Create plotter.py

plotter.py

@@ -0,0 +1,100 @@
+import pandas as pd
+import mplfinance as mpf
+from io import BytesIO
+import base64
+import matplotlib.pyplot as plt
+
+def create_mplfinance_chart(df, ticker, predictions=None):
+    """
+    Creates a custom mplfinance candlestick chart and returns it as a base64 encoded image.
+    Style: white, black, with accent #f0f4f9.
+    """
+    if df.empty:
+        return None
+
+    # --- 1. Define Custom Style ---
+    mc = mpf.make_marketcolors(
+        up='green', down='red',  # Candlestick body colors (yfinance default)
+        edge='inherit',
+        wick='black',
+        volume='#f0f4f9', # Accent color for volume
+        inherit=True
+    )
+
+    s = mpf.make_mpf_style(
+        base_mpf_style='nightclouds', # Start with a dark base
+        marketcolors=mc,
+        # Customize background and grid to achieve white/black contrast
+        facecolor='#f0f4f9', # Background accent color (very light gray/off-white)
+        edgecolor='black',
+        gridcolor='gray',
+        gridstyle=':',
+        figcolor='white',
+        rc={'axes.labelcolor': 'black',
+            'xtick.color': 'black',
+            'ytick.color': 'black',
+            'figure.titlesize': 14,
+            'axes.titlesize': 16}
+    )
+
+    # --- 2. Define Technical Indicators (Apanels) ---
+    apds = []
+    # MACD Subplot
+    apds.append(
+        mpf.make_addplot(df['MACD'], color='black', panel=2, title='MACD'),
+    )
+    apds.append(
+        mpf.make_addplot(df['MACD_signal'], color='#FFA500', panel=2),
+    )
+    
+    # RSI Subplot
+    apds.append(
+        mpf.make_addplot(df['RSI'], color='red', panel=3, title='RSI', y_on_right=True),
+    )
+
+    # Overlays on Main Chart (MAs and BB)
+    apds.extend([
+        mpf.make_addplot(df['SMA_20'], color='#FFD700', linestyle='-', width=1.5),
+        mpf.make_addplot(df['SMA_50'], color='#FFA500', linestyle='-', width=1.5),
+        mpf.make_addplot(df['BB_upper'], color='grey', linestyle='--', width=0.5),
+        mpf.make_addplot(df['BB_lower'], color='grey', linestyle='--', width=0.5),
+    ])
+
+    # --- 3. Prediction Plotting (Trace as Overlay) ---
+    if predictions is not None and predictions.any():
+        
+        # Calculate future index (assuming daily frequency for simplicity)
+        last_date = df.index[-1]
+        future_index = pd.date_range(start=last_date, periods=len(predictions) + 1, freq=df.index.freq or 'D')[1:]
+        
+        # Create a series to align with the main DataFrame index for plotting
+        future_series = pd.Series(predictions, index=future_index)
+        
+        # Create an 'scatter' addplot for the prediction line
+        apds.append(
+            mpf.make_addplot(future_series, color='blue', linestyle='-.', width=2, marker='o', markersize=5)
+        )
+
+    # --- 4. Plotting ---
+    fig, axes = mpf.plot(
+        df,
+        type='candle',
+        style=s,
+        title=f'{ticker} Price Chart and Analysis',
+        ylabel='Price (USD)',
+        volume=True,
+        volume_panel=1,
+        addplot=apds,
+        figratio=(16,9),
+        figscale=1.5,
+        returnfig=True
+    )
+    
+    # --- 5. Convert to Base64 (for Gradio) ---
+    buf = BytesIO()
+    fig.savefig(buf, format='png', bbox_inches='tight')
+    plt.close(fig) # Close the figure to free memory
+    image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+    
+    # Gradio expects an HTML tag for raw HTML image display
+    return f'<img src="data:image/png;base64,{image_base64}" style="width: 100%; height: auto;">'