curl -X GET "https://starknet.impulse.avnu.fi/v3/tokens/0x049d36570d4e46f48e99674bd3fcc84644ddd6b96f7c741b1562b82f9e004dc7/prices/line?startDate=2024-02-01T00:00:00Z&endDate=2024-02-14T00:00:00Z&resolution=1D"

// Fetch 30 days of daily price data
const tokenAddress = '0x049d36570d4e46f48e99674bd3fcc84644ddd6b96f7c741b1562b82f9e004dc7';
const endDate = new Date();
const startDate = new Date();
startDate.setDate(startDate.getDate() - 30);

const params = new URLSearchParams({
  startDate: startDate.toISOString(),
  endDate: endDate.toISOString(),
  resolution: '1D'
});

const response = await fetch(
  `https://starknet.impulse.avnu.fi/v3/tokens/${tokenAddress}/prices/line?${params}`
);

const data = await response.json();

// Calculate price statistics
const prices = data.map(d => d.value);
const avgPrice = prices.reduce((a, b) => a + b) / prices.length;
const maxPrice = Math.max(...prices);
const minPrice = Math.min(...prices);

console.log(`30-day stats: Avg: $${avgPrice}, High: $${maxPrice}, Low: $${minPrice}`);

import requests
from datetime import datetime, timedelta
import pandas as pd

token_address = '0x049d36570d4e46f48e99674bd3fcc84644ddd6b96f7c741b1562b82f9e004dc7'
end_date = datetime.now()
start_date = end_date - timedelta(days=30)

params = {
    'startDate': start_date.isoformat() + 'Z',
    'endDate': end_date.isoformat() + 'Z',
    'resolution': '1D'
}

response = requests.get(
    f'https://starknet.impulse.avnu.fi/v3/tokens/{token_address}/prices/line',
    params=params
)

data = response.json()

# Convert to DataFrame for analysis
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

# Calculate moving averages
df['MA7'] = df['value'].rolling(window=7).mean()
df['MA30'] = df['value'].rolling(window=30).mean()