ci: fix lint baseline and stabilize failing main tests

src/analysis/backtest_cost_guard.py

@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
-from dataclasses import dataclass
 import math
+from dataclasses import dataclass
 
 
 @dataclass(frozen=True)

src/analysis/backtest_execution_model.py

@@ -2,12 +2,11 @@
 
 from __future__ import annotations
 
-from dataclasses import dataclass
 import math
+from dataclasses import dataclass
 from random import Random
 from typing import Literal
 
-
 OrderSide = Literal["BUY", "SELL"]
 
 
@@ -77,7 +76,9 @@ class BacktestExecutionModel:
                 reason="execution_failure",
             )
 
-        slip_mult = 1.0 + (slippage_bps / 10000.0 if request.side == "BUY" else -slippage_bps / 10000.0)
+        slip_mult = 1.0 + (
+            slippage_bps / 10000.0 if request.side == "BUY" else -slippage_bps / 10000.0
+        )
         exec_price = request.reference_price * slip_mult
 
         if self._rng.random() < partial_rate:

src/analysis/backtest_pipeline.py

@@ -10,8 +10,7 @@ from collections.abc import Sequence
 from dataclasses import dataclass
 from datetime import datetime
 from statistics import mean
-from typing import Literal
-from typing import cast
+from typing import Literal, cast
 
 from src.analysis.backtest_cost_guard import BacktestCostModel, validate_backtest_cost_model
 from src.analysis.triple_barrier import TripleBarrierSpec, label_with_triple_barrier

src/analysis/scanner.py

@@ -104,6 +104,7 @@ class MarketScanner:
 
             # Store in L7 real-time layer
             from datetime import UTC, datetime
+
             timeframe = datetime.now(UTC).isoformat()
             self.context_store.set_context(
                 ContextLayer.L7_REALTIME,
@@ -158,12 +159,8 @@ class MarketScanner:
         top_movers = valid_metrics[: self.top_n]
 
         # Detect breakouts and breakdowns
-        breakouts = [
-            m.stock_code for m in valid_metrics if self.analyzer.is_breakout(m)
-        ]
-        breakdowns = [
-            m.stock_code for m in valid_metrics if self.analyzer.is_breakdown(m)
-        ]
+        breakouts = [m.stock_code for m in valid_metrics if self.analyzer.is_breakout(m)]
+        breakdowns = [m.stock_code for m in valid_metrics if self.analyzer.is_breakdown(m)]
 
         logger.info(
             "%s scan complete: %d scanned, top momentum=%.1f, %d breakouts, %d breakdowns",
@@ -228,10 +225,9 @@ class MarketScanner:
 
         # If we removed too many, backfill from current watchlist
         if len(updated) < len(current_watchlist):
-            backfill = [
-                code for code in current_watchlist
-                if code not in updated
-            ][: len(current_watchlist) - len(updated)]
+            backfill = [code for code in current_watchlist if code not in updated][
+                : len(current_watchlist) - len(updated)
+            ]
             updated.extend(backfill)
 
         logger.info(

src/analysis/smart_scanner.py

@@ -158,7 +158,12 @@ class SmartVolatilityScanner:
                         price = latest_close
                     latest_high = _safe_float(latest.get("high"))
                     latest_low = _safe_float(latest.get("low"))
-                    if latest_close > 0 and latest_high > 0 and latest_low > 0 and latest_high >= latest_low:
+                    if (
+                        latest_close > 0
+                        and latest_high > 0
+                        and latest_low > 0
+                        and latest_high >= latest_low
+                    ):
                         intraday_range_pct = (latest_high - latest_low) / latest_close * 100.0
                     if volume <= 0:
                         volume = _safe_float(latest.get("volume"))
@@ -234,9 +239,7 @@ class SmartVolatilityScanner:
                 limit=50,
             )
         except Exception as exc:
-            logger.warning(
-                "Overseas fluctuation ranking failed for %s: %s", market.code, exc
-            )
+            logger.warning("Overseas fluctuation ranking failed for %s: %s", market.code, exc)
             fluct_rows = []
 
