adding metasupertrend data WIP

utils/converty_csv_to_day.py

@@ -0,0 +1,48 @@
+import pandas as pd
+from datetime import datetime
+
+def aggregate_to_daily(input_csv, output_csv):
+    """
+    Load a CSV file with Bitcoin price data, aggregate to daily values, and save to a new CSV.
+    
+    Args:
+        input_csv (str): Path to the input CSV file
+        output_csv (str): Path to save the output CSV file
+    """
+    try:
+        # Read the CSV file
+        print(f"Loading data from {input_csv}...")
+        df = pd.read_csv(input_csv)
+        
+        # Convert timestamp to datetime
+        df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
+        df.set_index('Timestamp', inplace=True)
+        
+        # Aggregate to daily values
+        print("Aggregating data to daily values...")
+        daily_df = df.resample('D').agg({
+            'Open': 'first',
+            'High': 'max',
+            'Low': 'min',
+            'Close': 'last',
+            'Volume': 'sum'
+        })
+        
+        # Reset index to make Timestamp a column
+        daily_df.reset_index(inplace=True)
+        
+        # Save to new CSV
+        print(f"Saving daily data to {output_csv}...")
+        daily_df.to_csv(output_csv, index=False)
+        
+        print(f"Successfully processed {len(df)} records into {len(daily_df)} daily records")
+        
+    except Exception as e:
+        print(f"Error processing data: {str(e)}")
+
+if __name__ == "__main__":
+    # Example usage
+    input_file = "../data/btcusd_1-min_data.csv"  # Update this path to your input file
+    output_file = "../data/btcusd_daily_data.csv"  # Update this path to your desired output file
+    
+    aggregate_to_daily(input_file, output_file)