Relax whale detection filters to capture more mid-size trades

- Price range: 0.20-0.80 → 0.10-0.90
- Absolute min trade size: $5K → $3K
- Dynamic threshold base: $10K → $5K, range $3K-$50K
- Resolution window: 6h-90d → 3h-180d
- Anomaly score threshold: 0.65 → 0.55

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/config/settings.py

@@ -51,8 +51,8 @@ class Settings(BaseSettings):
 
     # Whale Detection Settings
     min_trade_size_usd: float = Field(default=1000.0, alias="MIN_TRADE_SIZE_USD")
-    min_price: float = Field(default=0.2, alias="MIN_PRICE")
-    max_price: float = Field(default=0.8, alias="MAX_PRICE")
+    min_price: float = Field(default=0.10, alias="MIN_PRICE")
+    max_price: float = Field(default=0.90, alias="MAX_PRICE")
 
     # Monitoring Settings
     fetch_interval_seconds: int = Field(default=15, alias="FETCH_INTERVAL_SECONDS")

src/services/anomaly_detector.py

@@ -247,7 +247,7 @@ class AnomalyDetector:
         market: Optional[Market] = None,
         trader_history: Optional[TraderHistory] = None,
         market_id: str = "",
-        min_score: float = 0.65,
+        min_score: float = 0.55,
     ) -> Tuple[bool, float, dict]:
         """
         Decide whether a whale trade warrants LLM analysis.
@@ -275,7 +275,7 @@ class AnomalyDetector:
     def filter_whale_trades(
         self,
         trades: List[WhaleTrade],
-        min_score: float = 0.65,
+        min_score: float = 0.55,
     ) -> List[WhaleTrade]:
         """Filter whale trades by confidence score."""
         filtered = []

src/services/trade_monitor.py

@@ -653,26 +653,26 @@ class TradeMonitor:
                 end_dt = _dt.fromisoformat(market.end_date.replace("Z", "+00:00"))
                 now_dt = _dt.utcnow().replace(tzinfo=end_dt.tzinfo) if end_dt.tzinfo else _dt.utcnow()
                 hours_to_resolution = max(0, (end_dt - now_dt).total_seconds() / 3600)
-                if hours_to_resolution < 6:
+                if hours_to_resolution < 3:
                     return False  # too close, like DTE < 3
-                if hours_to_resolution > 90 * 24:
+                if hours_to_resolution > 180 * 24:
                     return False  # too far, like DTE > 60
             except (ValueError, TypeError):
                 pass  # unknown end date, don't reject
 
         # --- 4. Size (like premium min=$250K) ---
         # Base minimum: $5,000 (Polymarket scale vs options $250K)
-        if activity.usdc_size < 5_000:
+        if activity.usdc_size < 3_000:
             return False
 
         # --- 5. Dynamic size (like dynamic_premium = base × √(mcap / baseline)) ---
         # Larger markets require proportionally larger trades to be meaningful
-        base_size = 10_000.0
+        base_size = 5_000.0
         baseline_volume = 1_000_000.0
 
         if market and market.volume > 0:
             threshold = base_size * math.sqrt(market.volume / baseline_volume)
-            threshold = max(5_000.0, min(threshold, 100_000.0))  # floor $5K, cap $100K
+            threshold = max(3_000.0, min(threshold, 50_000.0))  # floor $3K, cap $50K
         else:
             threshold = base_size