Sync from GitHub (tests passed)

app/ai_engine.py

@@ -1409,7 +1409,11 @@ def score_unscored_processed_articles(
 
         llm_results_by_id: dict[int, dict] = {}
         llm_candidates: list[dict] = []
-        global_rate_limited = getattr(score_unscored_processed_articles, "_rate_limited", False)
+
+        # Rate-limit flag is keyed to today's UTC date so it resets automatically at midnight.
+        today_utc = datetime.now(timezone.utc).date().isoformat()
+        rate_limited_date = getattr(score_unscored_processed_articles, "_rate_limited_date", None)
+        global_rate_limited = rate_limited_date == today_utc
 
         if settings.openrouter_api_key and llm_budget_remaining > 0 and not global_rate_limited:
             llm_take = min(len(chunk_items), llm_budget_remaining)
@@ -1430,10 +1434,14 @@ def score_unscored_processed_articles(
                 fast_model = str(llm_bundle.get("model_fast", fast_model))
                 reliable_model = str(llm_bundle.get("model_reliable", reliable_model))
 
-                # If LLM returned 100% fail and flagged rate limit:
+                # If LLM returned 100% fail and flagged rate limit, mark for today's UTC date.
+                # Flag resets automatically the next UTC day when the daily limit refreshes.
                 if llm_bundle.get("rate_limited", False):
-                    score_unscored_processed_articles._rate_limited = True
-                    logger.warning("V2 batch hit rate limit - disabling LLM for remaining chunks in this run.")
+                    score_unscored_processed_articles._rate_limited_date = datetime.now(timezone.utc).date().isoformat()
+                    logger.warning(
+                        "V2 batch hit OpenRouter daily rate limit - LLM scoring disabled for the rest of UTC day %s.",
+                        score_unscored_processed_articles._rate_limited_date,
+                    )
 
             except Exception as exc:
                 logger.warning("V2 LLM scoring failed for chunk starting at %s: %s", chunk_idx, exc)

app/features.py

@@ -18,6 +18,8 @@ import pandas as pd
 from sqlalchemy import func
 from sqlalchemy.orm import Session
 
+pd.set_option("future.no_silent_downcasting", True)
+
 from app.db import SessionLocal
 from app.models import PriceBar, DailySentiment, DailySentimentV2
 from app.settings import get_settings

deep_learning/inference/predictor.py

@@ -19,6 +19,8 @@ from functools import lru_cache
 from pathlib import Path
 from typing import Any, Dict, Optional
 
+import os
+
 import numpy as np
 import pandas as pd
 
@@ -142,7 +144,8 @@ class TFTPredictor:
             logger.error("Failed to create inference dataset: %s", exc)
             return {"error": str(exc)}
 
-        dl = ds.to_dataloader(train=False, batch_size=1, num_workers=0)
+        _nw = 0 if os.name == "nt" else 2
+        dl = ds.to_dataloader(train=False, batch_size=1, num_workers=_nw)
 
         try:
             import torch

deep_learning/models/tft_copper.py

@@ -179,15 +179,19 @@ def load_tft_model(
 # Interpretation helpers
 # ---------------------------------------------------------------------------
 
-def get_variable_importance(model) -> Dict[str, float]:
+def get_variable_importance(model, val_dataloader=None) -> Dict[str, float]:
     """
     Extract learned variable importance from the TFT's Variable Selection Networks.
 
     Returns a dict mapping feature name -> normalised importance score.
+    val_dataloader must be passed explicitly (model.val_dataloader() only works
+    inside a Lightning Trainer context and raises an error otherwise).
     """
+    if val_dataloader is None:
+        return {}
     try:
         interpretation = model.interpret_output(
-            model.predict(model.val_dataloader(), return_x=True),
+            model.predict(val_dataloader, return_x=True),
             reduction="sum",
         )
         importance = interpretation.get("encoder_variables", {})

deep_learning/training/trainer.py

@@ -230,7 +230,7 @@ def train_tft_model(
         logger.info("Test metrics: %s", {k: f"{v:.4f}" for k, v in test_metrics.items()})
 
     # ---- 8. Variable importance ----
-    var_importance = get_variable_importance(model)
+    var_importance = get_variable_importance(model, val_dataloader=val_dl)
 
     # ---- 9. Persist metadata ----
     result = {