Volume Profile

Introduction#

Volume Profile is a trading indicator that displays trading activity over a specified time period at specified price levels. It provides a graphical representation of the amount of volume traded at specific price levels. Volume Profile helps traders understand where the most trading activity has occurred, offering insights into support and resistance levels.

Key Concepts#

• Value Area: The range of prices where a specified percentage (commonly 70%) of the total volume for the selected period occurred. This area often represents significant levels of support and resistance.
• Point of Control (POC): The price level with the highest traded volume during a specified time period. The POC indicates the price level at which traders are most willing to transact.
• High Volatility Nodes (HVN): Price levels where significant volume has occurred, typically indicating strong support or resistance.
• Low Volatility Nodes (LVN): Price levels with relatively low volume, often indicating weak support or resistance.

Calculation of Volume Profile#

While the specific calculations vary depending on the software used, the basic idea involves aggregating volume data at different price levels over a specified time period.

Example Calculation#

For example, let’s say we have the following data for a stock over a week:

Steps to Create a Volume Profile#

1. Aggregate Volume by Price:

   • Group volume data based on price levels. In this example, data is already grouped.

2. Identify the POC:

   • The price level with the highest volume:
   • In this case, the POC is at $14 with 3000 shares traded.

3. Determine the Value Area:

   • Calculate the total volume (in this example, it’s 1,000 + 2,000 + 1,500 + 500 + 3,000 + 2,500 = 10,500).
   • To find the Value Area (70% of total volume):
   • 0.70 * 10,500 = 7,350.
   • Determine the price range that encompasses 7,350 of the volume. In this case, the Value Area would be between $11 and $14.

Python Code#

import pandas as pd
import numpy as np

def calculate_volume_profile(data, bin_size=1.0):
    """Calculate the Volume Profile."""
    # Create a new DataFrame to hold the volume profile
    volume_profile = pd.DataFrame()

    # Create bins for price levels
    price_bins = np.arange(data['Low'].min(), data['High'].max() + bin_size, bin_size)
    
    # Assign each price to its corresponding bin
    data['Price Bin'] = pd.cut(data['Close'], bins=price_bins)

    # Group by price bins and sum volume
    volume_profile = data.groupby('Price Bin')['Volume'].sum().reset_index()
    
    # Calculate the midpoint of each bin for plotting
    volume_profile['Mid Price'] = volume_profile['Price Bin'].apply(lambda x: (x.left + x.right) / 2)

    return volume_profile

Interpretation of Volume Profile#

1. Support and Resistance:

   • Areas with high volume (HVN) are often interpreted as strong support or resistance levels.
   • For example, a volume peak at $14 might suggest that buyers and sellers are predominantly active around that price.

2. Trend Analysis:

   • Rising volume at increasing prices can validate an uptrend, while declining volumes at increasing prices can suggest a weakening trend.

3. Breakout and Reversal Signals:

   • A breakout above significant volume levels can signal new trends, while failure to hold above these levels may indicate reversals.

Analogy#

Think of Volume Profile as a crowded marketplace. The various prices represent different stalls, while the volume indicates the number of customers at each stall. A stall with a lot of customers (high volume) indicates a popular product (strong support/resistance), while a stall with only a few customers (low volume) might signify a product that isn’t attracting attention. Just as a trader might choose to sell a product more actively at a busy stall, market participants are likely to engage more at price levels with higher volume.

Conclusion#

Volume Profile is a crucial tool for traders to visualize price levels where significant trading activity has occurred. By utilizing Volume Profile, traders can gain insights into market behavior, identify potential support and resistance levels, and improve their trading strategies.

References#

• Dalton, J. (2007). Market Profile: Tools for Fine Tuning Your Trading.
• Investopedia: Volume Profile
• TradingView: Volume Profile