OPEN-SOURCE SCRIPT

VOLD Delta & VOLD Ratio

Description:
This script provides a comprehensive analysis of market sentiment by measuring the VOLD Delta and VOLD Ratio for both the NYSE and NASDAQ. It includes:

VOLD Delta Calculation: Measures the net volume difference between up volume (UVOL) and down volume (DVOL), normalized to millions. This helps identify significant buying or selling pressure in the market.

VOLD Ratio Calculation: Calculates the ratio of up volume to down volume, offering insights into the balance between buying and selling activity.

Combination of NYSE and NASDAQ Data: Utilizes UVOL and DVOL for NYSE, and UVOLQ and DVOLQ for NASDAQ to provide a more comprehensive view of market sentiment.

User Selection: Toggle between displaying VOLD Delta or VOLD Ratio based on your preference.

Dynamic Coloring: Colors the histogram bars green for positive values and red for negative values, making it easy to visualize market sentiment at a glance.

Implications for Understanding Market Sentiment:
Identifying Buying Pressure: A significant positive VOLD Delta indicates strong buying pressure, suggesting that the market sentiment is bullish. The corresponding green histogram bars make it easy to identify these periods.

Identifying Selling Pressure: A significant negative VOLD Delta indicates strong selling pressure, suggesting that the market sentiment is bearish. The corresponding red histogram bars highlight these periods.

Balance Between Buyers and Sellers: The VOLD Ratio helps identify the balance between buyers and sellers. A ratio greater than 3 indicates significant buying, while a ratio less than 0.5 indicates significant selling. Ratios close to 1 suggest a balanced market.

Real-Time Market Insights: By combining the VOLD Delta and VOLD Ratio, traders can gain real-time insights into market sentiment and make more informed trading decisions based on the volume activity on the NYSE and NASDAQ.

This script is a powerful tool for traders looking to stay informed about intraday market sentiment and make data-driven trading decisions.

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