Update MLCryptoForecasterAllAssetsTPSL.py

MLCryptoForecasterAllAssetsTPSL.py

@@ -9,7 +9,6 @@ from sklearn.metrics import classification_report
 import ta
 
 # Function to log results to both console and file
-# Does not insert blank lines; blank lines added explicitly after each asset block
 def log_results(message, filename="predictions_results.txt"):
     print(message)
     with open(filename, "a") as f:
@@ -28,7 +27,7 @@ if os.path.exists(result_file):
 
 # Initialize result file header
 with open(result_file, "w") as f:
-    f.write("Asset,Accuracy,Optimal_UP_TP,Optimal_UP_SL,Optimal_DN_TP,Optimal_DN_SL\n")
+    f.write("Asset,Time,Price,Prediction,Optimal_UP_TP,Optimal_UP_SL,Optimal_DN_TP,Optimal_DN_SL\n")
 
 # Get USDT-quoted trading symbols
 symbols = [s['symbol'] for s in client.get_exchange_info()['symbols']
@@ -120,10 +119,12 @@ for symbol in symbols:
     report = classification_report(yte, ypr, zero_division=0)
     log_results(f"Classification report for {symbol}:\n{report}", result_file)
 
-    # Predict latest trend and log it
+    # Predict latest trend and log time & price
+    pred_time = df.index[-1]
+    pred_price = df['close'].iloc[-1]
     latest = model.predict(X.iloc[-1:].values)[0]
     trend_map = {1:'Uptrend',0:'Downtrend',-1:'Neutral'}
-    log_results(f"Predicted next trend for {symbol}: {trend_map[latest]}", result_file)
+    log_results(f"Time: {pred_time}, Price: {pred_price:.2f}, Prediction: {trend_map[latest]}", result_file)
 
     # Optimize TP/SL and log results
     hist_sign = model.predict(X.values)