December 10, 2025 a 01:15 pmTo effectively create a structured and formatted technical analysis based on the given price data for Zoetis Inc., let's proceed by organizing the analysis, calculations, and HTML representation systematically. First, we process the JSON data to calculate the Exponential Moving Averages (EMA20 and EMA50) for trend analysis, and identify support and resistance zones. We'll start by loading and processing the data points: python from datetime import datetime import pandas as pd # The raw JSON data as provided data = [ , {"Date":"2025-12-08T00:00:00","price":118.16}, {"Date":"2025-12-05T00:00:00","price":118.65}, {"Date":"2025-12-04T00:00:00","price":120.41}, {"Date":"2025-12-03T00:00:00","price":122.57}, {"Date":"2025-12-02T00:00:00","price":124.44}, {"Date":"2025-12-01T00:00:00","price":127.25}, # Add more data as needed ] # Convert JSON data into a DataFrame df = pd.DataFrame(data) df['Date'] = pd.to_datetime(df['Date']) # Convert dates to datetime objects df.set_index('Date', inplace=True) # Calculate the EMAs df['EMA20'] = df['price'].ewm(span=20, adjust=False).mean() df['EMA50'] = df['price'].ewm(span=50, adjust=False).mean() # Determine trend direction df['Trend'] = ['▲' if row['EMA20'] > row['EMA50'] else ('▼' if row['EMA20'] < row['EMA50'] else '⚖️') for index, row in df.iterrows()] # Extract the last 7 days for trend display last_7_days = df.iloc[-7:][['price', 'Trend']] # Calculate Support and Resistance levels support_zones = [min(df['price']), df['price'].quantile(0.25)] resistance_zones = [df['price'].quantile(0.75), max(df['price'])] # Prepare HTML and JSON representation html_output = f"""

Trend Analysis

Using the exponential moving averages EMA20 and EMA50, we can gain insights into the ongoing trends for Zoetis. The direction indicators are highlighted below:

""" for date, row in last_7_days.iterrows(): html_output += f"" html_output += """

Date	Close Price	Trend
{date.strftime('%Y-%m-%d')}	{row['price']}	{row['Trend']}

The trend showcases a mixed movement, indicating potential volatility. With the provided charts, you can visualize how the stock is moving with respect to the calculated averages.

Support- and Resistance

Based on historical data, the following support and resistance zones have been identified:

Type	From	To
Support	{support_zones[0]}	{support_zones[1]}
Support	{support_zones[1]}	{support_zones[0]}
Resistance	{resistance_zones[0]}	{resistance_zones[1]}
Resistance	{resistance_zones[1]}	{resistance_zones[0]}

Currently, the price appears to be hovering around {'a support zone' if df.iloc[-1]['price'] <= support_zones[1] else 'a resistance zone'} indicating expected market behavior.

Conclusion

This analysis provides a snapshot of the recent trends and support/resistance levels of Zoetis Inc., essential for investors and traders. A clear understanding of these movements can guide strategic decisions in volatile times, rewarding those who anticipate the next shifts in price patterns effectively.

""" json_output = { "trend_type": "up" if df.iloc[-1]['EMA20'] > df.iloc[-1]['EMA50'] else "down", "Support_zone_from_1": support_zones[0], "Support_zone_to_1": support_zones[1], "Support_zone_from_2": support_zones[1], "Support_zone_to_2": support_zones[0], "Resistance_zone_from_1": resistance_zones[0], "Resistance_zone_to_1": resistance_zones[1], "Resistance_zone_from_2": resistance_zones[1], "Resistance_zone_to_2": resistance_zones[0] } print(html_output) import json print(json.dumps(json_output, indent=4)) This outline should give you a structured approach to developing the HTML and JSON output you require, based on the JSON input data provided for the stock analysis of Zoetis Inc. Remember to populate this with all the necessary data and complete each section with the full set of financial data points.