fixed lint errors

app/main.py

@@ -1,5 +1,5 @@
 """FastAPI application entry point with lifespan management."""
-
+# ruff: noqa: E402
 # ---------------------------------------------------------------------------
 # SSL + proxy injection — MUST happen before any HTTP client imports
 # ---------------------------------------------------------------------------

app/providers/gemini_sentiment.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 import json
 import logging
 import os
-import ssl
 from datetime import datetime, timezone
 from pathlib import Path
 

app/scheduler.py

@@ -16,7 +16,7 @@ from __future__ import annotations
 import json
 import logging
 import asyncio
-from datetime import date, datetime, timedelta, timezone
+from datetime import date, datetime, timezone
 
 from apscheduler.schedulers.asyncio import AsyncIOScheduler
 from sqlalchemy import case, func, select

app/services/indicator_service.py

@@ -74,8 +74,8 @@ def compute_adx(
     plus_dm: list[float] = []
     minus_dm: list[float] = []
     for i in range(1, n):
-        h, l, pc = highs[i], lows[i], closes[i - 1]
+        h, low_val, pc = highs[i], lows[i], closes[i - 1]
-        tr_list.append(max(h - l, abs(h - pc), abs(l - pc)))
+        tr_list.append(max(h - low_val, abs(h - pc), abs(low_val - pc)))
         up = highs[i] - highs[i - 1]
         down = lows[i - 1] - lows[i]
         plus_dm.append(up if up > down and up > 0 else 0.0)
@@ -208,8 +208,8 @@ def compute_atr(
 
     tr_list: list[float] = []
     for i in range(1, n):
-        h, l, pc = highs[i], lows[i], closes[i - 1]
+        h, low_val, pc = highs[i], lows[i], closes[i - 1]
-        tr_list.append(max(h - l, abs(h - pc), abs(l - pc)))
+        tr_list.append(max(h - low_val, abs(h - pc), abs(low_val - pc)))
 
     # Wilder smoothing
     atr = sum(tr_list[:period]) / period

app/services/sr_service.py

@@ -237,7 +237,7 @@ def cluster_sr_zones(
 
     for level in sorted_levels[1:]:
         # Compute current cluster midpoint
-        prices = [l["price_level"] for l in current_cluster]
+        prices = [lvl["price_level"] for lvl in current_cluster]
         cluster_low = min(prices)
         cluster_high = max(prices)
         cluster_mid = (cluster_low + cluster_high) / 2.0
@@ -259,11 +259,11 @@ def cluster_sr_zones(
     # 3. Compute zone for each cluster
     zones: list[dict] = []
     for cluster in clusters:
-        prices = [l["price_level"] for l in cluster]
+        prices = [lvl["price_level"] for lvl in cluster]
         low = min(prices)
         high = max(prices)
         midpoint = (low + high) / 2.0
-        strength = min(100, sum(l["strength"] for l in cluster))
+        strength = min(100, sum(lvl["strength"] for lvl in cluster))
         level_count = len(cluster)
 
         # 4. Tag zone type