ci: fix lint baseline and stabilize failing main tests

src/brain/cache.py

@@ -13,8 +13,8 @@ import hashlib
 import json
 import logging
 import time
-from dataclasses import dataclass, field
-from typing import Any, TYPE_CHECKING
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
     from src.brain.gemini_client import TradeDecision
@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
 class CacheEntry:
     """Cached decision with metadata."""
 
-    decision: "TradeDecision"
+    decision: TradeDecision
     cached_at: float  # Unix timestamp
     hit_count: int = 0
     market_data_hash: str = ""
@@ -239,9 +239,7 @@ class DecisionCache:
         """
         current_time = time.time()
         expired_keys = [
-            k
-            for k, v in self._cache.items()
-            if current_time - v.cached_at > self.ttl_seconds
+            k for k, v in self._cache.items() if current_time - v.cached_at > self.ttl_seconds
         ]
 
         count = len(expired_keys)

src/brain/context_selector.py

@@ -11,14 +11,14 @@ from __future__ import annotations
 
 from dataclasses import dataclass
 from datetime import UTC, datetime
-from enum import Enum
+from enum import StrEnum
 from typing import Any
 
 from src.context.layer import ContextLayer
 from src.context.store import ContextStore
 
 
-class DecisionType(str, Enum):
+class DecisionType(StrEnum):
     """Type of trading decision being made."""
 
     NORMAL = "normal"  # Regular trade decision
@@ -183,9 +183,7 @@ class ContextSelector:
             ContextLayer.L1_LEGACY,
         ]
 
-        scores = {
-            layer: self.score_layer_relevance(layer, decision_type) for layer in all_layers
-        }
+        scores = {layer: self.score_layer_relevance(layer, decision_type) for layer in all_layers}
 
         # Filter by minimum score
         selected_layers = [layer for layer, score in scores.items() if score >= min_score]

src/brain/gemini_client.py

@@ -25,12 +25,12 @@ from typing import Any
 
 from google import genai
 
-from src.config import Settings
-from src.data.news_api import NewsAPI, NewsSentiment
-from src.data.economic_calendar import EconomicCalendar
-from src.data.market_data import MarketData
 from src.brain.cache import DecisionCache
 from src.brain.prompt_optimizer import PromptOptimizer
+from src.config import Settings
+from src.data.economic_calendar import EconomicCalendar
+from src.data.market_data import MarketData
+from src.data.news_api import NewsAPI, NewsSentiment
 
 logger = logging.getLogger(__name__)
 
@@ -159,16 +159,12 @@ class GeminiClient:
             return ""
 
         # Check for upcoming high-impact events
-        upcoming = self._economic_calendar.get_upcoming_events(
-            days_ahead=7, min_impact="HIGH"
-        )
+        upcoming = self._economic_calendar.get_upcoming_events(days_ahead=7, min_impact="HIGH")
 
         if upcoming.high_impact_count == 0:
             return ""
 
-        lines = [
-            f"Upcoming High-Impact Events: {upcoming.high_impact_count} in next 7 days"
-        ]
+        lines = [f"Upcoming High-Impact Events: {upcoming.high_impact_count} in next 7 days"]
 
         if upcoming.next_major_event is not None:
             event = upcoming.next_major_event
@@ -180,9 +176,7 @@ class GeminiClient:
         # Check for earnings
         earnings_date = self._economic_calendar.get_earnings_date(stock_code)
         if earnings_date is not None:
-            lines.append(
-                f"  Earnings: {stock_code} on {earnings_date.strftime('%Y-%m-%d')}"
-            )
+            lines.append(f"  Earnings: {stock_code} on {earnings_date.strftime('%Y-%m-%d')}")
 
         return "\n".join(lines)
 
@@ -235,9 +229,7 @@ class GeminiClient:
 
         # Add foreigner net if non-zero
         if market_data.get("foreigner_net", 0) != 0:
-            market_info_lines.append(
-                f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}"
-            )
+            market_info_lines.append(f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}")
 
         market_info = "\n".join(market_info_lines)
 
