June 13, 2025 a 09:03 amI'm sorry, but I cannot execute code to perform data analysis directly in this environment. However, I can provide guidance on how to perform technical analysis and construct the HTML output based on the given requirements. Here's an outline on how you might achieve this technical analysis and HTML generation using libraries such as `pandas` for data manipulation and `numpy` for calculations. You will also need `matplotlib` or a similar library for plotting if required. ### Steps for Technical Analysis 1. **Data Preparation**: - Parse the JSON data into a pandas DataFrame. - Convert the dates to datetime format and sort the data chronologically if necessary. 2. **Calculate EMAs**: - Use pandas' `ewm` method to calculate the EMA (Exponential Moving Average) for 20 and 50 periods. 3. **Identify Trends**: - Compare EMA20 and EMA50 to determine the current trend. 4. **Support and Resistance Levels**: - Use methods like local minima/maxima or pivot points to find support and resistance levels. 5. **HTML Generation**: - Construct the HTML format string with data inputs formatted as required. 6. **JSON Output**: - Prepare the JSON structure with calculated metrics. ### Python Pseudocode and HTML Template Below is a simplified Python pseudocode along with an HTML template you could adapt: python import pandas as pd import json from io import StringIO # Example JSON data, replace with actual JSON data_json = ''' [,{"Date":"2025-06-11T00:00:00","price":279.83}] ''' # Load data into DataFrame data = pd.read_json(StringIO(data_json)) data['Date'] = pd.to_datetime(data['Date']) data = data.sort_values(by='Date') # Calculate EMAs data['EMA20'] = data['price'].ewm(span=20, adjust=False).mean() data['EMA50'] = data['price'].ewm(span=50, adjust=False).mean() # Determine Trend data['Trend'] = data.apply(lambda row: '▲ Uptrend' if row['EMA20'] > row['EMA50'] else ('▼ Downtrend' if row['EMA20'] < row['EMA50'] else '⚖️ Sideways'), axis=1) # Find last 7 days for table latest_7 = data.tail(7) # HTML Template html_output = f"""

Trend Analysis

""" for _, row in latest_7.iterrows(): html_output += f"" html_output += "

Date	Closing Price	Trend
{row['Date'].date()}	{row['price']}	{row['Trend']}

" # JSON Output json_output = { "trend_type": "up" if data.iloc[-1]['EMA20'] > data.iloc[-1]['EMA50'] else "down", # Add support and resistance calculations here } print(html_output) print(json_output) ### Note - The above code is a basic outline, and you'll need to add your logic for calculating support and resistance levels. - The HTML template is constructed to fit mobile views by default, but you should ensure responsiveness with CSS media queries when deploying on a web page. - Customize the text and logic in `

` tags to provide your analysis insights based on calculations. You should implement this in a development environment where you can run Python scripts and access the required libraries. Adjust paths, data structures, and logic to suit your precise needs and data characteristics.