prod deployment

sandbox/dexorder/__init__.py

@@ -1,5 +1,5 @@
 """
-DexOrder Trading Platform Python Client
+Dexorder Trading Platform Python Client
 
 Provides high-level APIs for:
 - Historical OHLC data retrieval with smart caching

sandbox/dexorder/api/__init__.py

@@ -1,5 +1,5 @@
 """
-DexOrder API - market data and charting for research and trading.
+Dexorder API - market data and charting for research and trading.
 
 For research scripts, import and use get_api() to access the API:
 

sandbox/dexorder/api/api.py

@@ -1,5 +1,5 @@
 """
-Main DexOrder API - provides access to market data and charting.
+Main Dexorder API - provides access to market data and charting.
 """
 
 import logging

sandbox/dexorder/api/charting_api.py

@@ -64,8 +64,10 @@ class ChartingAPI(ABC):
             )
 
             # Overlay moving average
+            # NOTE: mplfinance uses integer x-positions (0..N-1) internally,
+            # so overlays must use range(len(df)), not df.index.
             fig, ax = api.plot_ohlc(df)
-            ax.plot(df.index, df['sma_20'], label="SMA 20", color="blue")
+            ax.plot(range(len(df)), df['sma_20'], label="SMA 20", color="blue")
             ax.legend()
         """
         pass

sandbox/dexorder/impl/charting_api_impl.py

@@ -134,16 +134,11 @@ class ChartingAPIImpl(ChartingAPI):
         new_ax.sharex(existing_axes[0])
 
         # Plot the indicator data
+        # mplfinance uses integer x-positions (0..N-1) internally, so we must
+        # use range(len(df)) to align with the candlestick axes.
         for col in columns:
             if col in df.columns:
-                # Handle potential timestamp index (convert from microseconds)
-                if df.index.name == 'timestamp' or 'timestamp' in str(df.index.dtype):
-                    # Assume nanoseconds, convert to datetime
-                    plot_index = pd.to_datetime(df.index, unit='ns')
-                else:
-                    plot_index = df.index
-
-                new_ax.plot(plot_index, df[col], label=col, **kwargs)
+                new_ax.plot(range(len(df)), df[col], label=col, **kwargs)
 
         # Styling
         if ylabel: