Set label and line for volume data invisible

[maxko37]

Question

I am currently trying to set the line and label (marked in below image) for the volume data to not visible. But I am out of ideas how to do that.

The volume_config function does not provide any possibility to do that and calling pice_line just effects the candlestick chart but not the volume.

Are there any ideas?
Thanks

Code example

[air-dx]

chart.set(df_viz[['date','open','high','low','close']])

[maxko37]

Hi @air-dx,
thanks for your response! If I do as you suggest I loose the volume data completely, but I just want to hide the labe and shown line and keep the bars. Or did I understand your response wrong?

Best regards

[derbaumroman]

import pandas as pd
from lightweight_charts import Chart

Entry point of the program

if name == 'main':
# 1. INITIALIZATION AND CHART SETUP
# Create the main chart object
chart = Chart()

# 2. DATA LOADING
# Read the CSV file with OHLCV (Open, High, Low, Close, Volume) data
df = pd.read_csv('ohlcv.csv')

# 3. MAIN CHART DISPLAY CONFIGURATION
# Price scale configuration:
# - border_visible=True: show the border
# - border_color="#fff": white border color
# - scale_margin_top=0: no top margin
# - scale_margin_bottom=0.2: 20% bottom margin to create space for the histogram
chart.price_scale(border_visible=True, border_color="#fff",
                  scale_margin_top=0, scale_margin_bottom=0.2)

# Disable the last price line and its label
chart.price_line(line_visible=False, label_visible=False)

# Disable grid lines (both vertical and horizontal)
chart.grid(vert_enabled=False, horz_enabled=False)

# Disable the crosshair cursor
chart.crosshair(vert_visible=False, horz_visible=False)

# Disable the chart legend
chart.legend(visible=False)

# 4. DISPLAY CANDLESTICK DATA
# Load the candlestick data onto the chart
chart.set(df[['time', 'open', 'high', 'low', 'close']])

# 5. CREATE AND CONFIGURE VOLUME HISTOGRAM
# Create a histogram object on the main chart:
# - name='volume': name of the data series (must match column name later)
# - color="#ffffff": default white color (will be overridden by data colors)
# - price_line=False: do not show current value line
# - price_label=False: do not show value label
histogram = chart.create_histogram(
    name='volume',
    color="#ffffff",
    price_line=False,
    price_label=False
)

# Configure histogram positioning within the panel:
# - scale_margin_top=0.8: histogram starts at 80% height of the panel (bottom)
# - scale_margin_bottom=0: sticks to the bottom edge
# Combined with the main chart's scale_margin_bottom=0.2, this places
# the histogram in the bottom 20% of the chart area as an overlay
histogram.scale(scale_margin_top=0.8, scale_margin_bottom=0)

# 6. PREPARE VOLUME DATA WITH COLOR CODING
# Copy the time and volume columns from the original data
volume_data = df[['time', 'volume']].copy()

# CRITICAL: ADD COLOR COLUMN FOR EACH VOLUME BAR
# Create an empty list to store colors for each bar
colors = []

# Iterate through each row to determine the color based on price movement
for i in range(len(df)):
    # Compare closing price with opening price:
    # - If close >= open: bullish candle (price increased)
    # - If close < open: bearish candle (price decreased)
    if df['close'].iloc[i] >= df['open'].iloc[i]:
        # Bullish volumes: green color in RGBA format
        # rgba(0, 255, 0, 0.7) = green with 70% opacity
        colors.append('rgba(0, 255, 0, 0.7)')
    else:
        # Bearish volumes: red color in RGBA format
        # rgba(255, 0, 0, 0.7) = red with 70% opacity
        colors.append('rgba(255, 0, 0, 0.7)')

# Add the color column to the volume DataFrame
volume_data['color'] = colors

# 7. LOAD DATA INTO THE HISTOGRAM
# Pass the prepared data to the histogram
# Data now contains three columns: time, volume, color
histogram.set(volume_data)

# 8. DISPLAY THE CHART
# Show the chart in blocking mode
# Program will run until the chart window is closed
chart.show(block=True)

# ====================================================================
# KEY CONCEPTS FOR VOLUME HISTOGRAM WITH LIGHTWEIGHT-CHARTS:
# ====================================================================
# 1. Histograms are created on the main chart as an overlay, not in a 
#    separate subchart by default. This keeps the visualization compact.
#
# 2. The `scale_margin` parameters control positioning:
#    - Main chart's `scale_margin_bottom` reserves space at the bottom
#    - Histogram's `scale_margin_top` pushes the histogram down
#
# 3. Color coding requires adding a 'color' column to the DataFrame.
#    The default color in `create_histogram()` is overridden by this.
#
# 4. Alternative approach: Use `create_subchart()` for a truly separate
#    volume panel (better for adding multiple indicators below price).
#
# 5. Data must be in the correct format: 'time', 'volume' (matching the
#    series name), and optionally 'color' for individual bar colors.
# ====================================================================

# ALTERNATIVE COLOR SCHEMES (uncomment to use):
#
# 1. TradingView-style colors:
# bull_color = 'rgba(38, 166, 154, 0.7)'  # Bright teal for bullish
# bear_color = 'rgba(239, 83, 80, 0.7)'   # Bright red for bearish
#
# 2. Professional muted colors:
# bull_color = 'rgba(76, 175, 80, 0.7)'   # Muted green for bullish
# bear_color = 'rgba(244, 67, 54, 0.7)'   # Muted red for bearish
#
# 3. Gradient based on volume size (normalized to max volume):
# max_vol = df['volume'].max()
# for i in range(len(df)):
#     vol_ratio = df['volume'].iloc[i] / max_vol
#     if df['close'].iloc[i] >= df['open'].iloc[i]:
#         alpha = 0.3 + 0.5 * vol_ratio  # Opacity from 30% to 80%
#         colors.append(f'rgba(0, 255, 0, {alpha})')
#     else:
#         alpha = 0.3 + 0.5 * vol_ratio
#         colors.append(f'rgba(255, 0, 0, {alpha})')