Move away from Python Interop for better stability

WebServer/AIPython/aipredictor.py

@@ -1,4 +1,5 @@
 import os
+import sys
 import joblib
 import numpy as np
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
@@ -7,7 +8,11 @@ import features
 import matplotlib
 matplotlib.use("Agg")
 
-def Predict(Symbol):
+def Predict():
+
+    # Get the Symbol from ARGV
+    Symbol = sys.argv[1]
+
     # Define paths (consistent with your previous script)
     SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
     DATA_DIR = os.path.join(SCRIPT_DIR, "data")
@@ -21,8 +26,6 @@ def Predict(Symbol):
         # Make the feature set
         df = features.MakeFeatures(df)
 
-    print(Symbol)
-
     # Drop our predictor
     df.drop('Target_Close', axis=1, inplace=True)
 
@@ -51,8 +54,7 @@ def Predict(Symbol):
     # 'predictions' will be a 2D array, flatten it if you want a simple list
     flat_predictions = actual_prediction.flatten().tolist()
 
-    print(f"Predicted Target_Close: {flat_predictions}")
-
+    # Set the movement indicator
     movement_indicator = 0
     if (np.mean(flat_predictions) > 0.01):
         movement_indicator = 1
@@ -61,7 +63,10 @@ def Predict(Symbol):
     else:
         movement_indicator = 0
 
-    return movement_indicator
+    # Return to C# via stdout
+    print(f"---RESULT_START---")
+    print(movement_indicator)
+    print(f"---RESULT_END---")
 
 if __name__ == "__main__":
-    Predict("AAPL")
+    Predict()

WebServer/AIPython/aitrainer.py

@@ -75,12 +75,9 @@ def TrainAI():
     test_results = dnn_model.evaluate(
         test_features, test_labels, verbose=0
     )
-    print(f"Test Results: {test_results}")
 
     # Save the model
     dnn_model.save(os.path.join(DATA_DIR, "model.keras"))
 
 if __name__ == "__main__":
-    TrainAI()
-
-# Last train Predicted Target_Close: [0.0022113274317234755, 0.0021446370519697666, 0.0022628342267125845, 0.002175702480599284, 0.0021452796645462513, 0.0020838389173150063, 0.0017336219316348433, 0.002210840117186308, 0.0021144403144717216, 0.0021278387866914272, 0.0021266420371830463, 0.002261851681396365, 0.002108299173414707, 0.002121902070939541, 0.0022294146474450827]
+    TrainAI()

WebServer/AIPython/currentprice.py

@@ -1,7 +1,19 @@
+import sys
 import yfinance as yf
 
-def getCurrentPrice(symbol):
+def getCurrentPrice():
+
+    # Get the Symbol from ARGV
+    symbol = sys.argv[1]
+
     ticker = yf.Ticker(symbol)
     data = ticker.history(period="1d", interval="1m")
     current_price = data['Close'].iloc[-1]
-    return current_price
+
+    # Return to C# via stdout
+    print(f"---RESULT_START---")
+    print(current_price)
+    print(f"---RESULT_END---")
+
+if __name__ == "__main__":
+    getCurrentPrice()

WebServer/AIPython/datapuller.py

@@ -15,7 +15,6 @@ def pull():
     # Scrape the data
     all_data = []
     for i, symbol in enumerate(tickers):
-        print(f"Processing: {i} of {len(tickers)}")
         df = yf.download(symbol, period="max", auto_adjust=True)
         if not df.empty:
             # Remove the ticker column
@@ -24,10 +23,11 @@ def pull():
             all_data.append(df)
 
     # Concatinate into a combined list and cache
-    print("Processing data")
     final_df = pd.concat(all_data)
 
     # Save to file
-    print("Writing data to file")
     final_df.to_parquet(os.path.join(DATA_DIR, "stocks.parquet"))
-    final_df.head(200).to_csv(os.path.join(DATA_DIR, "stocks.preview.csv"))
+    final_df.head(200).to_csv(os.path.join(DATA_DIR, "stocks.preview.csv"))
+
+if __name__ == "__main__":
+    pull()