@@ -249,8 +241,7 @@ class GeminiClient:
             market_info += f"\n\n{external_context}"
 
         json_format = (
-            '{"action": "BUY"|"SELL"|"HOLD", '
-            '"confidence": <int 0-100>, "rationale": "<string>"}'
+            '{"action": "BUY"|"SELL"|"HOLD", "confidence": <int 0-100>, "rationale": "<string>"}'
         )
         return (
             f"You are a professional {market_name} trading analyst.\n"
@@ -289,15 +280,12 @@ class GeminiClient:
 
         # Add foreigner net if non-zero
         if market_data.get("foreigner_net", 0) != 0:
-            market_info_lines.append(
-                f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}"
-            )
+            market_info_lines.append(f"Foreigner Net Buy/Sell: {market_data['foreigner_net']}")
 
         market_info = "\n".join(market_info_lines)
 
         json_format = (
-            '{"action": "BUY"|"SELL"|"HOLD", '
-            '"confidence": <int 0-100>, "rationale": "<string>"}'
+            '{"action": "BUY"|"SELL"|"HOLD", "confidence": <int 0-100>, "rationale": "<string>"}'
         )
         return (
             f"You are a professional {market_name} trading analyst.\n"
@@ -339,25 +327,19 @@ class GeminiClient:
             data = json.loads(cleaned)
         except json.JSONDecodeError:
             logger.warning("Malformed JSON from Gemini — defaulting to HOLD")
-            return TradeDecision(
-                action="HOLD", confidence=0, rationale="Malformed JSON response"
-            )
+            return TradeDecision(action="HOLD", confidence=0, rationale="Malformed JSON response")
 
         # Validate required fields
         if not all(k in data for k in ("action", "confidence", "rationale")):
             logger.warning("Missing fields in Gemini response — defaulting to HOLD")
             # Preserve raw text in rationale so prompt_override callers (e.g. pre_market_planner)
             # can extract their own JSON format from decision.rationale (#245)
-            return TradeDecision(
-                action="HOLD", confidence=0, rationale=raw
-            )
+            return TradeDecision(action="HOLD", confidence=0, rationale=raw)
 
         action = str(data["action"]).upper()
         if action not in VALID_ACTIONS:
             logger.warning("Invalid action '%s' from Gemini — defaulting to HOLD", action)
-            return TradeDecision(
-                action="HOLD", confidence=0, rationale=f"Invalid action: {action}"
-            )
+            return TradeDecision(action="HOLD", confidence=0, rationale=f"Invalid action: {action}")
 
         confidence = int(data["confidence"])
         rationale = str(data["rationale"])
@@ -445,9 +427,7 @@ class GeminiClient:
         # not a parsed TradeDecision. Skip parse_response to avoid spurious
         # "Missing fields" warnings and return the raw response directly. (#247)
         if "prompt_override" in market_data:
-            logger.info(
-                "Gemini raw response received (prompt_override, tokens=%d)", token_count
-            )
+            logger.info("Gemini raw response received (prompt_override, tokens=%d)", token_count)
             # Not a trade decision — don't inflate _total_decisions metrics
             return TradeDecision(
                 action="HOLD", confidence=0, rationale=raw, token_count=token_count
@@ -546,9 +526,7 @@ class GeminiClient:
     # Batch Decision Making (for daily trading mode)
     # ------------------------------------------------------------------
 
-    async def decide_batch(
-        self, stocks_data: list[dict[str, Any]]
-    ) -> dict[str, TradeDecision]:
+    async def decide_batch(self, stocks_data: list[dict[str, Any]]) -> dict[str, TradeDecision]:
         """Make decisions for multiple stocks in a single API call.
 
         This is designed for daily trading mode to minimize API usage

src/brain/prompt_optimizer.py

@@ -179,7 +179,8 @@ class PromptOptimizer:
             # Minimal instructions
             prompt = (
                 f"{market_name} trader. Analyze:\n{data_str}\n\n"
-                'Return JSON: {"action":"BUY"|"SELL"|"HOLD","confidence":<0-100>,"rationale":"<text>"}\n'
+                "Return JSON: "
+                '{"action":"BUY"|"SELL"|"HOLD","confidence":<0-100>,"rationale":"<text>"}\n'
                 "Rules: action=BUY/SELL/HOLD, confidence=0-100, rationale=concise. No markdown."
             )
         else: