Create a simulation to test 1 month of data in 30 mins

WebServer/AIPython/aipredictor.py

@@ -1,3 +1,4 @@
+from datetime import datetime, timedelta
 import os
 import sys
 import joblib
@@ -13,13 +14,29 @@ def Predict():
     # 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])
+
+    print(f"Days back: {DaysBack}")
+
+    # calculate the time offsets
+    end_date = datetime.now() - timedelta(days=DaysBack)
+    start_date = end_date - timedelta(days=70)
+
+    # 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__))
     DATA_DIR = os.path.join(SCRIPT_DIR, "data")
     MODEL_PATH = os.path.join(DATA_DIR, "model.keras")
 
     # Pull 1 month of current data to make prediction against | for volatility 20
-    df = yf.download(Symbol, period="2mo", auto_adjust=True)
+    df = yf.download(Symbol, start=start_str, end=end_str, auto_adjust=True, progress=False)
     if not df.empty:
         # Remove the horizontal ticker column
         df.columns = df.columns.get_level_values(0)

WebServer/AIPython/features.py

@@ -78,10 +78,11 @@ def MakeFeatures(df):
             df.drop(col, axis=1, inplace=True)
 
     # Drop rows with null values
-    df.dropna(inplace=True)
+    feature_columns = [col for col in df.columns if col != 'Target_Close'] # Dont drop any Target_Close as we need this data for predictions
+    df.dropna(subset=feature_columns, inplace=True)
 
     # Replace Infinity with 0 -> This fixes the AI mental breakdown
-    df = df.replace([np.inf, -np.inf], 0)
+    df.replace([np.inf, -np.inf], 0, inplace=True)
 
     # Replace Infinity with 0 -> This fixes the AI mental breakdown
     df['Volume_Chg'] = df['Volume_Chg'].replace([np.inf, -np.inf], 0)