         if not fluct_rows:
@@ -250,9 +253,7 @@ class SmartVolatilityScanner:
                 limit=50,
             )
         except Exception as exc:
-            logger.warning(
-                "Overseas volume ranking failed for %s: %s", market.code, exc
-            )
+            logger.warning("Overseas volume ranking failed for %s: %s", market.code, exc)
             volume_rows = []
 
         for idx, row in enumerate(volume_rows):
@@ -433,16 +434,10 @@ def _extract_intraday_range_pct(row: dict[str, Any], price: float) -> float:
     if price <= 0:
         return 0.0
     high = _safe_float(
-        row.get("high")
-        or row.get("ovrs_hgpr")
-        or row.get("stck_hgpr")
-        or row.get("day_hgpr")
+        row.get("high") or row.get("ovrs_hgpr") or row.get("stck_hgpr") or row.get("day_hgpr")
     )
     low = _safe_float(
-        row.get("low")
-        or row.get("ovrs_lwpr")
-        or row.get("stck_lwpr")
-        or row.get("day_lwpr")
+        row.get("low") or row.get("ovrs_lwpr") or row.get("stck_lwpr") or row.get("day_lwpr")
     )
     if high <= 0 or low <= 0 or high < low:
         return 0.0

src/analysis/triple_barrier.py

@@ -6,10 +6,10 @@ Implements first-touch labeling with upper/lower/time barriers.
 from __future__ import annotations
 
 import warnings
+from collections.abc import Sequence
 from dataclasses import dataclass
 from datetime import datetime, timedelta
-from typing import Literal, Sequence
-
+from typing import Literal
 
 TieBreakMode = Literal["stop_first", "take_first"]
 
@@ -92,7 +92,10 @@ def label_with_triple_barrier(
     else:
         assert spec.max_holding_bars is not None
         warnings.warn(
-            "TripleBarrierSpec.max_holding_bars is deprecated; use max_holding_minutes with timestamps instead.",
+            (
+                "TripleBarrierSpec.max_holding_bars is deprecated; "
+                "use max_holding_minutes with timestamps instead."
+            ),
             DeprecationWarning,
             stacklevel=2,
         )

src/analysis/volatility.py

@@ -92,9 +92,7 @@ class VolatilityAnalyzer:
         recent_tr = true_ranges[-period:]
         return sum(recent_tr) / len(recent_tr)
 
-    def calculate_price_change(
-        self, current_price: float, past_price: float
-    ) -> float:
+    def calculate_price_change(self, current_price: float, past_price: float) -> float:
         """Calculate price change percentage.
 
         Args:
@@ -108,9 +106,7 @@ class VolatilityAnalyzer:
             return 0.0
         return ((current_price - past_price) / past_price) * 100
 
-    def calculate_volume_surge(
-        self, current_volume: float, avg_volume: float
-    ) -> float:
+    def calculate_volume_surge(self, current_volume: float, avg_volume: float) -> float:
         """Calculate volume surge ratio.
 
         Args:
@@ -240,11 +236,7 @@ class VolatilityAnalyzer:
             Momentum score (0-100)
         """
         # Weight recent changes more heavily
-        weighted_change = (
-            price_change_1m * 0.4 +
-            price_change_5m * 0.3 +
-            price_change_15m * 0.2
-        )
+        weighted_change = price_change_1m * 0.4 + price_change_5m * 0.3 + price_change_15m * 0.2
 
         # Volume contribution (normalized to 0-10 scale)
         volume_contribution = min(10.0, (volume_surge - 1.0) * 5.0)
@@ -301,17 +293,11 @@ class VolatilityAnalyzer:
 
         if len(close_prices) > 0:
             if len(close_prices) >= 1:
-                price_change_1m = self.calculate_price_change(
-                    current_price, close_prices[-1]
-                )
+                price_change_1m = self.calculate_price_change(current_price, close_prices[-1])
             if len(close_prices) >= 5:
-                price_change_5m = self.calculate_price_change(
-                    current_price, close_prices[-5]
-                )
+                price_change_5m = self.calculate_price_change(current_price, close_prices[-5])
             if len(close_prices) >= 15:
-                price_change_15m = self.calculate_price_change(
-                    current_price, close_prices[-15]
-                )
+                price_change_15m = self.calculate_price_change(current_price, close_prices[-15])
 
         # Calculate volume surge
         avg_volume = sum(volumes) / len(volumes) if volumes else current_volume