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/detect_drift.py

@@ -0,0 +1,86 @@
+"""detect_drift — detecta drift estadistico por z-score comparando metricas contra historial."""
+
+import math
+
+
+def detect_drift(
+    history: list[dict],
+    current: dict,
+    fields: list[str],
+    threshold: float = 2.0,
+) -> list[dict]:
+    """Detecta drift estadistico comparando metricas actuales contra el historial.
+
+    Usa z-score: si |z| > threshold, el campo ha drifteado. Pensado para
+    comparar metrics de executions sucesivas en operations.db.
+
+    Args:
+        history: Lista de dicts con metricas historicas. Cada dict puede
+                 contener cualquier combinacion de los campos indicados.
+        current: Dict con las metricas de la ejecucion actual.
+        fields: Lista de campos numericos a analizar.
+        threshold: Umbral de z-score para considerar drift. Default 2.0.
+
+    Returns:
+        Lista de dicts con: field, current, mean, std, z_score, drifted.
+        Si el historial tiene 0 o 1 punto, z_score=0.0 y drifted=False
+        porque no hay suficiente informacion estadistica.
+    """
+    results = []
+
+    for field in fields:
+        values = [
+            float(h[field])
+            for h in history
+            if field in h and h[field] is not None
+        ]
+
+        current_val = float(current.get(field, 0))
+
+        if len(values) == 0:
+            results.append({
+                "field": field,
+                "current": current_val,
+                "mean": 0.0,
+                "std": 0.0,
+                "z_score": 0.0,
+                "drifted": False,
+            })
+            continue
+
+        n = len(values)
+        mean = sum(values) / n
+
+        if n < 2:
+            # Un solo punto: no hay std, no podemos calcular z-score
+            results.append({
+                "field": field,
+                "current": current_val,
+                "mean": mean,
+                "std": 0.0,
+                "z_score": 0.0,
+                "drifted": False,
+            })
+            continue
+
+        variance = sum((v - mean) ** 2 for v in values) / n
+        std = math.sqrt(variance)
+
+        if std == 0.0:
+            # Todos los valores identicos: z_score indeterminado, no drift
+            z_score = 0.0
+            drifted = False
+        else:
+            z_score = (current_val - mean) / std
+            drifted = abs(z_score) > threshold
+
+        results.append({
+            "field": field,
+            "current": current_val,
+            "mean": mean,
+            "std": std,
+            "z_score": z_score,
+            "drifted": drifted,
+        })
+
+    return results