chore(deps-dev): bump mypy to 2.4.0 and fix new type errors (supersedes #163)

CHANGELOG.md

@@ -24,6 +24,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Internal
 
+- Bump mypy to 2.4.0 and drop now-unused type ignores (supersedes #163).
 - Normalize Polars `Series.sum()` aggregates to `float`/`int` before
   arithmetic so mypy is clean under polars 1.44 (`sum()` now types as a
   union that includes `Decimal`). Unblocks Dependabot #154.

src/project_x_py/sessions/filtering.py

@@ -8,7 +8,7 @@
 Date: 2025-08-28
 """
 
-from datetime import UTC, date, datetime, time
+from datetime import UTC, date, datetime, time, tzinfo
 from typing import Any
 
 import polars as pl
@@ -34,7 +34,7 @@ class SessionFilterMixin:
     CACHE_TTL_SECONDS = 3600  # Cache time-to-live in seconds
 
     # Cached timezone object for performance
-    _market_tz = None
+    _market_tz: tzinfo | None = None
 
     def __init__(
         self,

src/project_x_py/statistics/health.py

@@ -608,25 +608,25 @@ async def _score_resources(self, stats: ComprehensiveStats) -> float:
                 "memory_usage_percent", memory_stats.get("usage_percent", 0.0)
             )
 
-            if memory_usage_percent <= self.thresholds.memory_usage_excellent:  # type: ignore[operator]
+            if memory_usage_percent <= self.thresholds.memory_usage_excellent:
                 memory_score = 100.0
-            elif memory_usage_percent <= self.thresholds.memory_usage_good:  # type: ignore[operator]
+            elif memory_usage_percent <= self.thresholds.memory_usage_good:
                 ratio = (
-                    memory_usage_percent - self.thresholds.memory_usage_excellent  # type: ignore[operator]
+                    memory_usage_percent - self.thresholds.memory_usage_excellent
                 ) / (
                     self.thresholds.memory_usage_good
                     - self.thresholds.memory_usage_excellent
                 )
                 memory_score = 100.0 - (ratio * 20.0)
-            elif memory_usage_percent <= self.thresholds.memory_usage_warning:  # type: ignore[operator]
-                ratio = (memory_usage_percent - self.thresholds.memory_usage_good) / (  # type: ignore[operator]
+            elif memory_usage_percent <= self.thresholds.memory_usage_warning:
+                ratio = (memory_usage_percent - self.thresholds.memory_usage_good) / (
                     self.thresholds.memory_usage_warning
                     - self.thresholds.memory_usage_good
                 )
                 memory_score = 80.0 - (ratio * 40.0)
-            elif memory_usage_percent <= self.thresholds.memory_usage_critical:  # type: ignore[operator]
+            elif memory_usage_percent <= self.thresholds.memory_usage_critical:
                 ratio = (
-                    memory_usage_percent - self.thresholds.memory_usage_warning  # type: ignore[operator]
+                    memory_usage_percent - self.thresholds.memory_usage_warning
                 ) / (
                     self.thresholds.memory_usage_critical
                     - self.thresholds.memory_usage_warning
@@ -984,38 +984,38 @@ async def _check_resource_alerts(
                 "memory_usage_percent", memory_stats.get("usage_percent", 0.0)
             )
 
-            if memory_usage_percent >= self.thresholds.memory_usage_critical:  # type: ignore[operator]
+            if memory_usage_percent >= self.thresholds.memory_usage_critical:
                 alerts.append(
                     {
                         "level": AlertLevel.CRITICAL.value,
                         "category": "resources",
                         "message": f"Critical memory usage: {memory_usage_percent:.1f}%",
                         "metric": "memory_usage_percent",
-                        "current_value": memory_usage_percent,  # type: ignore[typeddict-item]
+                        "current_value": memory_usage_percent,
                         "threshold": self.thresholds.memory_usage_critical,
                         "recommendation": "Immediately review memory leaks and restart if necessary",
                     }
                 )
-            elif memory_usage_percent >= self.thresholds.memory_usage_warning:  # type: ignore[operator]
+            elif memory_usage_percent >= self.thresholds.memory_usage_warning:
                 alerts.append(
                     {
                         "level": AlertLevel.DEGRADED.value,
                         "category": "resources",
                         "message": f"High memory usage: {memory_usage_percent:.1f}%",
                         "metric": "memory_usage_percent",
-                        "current_value": memory_usage_percent,  # type: ignore[typeddict-item]
+                        "current_value": memory_usage_percent,
                         "threshold": self.thresholds.memory_usage_warning,
                         "recommendation": "Monitor memory trends and consider implementing cleanup routines",
                     }
                 )
-            elif memory_usage_percent >= self.thresholds.memory_usage_good:  # type: ignore[operator]
+            elif memory_usage_percent >= self.thresholds.memory_usage_good:
                 alerts.append(
                     {
                         "level": AlertLevel.WARNING.value,
                         "category": "resources",
                         "message": f"Elevated memory usage: {memory_usage_percent:.1f}%",
                         "metric": "memory_usage_percent",
-                        "current_value": memory_usage_percent,  # type: ignore[typeddict-item]
+                        "current_value": memory_usage_percent,
                         "threshold": self.thresholds.memory_usage_good,
                         "recommendation": "Review memory usage patterns and optimize data structures",
                     }