Building a Stock Price Predictor using LSTM in Keras

Upload the code.ipynb to a folder named Stock predictor in Google Drive, then open it with Google Colab and enable gpu acceleration by changing the runtime type.

Install essential libraries, including yfinance, pandas, numpy; fetch Yahoo Finance data, preprocess with a scaler, train an lstm in TensorFlow, and evaluate with mean squared error and mean absolute error.

Import essential Python libraries to fetch, manipulate, scale, and visualize stock data. Build and train an LSTM model in Keras using layers like LSTM, Dense, and Dropout.

Learn to connect Google Colab to Google Drive to persist models, plots, and configurations. Mount Google Drive, authorize access, and save outputs securely to prevent data loss from timeouts.

Create a dedicated Stock Predictor folder in Google Drive to store all project outputs—trained LSTM model, plots, forecasts, CSV files, and metadata—using exist_ok to ensure reproducibility.

Configure a stock price predictor by selecting ticker and date range, setting 60 time steps, and tuning features and forecast horizon to 30 days for an LSTM model in Keras.

Discover a flexible data fetching function that downloads stock data with yfinance, offering either close prices only or multi-feature data including open, high, low, close, and volume.

Visualize stock price data with a time series line plot to identify trends and anomalies. The code adds axes labels, a grid, and tight layout for a readable, exportable visualization.

Scale data with a min-max scaler to 0–1, using fit_transform to prepare time series inputs for the LSTM in Keras and boost training stability.

Store the trained MinMaxScaler to disk using Joblib, so the same object used in training can inverse transform predictions into dollars during deployment.

Create sequences of data for an LSTM stock price predictor using a 60-day sliding window, apply an 80/20 split, then reshape into 3D [samples, timesteps, features] for training.

Build a two-layer LSTM model in Keras to predict stock prices from a 60-day window, with dropout and dense layers. Compile with Adam and MSE and review the model summary.

Save the best Keras model to Google Drive with a model checkpoint that monitors validation loss and uses early stopping to restore weights in HDF5 format for efficient deployment.

Train the LSTM stock price predictor with model.fit on Xtrain and Ytrain, validating on Xtest and Ytest, across 20 epochs with batch size 16, using checkpoint and early stopping.

Load the best performing LSTM model saved by TensorFlow Keras checkpointing for prediction and evaluation, ensuring maximum generalization by deploying the best checkpoint rather than the latest training state.

Execute the trained LSTM model loaded from disk to predict the next stock price from the past 60 days, generate predictions, and inverse transform to original values for backtesting.

Apply scaler inverse transform to lstm predictions to recover real stock prices for single and multi-feature setups, enabling real-world plots and mae and rmse metrics.

Visualize the LSTM stock price predictions by plotting actual versus predicted prices over test dates, with a legend and grid to show whether the model learns temporal patterns.

Save your lstm stock predictions as a clean csv via pandas data frames, including actual and predicted prices after inverse transform, ready for Excel dashboards and sharing with stakeholders.

Forecast the future with a trained LSTM model by generating the next 30 days of stock prices using rolling, autoregressive predictions and inverse transforming the results to the original scale.

Generate 30 future business-day timestamps after the last historical stock date to label forecasted prices for plotting, using business day frequency to match trading days and skip weekends.

Plot forecasted future prices to visualize the lstm predictions for next 30 business days, with labeled axes and a legend for clarity. Save as a png and display in Colab.

Save the future forecast to a csv file, creating a pandas data frame with date and forecasted price columns for the next 30 days, saved as AAPL_future_forecast.csv without index.

Evaluate model performance by comparing predicted and actual stock prices using RMSE and MAE. Choose the best model by comparing these metrics and aim to lower them.

Document the model by saving a JSON metadata file in Google Drive with ticker, training period, sequence length, features, forecast horizon, hyperparameters, and RMSE. This passport ensures reproducibility and transparency.