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_gbm_prices.md

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+---
+name: generate_gbm_prices
+kind: function
+lang: py
+domain: finance
+version: "1.0.0"
+purity: pure
+signature: "generate_gbm_prices(initial_price: float, n_ticks: int, sigma: float, mu: float, jump_intensity: float, jump_size_std: float, seed: int) -> list[float]"
+description: "Genera serie de precios fundamentales con Geometric Brownian Motion + jump-diffusion. S(t+1) = S(t) * exp((mu - sigma^2/2)*dt + sigma*sqrt(dt)*Z + J*N)."
+tags: [simulation, gbm, price, montecarlo, finance, stochastic]
+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
+prices = generate_gbm_prices(
+    initial_price=100.0,
+    n_ticks=1000,
+    sigma=0.02,
+    mu=0.0,
+    jump_intensity=0.01,
+    jump_size_std=0.05,
+    seed=42,
+)
+# prices[0] == 100.0
+# len(prices) == 1000
+```
+
+## Notas
+
+Funcion pura — el seed fija el resultado deterministicamente.
+`jump_intensity=0.0` desactiva los saltos (GBM puro).
+`dt=1.0` por tick (tiempo discreto). Para tiempo continuo, ajustar sigma y mu en consecuencia.
+Requiere numpy para la generacion de numeros aleatorios y el calculo de exp.