feat: funciones Python datascience, finance, cybersecurity y pipelines

Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift,
diff_entities/relations, extract_entities/relations_llm, hotness_score, melt,
merge_graphs, pivot, build_entity/relation_schema_prompt.
Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order,
hawkes_intensity + módulo finance.py.
Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py.
Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

python/functions/datascience/estimate_pareto_alpha.md

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+---
+name: estimate_pareto_alpha
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def estimate_pareto_alpha(values: list[float], x_min_percentile: float = 90.0) -> dict"
+description: "Estima el exponente alpha de una distribución Pareto via MLE. Alpha bajo indica cola más pesada y mayor frecuencia de valores extremos."
+tags: [estimation, pareto, power-law, heavy-tail, statistics]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [numpy]
+tested: false
+tests: []
+test_file_path: ""
+file_path: "python/functions/datascience/datascience.py"
+---
+
+## Ejemplo
+
+```python
+import numpy as np
+# Simular datos con cola pesada
+values = list(np.random.pareto(2.0, 1000) + 1)
+result = estimate_pareto_alpha(values, x_min_percentile=90.0)
+# {'alpha': ~2.0, 'x_min': ..., 'n_tail': 100}
+```
+
+## Notas
+
+Usa el estimador MLE de Hill: α = n / Σ ln(xᵢ / x_min).
+x_min se determina como el percentil indicado de los valores positivos.
+Retorna alpha=0 si hay menos de 10 valores positivos o la cola tiene menos de 2 elementos.
+Función pura: requiere numpy instalado.