Sync from GitHub (tests passed)

app/ai_engine.py

@@ -2165,7 +2165,7 @@ def save_model_metadata_to_db(
         existing.importance_json = json.dumps(importance)
         existing.features_json = json.dumps(features)
         existing.metrics_json = json.dumps(metrics)
-        existing.trained_at = datetime.utcnow()
+        existing.trained_at = datetime.now(timezone.utc)
         logger.info(f"Updated model metadata in DB for {symbol}")
     else:
         new_record = ModelMetadata(

app/commentary.py

@@ -7,7 +7,7 @@ from __future__ import annotations
 
 import json
 import logging
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Optional
 
 from .openrouter_client import OpenRouterError, create_chat_completion
@@ -18,20 +18,7 @@ logger = logging.getLogger(__name__)
 VALID_STANCES = {"BULLISH", "NEUTRAL", "BEARISH"}
 
 COMMENTARY_RESPONSE_FORMAT = {
-    "type": "json_schema",
-    "json_schema": {
-        "name": "commentary_with_stance",
-        "strict": True,
-        "schema": {
-            "type": "object",
-            "properties": {
-                "stance": {"type": "string", "enum": ["BULLISH", "NEUTRAL", "BEARISH"]},
-                "commentary": {"type": "string"},
-            },
-            "required": ["stance", "commentary"],
-            "additionalProperties": False,
-        },
-    },
+    "type": "json_object",
 }
 
 
@@ -281,11 +268,9 @@ Data:
         "title": "CopperMind Commentary",
     }
 
-    async def _request_commentary(strict_schema: bool) -> str:
+    async def _request_commentary() -> str:
         kwargs = dict(base_request_kwargs)
-        if strict_schema:
-            kwargs["response_format"] = COMMENTARY_RESPONSE_FORMAT
-            kwargs["provider"] = {"require_parameters": True}
+        kwargs["response_format"] = COMMENTARY_RESPONSE_FORMAT
         data = await create_chat_completion(**kwargs)
         content = _extract_chat_message_content(data)
         if not content:
@@ -323,17 +308,7 @@ Data:
         return repaired
 
     try:
-        try:
-            content = await _request_commentary(strict_schema=True)
-        except OpenRouterError as exc:
-            message = str(exc).lower()
-            if exc.status_code == 404 and "no endpoints found" in message:
-                logger.warning(
-                    "Structured commentary request unsupported by provider routing; retrying relaxed."
-                )
-                content = await _request_commentary(strict_schema=False)
-            else:
-                raise
+        content = await _request_commentary()
 
         try:
             stance, commentary = _parse_commentary_payload(content)
@@ -400,7 +375,7 @@ def save_commentary_to_db(
         existing.predicted_return = predicted_return
         existing.sentiment_label = sentiment_label
         existing.ai_stance = ai_stance
-        existing.generated_at = datetime.utcnow()
+        existing.generated_at = datetime.now(timezone.utc)
         existing.model_name = settings.resolved_commentary_model
         logger.info("Updated AI commentary for %s (stance: %s)", symbol, ai_stance)
     else:

app/models.py

@@ -9,9 +9,14 @@ Tables:
 - AnalysisSnapshot: Cached analysis reports
 """
 
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Optional
 
+
+def _utcnow() -> datetime:
+    """Timezone-aware UTC now, replacing deprecated datetime.utcnow()."""
+    return datetime.now(timezone.utc)
+
 from sqlalchemy import (
     Column,
     Integer,
@@ -59,7 +64,7 @@ class NewsArticle(Base):
     
     # Timestamps
     published_at = Column(DateTime(timezone=True), nullable=False, index=True)
-    fetched_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow)
+    fetched_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow)
     
     # Relationships
     sentiment = relationship("NewsSentiment", back_populates="article", uselist=False)
@@ -91,7 +96,7 @@ class PriceBar(Base):
     adj_close = Column(Float, nullable=True)
     
     # When this record was fetched
-    fetched_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow)
+    fetched_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow)
     
     __table_args__ = (
         UniqueConstraint("symbol", "date", name="uq_price_symbol_date"),
@@ -136,7 +141,7 @@ class NewsSentiment(Base):
     model_name = Column(String(100), default="google/gemini-2.0-flash-exp:free")
     
     # When scored
-    scored_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow)
+    scored_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow)
     
     # Relationship
     article = relationship("NewsArticle", back_populates="sentiment")
@@ -169,7 +174,7 @@ class DailySentiment(Base):
     weighting_method = Column(String(50), default="recency_exponential")
     
     # When aggregated
-    aggregated_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow)
+    aggregated_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow)
     
     def __repr__(self):
         return f"<DailySentiment(date={self.date}, index={self.sentiment_index:.3f}, news={self.news_count})>"
@@ -191,7 +196,7 @@ class AnalysisSnapshot(Base):
     report_json = Column(JSON, nullable=False)
     
     # When this snapshot was generated
-    generated_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow, index=True)
+    generated_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow, index=True)
     
     # Model version used
     model_version = Column(String(100), nullable=True)
@@ -230,7 +235,7 @@ class AICommentary(Base):
     ai_stance = Column(String(20), nullable=True, default="NEUTRAL")
     
     # When generated
-    generated_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow, index=True)
+    generated_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow, index=True)
     
     # Model used
     model_name = Column(String(100), nullable=True)
@@ -262,7 +267,7 @@ class ModelMetadata(Base):
     metrics_json = Column(Text, nullable=True)
     
     # When the model was trained
-    trained_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow, index=True)
+    trained_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow, index=True)
     
     def __repr__(self):
         return f"<ModelMetadata(symbol={self.symbol}, trained_at={self.trained_at})>"
@@ -467,7 +472,7 @@ class NewsSentimentV2(Base):
     reasoning_json = Column(Text, nullable=True)
     model_fast = Column(String(100), nullable=True)
     model_reliable = Column(String(100), nullable=True)
-    scored_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow, index=True)
+    scored_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow, index=True)
 
     processed = relationship("NewsProcessed", back_populates="sentiment_v2_items")
 
@@ -498,7 +503,7 @@ class DailySentimentV2(Base):
     avg_confidence = Column(Float, nullable=True)
     avg_relevance = Column(Float, nullable=True)
     source_version = Column(String(20), nullable=False, default="v2")
-    aggregated_at = Column(DateTime(timezone=True), nullable=False, default=datetime.utcnow, index=True)
+    aggregated_at = Column(DateTime(timezone=True), nullable=False, default=_utcnow, index=True)
 
     def __repr__(self):
         return (

backtest/runner.py

@@ -17,7 +17,7 @@ import hashlib
 import json
 import logging
 from dataclasses import dataclass, asdict
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Optional
 
@@ -166,7 +166,7 @@ class BacktestRunner:
     
     def __init__(self, config: BacktestConfig):
         self.config = config
-        self.run_id = f"backtest-{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}"
+        self.run_id = f"backtest-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}"
     
     def fetch_prices(self, symbols: list[str], start: str, end: str) -> pd.DataFrame:
         """
@@ -476,7 +476,7 @@ class BacktestRunner:
         
         result = BacktestResult(
             run_id=self.run_id,
-            generated_at=datetime.utcnow().isoformat() + "Z",
+            generated_at=datetime.now(timezone.utc).isoformat() + "Z",
             config=self.config,
             champion={
                 "symbol_set": asdict(champion_set),