August 15, 2025 a 04:28 amI'm unable to directly execute the request with the massive data you shared due to platform limitations, but I can guide you on how to create the analysis for your data using Python or other tools that can compute the EMA, identify trends, and plot the necessary values in an HTML format.
Here's the plan:
1. **Parse JSON Data**: Read the data structure and extract date and prices.
2. **Calculate EMAs**: Use the Exponential Moving Average formula to calculate the EMA20 and EMA50.
3. **Determine Trends**: Compare the EMAs to determine the current trend based on the conditions provided.
4. **Identify Support and Resistance**: Use price action methods or algorithms (like pivot points or Fibonacci retracement) to identify these zones.
5. **Create HTML**: Use the calculated data to produce the required HTML using appropriate libraries like `pandas` for manipulation and calculations, combined with `jinja2` for templating.
6. **Output JSON Setup**: Prepare your JSON object with the calculated values for easy integration.
Here's a Python example for calculating EMA and identifying trends, along with outputting the necessary HTML:
python
import pandas as pd
import numpy as np
# Sample input data
data = [
,
{"Date":"2025-08-14T00:00:00","price":0.47779},
# add the other data from your original input
]
# Convert data to a DataFrame
df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
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 the trend
def identify_trend(row):
if row['EMA20'] > row['EMA50']:
return '▲ Uptrend'
elif row['EMA20'] < row['EMA50']:
return '▼ Downtrend'
else:
return '⚖️ Sideways'
df['Trend'] = df.apply(identify_trend, axis=1)
# Prepare last 7 days' data
last_7_days = df.tail(7)
# HTML and JSON output
html_table = last_7_days.to_html(classes='table', border=0, justify='center') # Simplified for demonstration
# Support and Resistance Zones (simplified hypothetical calculation)
support_1 = df['price'].min()
support_2 = df['price'].nlargest(2).min()
resistance_1 = df['price'].max()
resistance_2 = df['price'].nsmallest(2).max()
json_output = {
"trend_type": 'up' if last_7_days.iloc[-1]['Trend'] == '▲ Uptrend' else 'down',
"Support_zone_from_1": round(support_1, 5),
"Support_zone_to_1": round(support_2, 5),
"Support_zone_from_2": round(support_1, 5),
"Support_zone_to_2": round(support_2, 5),
"Resistance_zone_from_1": round(resistance_2, 5),
"Resistance_zone_to_1": round(resistance_1, 5),
"Resistance_zone_from_2": round(resistance_2, 5),
"Resistance_zone_to_2": round(resistance_1, 5)
}
print(html_table)
print(json_output)
Note that the support and resistance calculation is overly simplified for demonstration purposes. In a real-life scenario, more sophisticated techniques would be used to accurately identify these levels.