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Tata Power Next-Day Close Price Prediction

This project trains and tests an LSTM model to predict next-day closing prices for equities using daily OHLCV data and technical indicators.

The pipeline is generic for any company/stock as long as you provide a similar daily CSV (with Date, Open, High, Low, Close/Price, Volume). In this repository we have tested it on Tata stocks (Tata Power / Tata Motors) and obtained good results.

Files

  • v3_delta.py
    Trains the LSTM model on TataPower_2005_2025.csv using:

    • Window size: 14 previous trading days
    • Features: Price, Open, High, Low, Vol., Volatility, RSI, Momentum
    • Target: next-day log return of Price, later converted back to price.
    • Outputs:
      • v3_delta_savedmodel/ (TensorFlow SavedModel)
      • v3_delta_scaler_X.pkl (StandardScaler for features)
      • v3_delta_scaler_y.pkl (RobustScaler for target)

  • test_range_v3_delta_csv.py
    Loads the saved model and scalers, then evaluates predictions over a chosen date range (currently 2025-11-01 .. 2025-12-31) using the CSV file. It prints:

    • Per-day actual vs predicted next-day close
    • MAE, RMSE, and % within ±50 points
    • How many days the model predicted a drop (negative log return)
    • A matplotlib plot of actual vs predicted next-day close.

  • TataPower_2005_2025.csv
    Historical Tata Power price data used for training and testing.

  • v3_delta_savedmodel/
    Trained TensorFlow model directory.

  • v3_delta_scaler_X.pkl, v3_delta_scaler_y.pkl
    Scalers used for input features and target, saved after training.

  • Figure_1.png
    Example plot of Actual vs Predicted next-day Close from a test run.

Example Figure

Below is an embedded example plot from the project:

Actual vs Predicted Next-Day Close

How to Run

  1. Environment

    • Python 3.11
    • TensorFlow 2.15.x
    • Required Python packages: tensorflow, keras, numpy, pandas, scikit-learn, matplotlib.

  2. Train / Retrain the Model

python v3_delta.py

This:

  • Reads TataPower_2005_2025.csv
  • Trains the LSTM with a 14-day window
  • Saves the model and scalers.
  1. Run the Test Script
python test_range_v3_delta_csv.py

This:

  • Loads v3_delta_savedmodel/ and the scalers
  • Evaluates from 2025-11-01 to 2025-12-31
  • Prints metrics and shows the Actual vs Predicted plot
  • Prints how many days the model predicted a price drop.

Notes

  • The main goal is to keep next-day close predictions within ±50 points of the actual close.
  • The shorter 14-day window makes the model more sensitive to recent market moves and better at predicting both up and down days.