE SCRIPT
test - Classification
Tensor-Based Classification Experiment
This innovative script represents an experimental foray into classification techniques, specifically designed to analyze returns within a compact time frame. By leveraging tensor-based analytics, it generates a comprehensive table that visually illustrates the distribution of counts across both current and historical bars, providing valuable insights into market patterns.
The script's primary objective is to classify returns over a small window, using this information to inform trading decisions. The output table showcases a normal distribution of count values for each bar in the lookback period, allowing traders to gain a deeper understanding of market behavior and identify potential opportunities.
Key Features:
This innovative script represents an experimental foray into classification techniques, specifically designed to analyze returns within a compact time frame. By leveraging tensor-based analytics, it generates a comprehensive table that visually illustrates the distribution of counts across both current and historical bars, providing valuable insights into market patterns.
The script's primary objective is to classify returns over a small window, using this information to inform trading decisions. The output table showcases a normal distribution of count values for each bar in the lookback period, allowing traders to gain a deeper understanding of market behavior and identify potential opportunities.
Key Features:
- Experimental classification approach utilizing tensor-based analytics
- Compact time frame analysis (small window)
- Comprehensive table displaying return counts across current and historical bars
- Normal distribution visualization for better insight into market patterns
- By exploring this script, traders can gain a deeper understanding of the underlying dynamics driving market movements and develop more effective trading strategies.
Skrypt open-source
W prawdziwym duchu TradingView autor tego skryptu opublikował go jako open source, aby inwestorzy mogli go zrozumieć i zweryfikować. Pozdrowienia dla autora! Możesz go używać bezpłatnie, ale ponowne użycie tego kodu w publikacji podlega Zasadom Regulaminu. Możesz go oznaczyć jako ulubione, aby użyć go na wykresie.
Wyłączenie odpowiedzialności
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