fix: lint errors (import sorting, unused vars)

services/aggregation/interpolation.py

@@ -11,7 +11,6 @@ from __future__ import annotations
 
 import json
 import logging
-import uuid
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
 

services/aggregation/projection.py

@@ -16,7 +16,7 @@ from datetime import datetime, timezone
 
 import asyncpg
 
-from services.shared.schemas import TrendDirection, TrendSummary
+from services.shared.schemas import TrendSummary
 
 logger = logging.getLogger("projection")
 
@@ -169,10 +169,8 @@ def project_macro_decay(
 
         # Decay factor: ratio of future impact to current impact
         if half_life > 0:
-            current_factor = math.pow(2.0, -current_age_days / half_life)
             future_factor = math.pow(2.0, -future_age_days / half_life)
         else:
-            current_factor = 0.0
             future_factor = 0.0
 
         severity_w = _SEVERITY_WEIGHT.get(ev.severity, 0.25)

services/aggregation/worker.py

@@ -27,6 +27,12 @@ from services.aggregation.evidence import (
     rank_evidence as _rank_evidence_composite,
 )
 from services.aggregation.market_context import fetch_market_context
+from services.aggregation.pattern_matcher import find_self_patterns
+from services.aggregation.projection import (
+    MacroEventInfo,
+    compute_projection,
+    persist_trend_projection,
+)
 from services.aggregation.scoring import (
     ScoringConfig,
     WeightedSignal,
@@ -34,23 +40,16 @@ from services.aggregation.scoring import (
     sentiment_to_numeric,
     weighted_sentiment_average,
 )
+from services.aggregation.signal_propagation import (
+    CompetitiveSignalRecord,
+    build_pattern_weighted_signals,
+)
 from services.shared.metrics import (
     AGGREGATION_CONTRADICTION_SCORE,
     AGGREGATION_DURATION,
     AGGREGATION_SIGNALS_PROCESSED,
     AGGREGATION_WINDOWS_COMPUTED,
 )
-from services.aggregation.pattern_matcher import find_self_patterns
-from services.aggregation.projection import (
-    MacroEventInfo,
-    TrendProjection,
-    compute_projection,
-    persist_trend_projection,
-)
-from services.aggregation.signal_propagation import (
-    CompetitiveSignalRecord,
-    build_pattern_weighted_signals,
-)
 from services.shared.schemas import TrendDirection, TrendSummary, TrendWindow
 
 logger = logging.getLogger(__name__)

services/api/app.py

@@ -16,6 +16,7 @@ import json
 import logging
 import time as _time
 from contextlib import asynccontextmanager
+from dataclasses import asdict
 from datetime import datetime, timezone
 from typing import Any, Optional
 
@@ -27,11 +28,16 @@ from pydantic import BaseModel
 from starlette.middleware.base import BaseHTTPMiddleware
 from starlette.responses import Response
 
+from services.aggregation.pattern_matcher import (
+    find_cross_company_patterns,
+    find_self_patterns,
+)
 from services.extractor.metrics import get_model_performance_summary
 from services.shared.audit import get_entity_audit_trail, get_order_audit_trail, record_audit_event
 from services.shared.config import load_config
 from services.shared.db import get_pg_pool
 from services.shared.logging import new_trace_id, set_trace_context, setup_logging
+from services.shared.schemas import MAJOR_DECISION_CATALYSTS
 
 logger = logging.getLogger("query_api")
 
@@ -2092,14 +2098,6 @@ async def toggle_competitive_layer(body: CompetitiveToggleBody):
 # (Requirements 10.1, 10.2, 10.3, 10.4, 11.4, 11.6)
 # ---------------------------------------------------------------------------
 
-from dataclasses import asdict
-
-from services.aggregation.pattern_matcher import (
-    find_cross_company_patterns,
-    find_self_patterns,
-)
-from services.shared.schemas import MAJOR_DECISION_CATALYSTS
-
 
 def _pattern_to_dict(p) -> dict[str, Any]:
     """Convert a HistoricalPattern dataclass to a JSON-safe dict."""

services/extractor/event_classifier.py

@@ -461,10 +461,9 @@ async def classify_global_event(
     model_name = ollama_client._config.model
 
     # Persist prompt to MinIO
-    prompt_ref = None
     if minio_client:
         try:
-            prompt_ref = _upload_classification_prompt(
+            _upload_classification_prompt(
                 minio_client, document_id, prompts, model_name, timestamp=ts,
             )
         except Exception:

services/lake_publisher/iceberg.py

@@ -128,8 +128,8 @@ TABLE_SCHEMAS: dict[str, pa.Schema] = {
 # These are added after the initial dict definition.
 def _register_worker_schemas() -> None:
     from services.lake_publisher.worker import (
-        COMPETITOR_RELATIONSHIPS_SCHEMA,
         COMPETITIVE_SIGNALS_SCHEMA,
+        COMPETITOR_RELATIONSHIPS_SCHEMA,
         GLOBAL_EVENTS_SCHEMA,
         MACRO_IMPACTS_SCHEMA,
         TREND_PROJECTIONS_SCHEMA,

services/lake_publisher/jobs.py

@@ -35,6 +35,8 @@ from minio import Minio
 
 from services.lake_publisher.partitions import partition_values
 from services.lake_publisher.worker import (
+    publish_competitive_signal_fact,
+    publish_competitor_relationship_fact,
     publish_document_extraction,
     publish_document_extractions_batch,
     publish_document_fact,
@@ -48,8 +50,6 @@ from services.lake_publisher.worker import (
     publish_trade_fill,
     publish_trade_order,
     publish_trend_projection_fact,
-    publish_competitor_relationship_fact,
-    publish_competitive_signal_fact,
 )
 from services.shared.config import load_config
 from services.shared.db import get_minio, get_pg_pool, get_redis

services/recommendation/worker.py

@@ -15,6 +15,7 @@ from datetime import datetime, timezone
 import asyncpg
 from minio import Minio
 
+from services.aggregation.projection import TrendProjection
 from services.lake_publisher.worker import publish_recommendation_facts
 from services.recommendation.eligibility import (
     EligibilityConfig,
@@ -31,7 +32,6 @@ from services.recommendation.thesis_llm import (
     THESIS_PROMPT_VERSION,
     rewrite_thesis_with_llm,
 )
-from services.aggregation.projection import TrendProjection
 from services.shared.config import OllamaConfig
 from services.shared.metrics import (
     RECOMMENDATION_CONFIDENCE,

services/symbol_registry/app.py

@@ -12,9 +12,9 @@ from pydantic import BaseModel, field_validator
 from services.shared.config import load_config
 from services.shared.db import get_pg_pool
 from services.shared.logging import setup_logging
-from services.symbol_registry.exposure import router as exposure_router
-from services.symbol_registry.competitors import router as competitors_router
 from services.symbol_registry.competitor_inference import router as inference_router
+from services.symbol_registry.competitors import router as competitors_router
+from services.symbol_registry.exposure import router as exposure_router
 
 config = load_config()
 pool: Optional[asyncpg.Pool] = None