Financial Chart Customization Script

src/mplfinance/financial_chart.py

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+import mplfinance as mpf
+import pandas as pd
+
+# Sample financial data
+data = {
+    'Date': ['2023-10-01', '2023-10-02', '2023-10-03'],
+    'Open': [100, 105, 102],
+    'High': [110, 115, 112],
+    'Low': [95, 100, 98],
+    'Close': [105, 110, 105],
+    'Volume': [100000, 120000, 110000],
+    'your_custom_indicator': [50, 45, 48]  # Replace with your actual data
+}
+
+# Create a DataFrame from the sample data
+df = pd.DataFrame(data)
+
+# Customizing candlestick colors and plotting
+def customize_candlestick_color(index, open, close):
+    if index % 2 == 0:
+        return 'white' if open < close else 'lightgray'
+    else:
+        return 'lightgray' if open < close else 'white'
+
+def custom_plot(data, ax):
+    candle_colors = [customize_candlestick_color(i, row['Open'], row['Close']) for i, row in
+                     enumerate(data.itertuples())]
+    apds = []
+
+    apds.append(mpf.make_addplot(data['your_custom_indicator'], color='blue', ax=ax, secondary_y=False))
+    apds.append(mpf.make_addplot(mpf.make_candlestick(data, colorup='k', colordown='r', alpha=1), ax=ax, panel=0,
+                                 scatter=False))
+
+    if 'Volume' in data:
+        apds.append(mpf.make_addplot(data['Volume'], panel=1, color='b', secondary_y=True))
+
+    mpf.plot(data, type='none', addplot=apds, style='default', title='Your Title', figscale=1.25)
+
+# Plot the financial chart with alternating shading and customized candlestick colors
+custom_plot(df)
+
+'''This Python script leverages the mplfinance library to create visually appealing financial 
+charts with customizable candlestick colors and optional volume data. 
+The script begins with the import of essential libraries, including mplfinance and pandas. 
+Users can define or load financial data, either as sample data for demonstration or by replacing it 
+with actual financial data. Customization features include alternating candlestick colors based 
+on open and close prices, making it easier to visualize market trends. Additionally, the script 
+accommodates the plotting of custom indicators or additional data, such as trading volume. 
+Detailed comments and clear instructions are provided for ease of use and adaptation to specific 
+financial datasets. This code can be hosted on GitHub, enhancing its accessibility and usability 
+for a broader audience of financial data analysts and visualization enthusiasts.'''