Make current price work with date offsets

WebServer/AIPython/aipredictor.py

@@ -17,8 +17,6 @@ def Predict():
     # get the number of days ago to run the simulation for
     DaysBack = int(sys.argv[2])
 
-    print(f"Days back: {DaysBack}")
-
     # calculate the time offsets
     end_date = datetime.now() - timedelta(days=DaysBack)
     start_date = end_date - timedelta(days=70)
@@ -26,9 +24,6 @@ def Predict():
     # convert to string formats
     start_str = start_date.strftime('%Y-%m-%d')
     end_str = end_date.strftime('%Y-%m-%d')
-    
-    print(f"Start Date: {start_str}")
-    print(f"End Date: {end_str}")
 
     # Define paths (consistent with your previous script)
     SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))

WebServer/AIPython/currentprice.py

@@ -1,3 +1,4 @@
+from datetime import datetime, timedelta
 import sys
 import yfinance as yf
 
@@ -6,8 +7,21 @@ def getCurrentPrice():
     # Get the Symbol from ARGV
     symbol = sys.argv[1]
 
+    # get the number of days ago to run the simulation for
+    DaysBack = int(sys.argv[2])
+
     ticker = yf.Ticker(symbol)
-    data = ticker.history(period="1d", interval="1m")
+    
+    # Calculate the target date
+    target_date = datetime.now() - timedelta(days=DaysBack)
+    start_str = target_date.strftime('%Y-%m-%d')
+    
+    # End date must be the day AFTER the target to get that day's data
+    end_date = target_date + timedelta(days=1)
+    end_str = end_date.strftime('%Y-%m-%d')
+
+    # Fetch data for that specific 24-hour window
+    data = ticker.history(start=start_str, end=end_str)
     current_price = data['Close'].iloc[-1]
 
     # Return to C# via stdout