[Python] Trying to do project which suppouse to fetch Recent news on BTC and historical data...

Here's error i receive, what can it be. Any ideas?

INFO: root:Fetched Bitcoin news successfully! INFO:root:Average sentiment of the latest news: 0.38 INFO:root:Raw Response from CoinMarketCap: {'status': {'timestamp': '2025-02-03T16:46:55.598Z', 'error_code': 0, 'error_message': None, 'elapsed': 40, 'credit_count': 1, 'notice': None}, 'data': {'id': 1, 'name': 'Bitcoin', 'symbol': 'BTC', 'quotes': [{'time_open': '2025-01-05T00:00:00.000Z', 'time_close': '2025-01-05T23:59:59.999Z', 'time_high': '2025-01-05T23:03:00.000Z', 'time_low': '2025-01-05T14:21:00.000Z', 'quote': {'USD': {'open': 98233.90577658577, 'high': 98813.30855441149, 'low': 97291.76460634715, 'close': 98314.95944363723, 'volume': 20525254824.64, 'market_cap': 1947189546143.58, 'timestamp': '2025-01-05T23:59:59.999Z'}}}, ERROR:root:Invalid response structure or no historical data found. ERROR:root:Failed to fetch historical Bitcoin data. ERROR:root:Failed to predict Bitcoin price.
​

import os
import requests
import numpy as np
import matplotlib.pyplot as plt
import logging
from datetime import datetime, timedelta, timezone
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from dotenv import load_dotenv, dotenv_values

# Download the vader_lexicon
nltk.download('vader_lexicon')

# Load environment variables
load_dotenv(dotenv_path="MainKeys.env")
env_vars = dotenv_values(".env") # Loads environment variables as a dictionary
print(env_vars) # For debugging: should show your API keys

# Retrieve API keys
NEWS_API_KEY = os.getenv('NEWS_API_KEY')
COINMARKETCAP_API_KEY = os.getenv('COINMARKETCAP_API_KEY')

# Set up logging
logging.basicConfig(level=logging.INFO)

def get_bitcoin_news(api_key, page_size=5):
url = 'https://newsapi.org/v2/everything'
params = {'q': 'bitcoin', 'apiKey': api_key, 'language': 'en', 'sortBy': 'publishedAt', 'pageSize': page_size}
try:
response = requests.get(url, params=params)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
logging.error(f"Error fetching Bitcoin news: {e}")
return None

def get_historical_bitcoin_data(api_key, start_date, end_date):
url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/ohlcv/historical'
params = {'id': 1, 'convert': 'USD', 'time_start': start_date, 'time_end': end_date, 'interval': 'daily'}
headers = {'X-CMC_PRO_API_KEY': api_key, 'Accept': 'application/json'}
try:
response = requests.get(url, headers=headers, params=params)
response.raise_for_status()

# Debugging: print the raw API response to understand the structure
logging.info(f"Raw Response from CoinMarketCap: {response.json()}")

data = response.json()

# Check for the 'data' key and validate structure
if 'data' not in data or 'BTC' not in data['data'] or 'quotes' not in data['data']['BTC']:
logging.error("Invalid response structure or no historical data found.")
return None

return data['data']['BTC']['quotes']
except requests.exceptions.RequestException as e:
logging.error(f"Error fetching historical Bitcoin data: {e}")
return None

def analyze_sentiment(news_data):
sid = SentimentIntensityAnalyzer()
sentiments = []
for article in news_data.get('articles', []):
title = article.get('title', '')
description = article.get('description', '')
text = f"{title} {description}".strip()
sentiment_score = sid.polarity_scores(text)
sentiments.append(sentiment_score['compound']) # Only storing compound score for simplicity
return sentiments

def predict_price(api_key):
# Get the current date and calculate the start date for one month ago
end_date = datetime.now(timezone.utc)
start_date = end_date - timedelta(days=30)

# Format dates as YYYY-MM-DD strings
start_date_str = start_date.strftime('%Y-%m-%d')
end_date_str = end_date.strftime('%Y-%m-%d')

# Fetch the historical Bitcoin data for the last month
historical_data = get_historical_bitcoin_data(api_key, start_date_str, end_date_str)
if not historical_data:
logging.error("Failed to fetch historical Bitcoin data.")
return None

# Extract prices and dates
prices = [item.get('close', 0) for item in historical_data]
dates = [item.get('time_open', '').split('T')[0] for item in historical_data] # Extract only the date part
if len(prices) == 0 or len(dates) == 0:
logging.error("No historical price data available.")
return None

years = np.array([int(date.split('-')[0]) for date in dates]).reshape(-1, 1)

# Using Polynomial Regression
model = make_pipeline(PolynomialFeatures(2), LinearRegression())
model.fit(years, prices)

# Predict future Bitcoin price for 2025
future_year = np.array([[2025]])
predicted_price = model.predict(future_year)
plot_bitcoin_prices(years, prices, future_year, predicted_price)
return predicted_price[0]

def plot_bitcoin_prices(years, prices, future_year, predicted_price):
plt.figure(figsize=(8, 6))
plt.plot(years, prices, color='blue', label='Historical Prices')
plt.plot(future_year, predicted_price, 'ro', label=f'Predicted Price (2025): ${predicted_price[0]:.2f}')
plt.xlabel("Year")
plt.ylabel("Price (in USD)")
plt.title("Bitcoin Price Prediction")
plt.legend(loc="upper left")
plt.grid(True)
plt.show()

def main():
if not NEWS_API_KEY or not COINMARKETCAP_API_KEY:
logging.error("Missing API keys. Set them in the .env file.")
return

# Fetch Bitcoin news and analyze sentiment
news = get_bitcoin_news(NEWS_API_KEY)
if news and news.get('status') == 'ok':
logging.info("Fetched Bitcoin news successfully!")
sentiments = analyze_sentiment(news)
if sentiments:
avg_sentiment = np.mean(sentiments)
logging.info(f"Average sentiment of the latest news: {avg_sentiment:.2f}")
else:
logging.warning("No valid sentiment data available.")
else:
logging.error("Failed to fetch Bitcoin news")

# Predict Bitcoin price in 2025
predicted_price = predict_price(COINMARKETCAP_API_KEY)
if predicted_price is not None:
logging.info(f"Predicted Bitcoin Price in 2025: ${predicted_price:.2f}")
else:
logging.error("Failed to predict Bitcoin price.")

if __name__ == '__main__':
main()

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