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/finance/generate_taker_order.md

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+---
+name: generate_taker_order
+kind: function
+lang: py
+domain: finance
+version: "1.0.0"
+purity: pure
+signature: "generate_taker_order(alpha: float, size_min: float, size_max: float, buy_prob: float, seed: int | None) -> dict"
+description: "Genera una market order de taker con tamano distribuido segun power-law (Pareto). Alpha bajo produce ordenes mas grandes (ballenas)."
+tags: [simulation, taker, power-law, montecarlo, finance, order-book]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [numpy]
+tested: false
+tests: []
+test_file_path: ""
+file_path: "python/functions/finance/finance.py"
+---
+
+## Ejemplo
+
+```python
+order = generate_taker_order(
+    alpha=2.0,
+    size_min=1.0,
+    size_max=100.0,
+    buy_prob=0.5,
+    seed=42,
+)
+# {'side': 'buy', 'qty': 3.7}
+```
+
+## Notas
+
+Funcion pura cuando se fija seed. Con seed=None el resultado es no deterministico.
+La distribucion Pareto con alpha=2 modela bien la distribucion empirica de tamaños de ordenes en mercados reales.
+`size_max` actua como techo (clipping) para evitar ordenes extremas.
+Retorna dict con keys: `side` ('buy' o 'sell') y `qty` (float redondeado a 1 decimal).