Create data_fetcher.py

data_fetcher.py

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+import requests
+import pandas as pd
+
+def fetch_crypto_data(symbol):
+    """Fetch crypto market data from Binance."""
+    url = f"https://api.binance.com/api/v3/klines"
+    params = {"symbol": symbol, "interval": "1h", "limit": 100}
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        data = response.json()
+        df = pd.DataFrame(data, columns=["timestamp", "open", "high", "low", "close", "volume"])
+        df["close"] = df["close"].astype(float)
+        return df.dropna()
+    else:
+        raise Exception("Error fetching crypto data.")
+
+def fetch_stock_data(symbol):
+    """Fetch stock market data from Alpha Vantage."""
+    url = f"https://www.alphavantage.co/query"
+    params = {"function": "TIME_SERIES_INTRADAY", "symbol": symbol, "interval": "60min", 
+              "apikey": ALPHA_VANTAGE_API_KEY}
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        data = response.json()["Time Series (60min)"]
+        df = pd.DataFrame(data).T.astype(float).reset_index()
+        df.columns = ["timestamp", "open", "high", "low", "close", "volume"]
+        return df.dropna()
+    else:
+        raise Exception("Error fetching stock data.")
+
+def fetch_sentiment_data(keyword):
+    """Analyze sentiment from social media."""
+    tweets = [
+        f"{keyword} is going to moon!",
+        f"I hate {keyword}, it's trash!",
+        f"{keyword} is amazing!"
+    ]
+    
+    sentiments = [TextBlob(tweet).sentiment.polarity for tweet in tweets]
+    return sum(sentiments) / len(sentiments)