Create plotter.py

plotter.py

@@ -0,0 +1,98 @@
+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.
+    Implements the exact layout: Candlestick + Volume + MACD + Stochastic.
+    """
+    if df.empty:
+        return None
+
+    # Define style - Yahoo style as requested
+    mc = mpf.make_marketcolors(
+        up='#00ff00', down='#ff0000',  # Green/Red candles
+        wick='black',
+        edge='black',
+        volume='#00bfff',
+        inherit=True
+    )
+
+    s = mpf.make_mpf_style(
+        base_mpf_style='yahoo',
+        marketcolors=mc,
+        facecolor='white',
+        edgecolor='black',
+        gridcolor='lightgray',
+        gridstyle='-',
+        figcolor='white',
+        rc={'axes.labelcolor': 'black',
+            'xtick.color': 'black',
+            'ytick.color': 'black',
+            'figure.titlesize': 16,
+            'axes.titlesize': 14,
+            'axes.titleweight': 'bold'}
+    )
+
+    # Define panels: [Candlestick, Volume, MACD, Stochastic]
+    apds = []
+    
+    # MACD Panel (Panel 2 - index 1 for addplot)
+    # MACD Line
+    apds.append(
+        mpf.make_addplot(df['MACD'], color='#606060', panel=2, ylabel='MACD', secondary_y=False)
+    )
+    # Signal Line
+    apds.append(
+        mpf.make_addplot(df['MACD_signal'], color='#1f77b4', panel=2, secondary_y=False)
+    )
+    # Positive Histogram Bars
+    apds.append(
+        mpf.make_addplot(df['MACD_bar_positive'], type='bar', color='#4dc790', panel=2, width=0.8)
+    )
+    # Negative Histogram Bars
+    apds.append(
+        mpf.make_addplot(df['MACD_bar_negative'], type='bar', color='#fd6b6c', panel=2, width=0.8)
+    )
+    
+    # Stochastic Panel (Panel 3 - index 2 for addplot)
+    apds.append(
+        mpf.make_addplot(df[['%D', '%SD', 'UL', 'DL']], panel=3, ylabel='Stoch (14,3)', ylim=[0, 100])
+    )
+
+    # Prediction overlay on main chart
+    if predictions is not None and predictions.any():
+        last_date = df.index[-1]
+        future_index = pd.date_range(start=last_date, periods=len(predictions) + 1, freq=df.index.freq or 'D')[1:]
+        future_series = pd.Series(predictions, index=future_index)
+        
+        apds.append(
+            mpf.make_addplot(future_series, color='blue', linestyle='-.', width=2, marker='o', markersize=4)
+        )
+
+    # Plotting
+    fig, axes = mpf.plot(
+        df,
+        type='candle',
+        style=s,
+        title=f'{ticker} Price Chart and Analysis',
+        ylabel='Price (USD)',
+        volume=True,
+        addplot=apds,
+        mav=(5, 20),  # Moving averages as requested
+        figratio=(16, 9),
+        figscale=1.5,
+        panel_ratios=(3, 1, 3, 3),  # Ratio as requested
+        returnfig=True
+    )
+    
+    # Convert to Base64
+    buf = BytesIO()
+    fig.savefig(buf, format='png', bbox_inches='tight', dpi=100)
+    plt.close(fig)
+    image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+    
+    return f'<img src="data:image/png;base64,{image_base64}" style="width: 100%; height: auto;">'