research price fix

sandbox/dexorder/ohlc_client.py

@@ -8,6 +8,7 @@ import logging
 from typing import Optional
 from .iceberg_client import IcebergClient
 from .history_client import HistoryClient
+from .symbol_metadata_client import SymbolMetadataClient
 
 log = logging.getLogger(__name__)
 
@@ -61,6 +62,12 @@ class OHLCClient:
             s3_secret_key=s3_secret_key,
             s3_region=s3_region,
         )
+        self.symbol_metadata = SymbolMetadataClient(
+            iceberg_catalog_uri, namespace,
+            s3_endpoint=s3_endpoint,
+            s3_access_key=s3_access_key,
+            s3_secret_key=s3_secret_key,
+        )
         self.history = HistoryClient(relay_endpoint, notification_endpoint)
         log.info("OHLCClient initialized")
 
@@ -125,7 +132,7 @@ class OHLCClient:
 
         if not missing_ranges:
             # All data exists in Iceberg
-            return df
+            return self._apply_decimal_correction(ticker, df)
 
         # Step 3: Request missing data for each range
         # For simplicity, request entire range (relay can merge adjacent requests)
@@ -144,6 +151,35 @@ class OHLCClient:
         # Step 5: Query Iceberg again for complete dataset
         df = self.iceberg.query_ohlc(ticker, period_seconds, start_time, end_time)
 
+        return self._apply_decimal_correction(ticker, df)
+
+    def _apply_decimal_correction(self, ticker: str, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        Convert raw integer OHLC columns to float prices/volumes.
+
+        Iceberg stores prices and volumes as integers (Nautilus internal units).
+        Divide by 10^price_precision for OHLC columns and 10^size_precision for
+        volume columns to recover actual floating-point values.
+        """
+        if df.empty:
+            return df
+
+        meta = self.symbol_metadata.get_metadata(ticker)
+        price_precision = meta.price_precision
+        size_precision = meta.size_precision
+
+        if price_precision is not None and price_precision > 0:
+            price_divisor = 10 ** price_precision
+            for col in ("open", "high", "low", "close"):
+                if col in df.columns:
+                    df[col] = df[col] / price_divisor
+
+        if size_precision is not None and size_precision > 0:
+            size_divisor = 10 ** size_precision
+            for col in ("volume", "buy_vol", "sell_vol"):
+                if col in df.columns:
+                    df[col] = df[col] / size_divisor
+
         return df
 
     async def __aenter__(self):