PS C:\Users\siddh> & C:/Users/siddh/AppData/Local/Programs/Python/Python311/python.exe c:/Users/swarn/python_codes/python_try/hugging/predictive_31.py
C:\Users\siddh\AppData\Local\Programs\Python\Python311\Lib\site-packages\yfinance\base.py:48: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  _empty_series = pd.Series()
Program started...
Do you want to 'Train' the models or 'Use' existing models? Enter 'Train' or 'Use': USE
Enter the stock symbols separated by commas (e.g., AAPL, GOOGL, MSFT): NSE:HAL,NSE:MAZDOCK
Enter the prediction horizon in days (e.g., 5): 15
Enter the number of years of data to use for training (e.g., 5 or 10): 5
Training the Models on 402 stocks data
Estimated Time in minutes =  402
Time of start =  2024-09-22 08:00:34.430520
Loading saved models...
RandomForestRegressor model loaded from c:/users/swarn/python_codes/python_try/hugging/new_models/predictive_31_RandomForestRegressor_model.pkl
XGBoostRegressor model loaded from c:/users/swarn/python_codes/python_try/hugging/new_models/predictive_31_XGBoostRegressor_model.pkl
Scaler loaded from scaler.pkl
Label encoder loaded from label_encoder.pkl

Available models:
1. Random Forest Regressor - RMSE: 0.123551
2. XGBoost Regressor - RMSE: 0.144303
Enter the number corresponding to the model you want to use: 1
You have selected: Random Forest Regressor
Fetching stock data...
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100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 402/402 [01:07<00:00,  5.97it/s] 
Calculating technical indicators on recent data...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 392/392 [00:02<00:00, 168.11it/s] 

Predicted returns for the next 15 days for your queried symbols (sorted by predicted return):
          Symbol       Date    Close  Predicted_Return_Percentage  Predicted_Future_Price  Predicted_Profit
7683  MAZDOCK.NS 2024-09-20  4374.10                         4.10                 4553.43            179.33
3023      HAL.NS 2024-09-20  4333.35                         3.69                 4493.31            159.96
Current Date =  2024-09-22

Predictions saved to c:/users/swarn/python_codes/python_try/hugging/predictions\Predictions_2024-09-22.csv
Program execution completed.

Guidance on model training:
1. Training with more stocks generally improves model generalization but increases training time.
2. Using data from longer periods (e.g., 10 years) can capture more market cycles but may include outdated patterns.
3. Balance between the number of stocks, data period, and computational resources for optimal performance.
PS C:\Users\siddh>