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XAUUSD-LSTM

A Python-based LSTM model for forecasting XAU/USD (Gold vs USD) prices using historical 15-minute data. This project allows you to train an LSTM neural network, evaluate its predictions, and generate future price forecasts.

Features

  • Train an LSTM model on historical gold prices.
  • Automatic preprocessing and resampling of data.
  • Customizable sequence length, batch size, epochs, and validation split.
  • Save best model checkpoint and final trained model.
  • Generate recursive multi-step forecasts.
  • Plot validation predictions and historical + forecast trends.
  • Support for CSV input and output for easy integration.

Dataset

XAUUSD 15 minutes (2024 - 2025)

Date;Open;High;Low;Close;Volume
2004.06.11 07:15;384;384.3;383.8;384.3;12
2004.06.11 07:30;383.8;384.3;383.6;383.8;12
2004.06.11 07:45;383.3;383.8;383.3;383.8;20
...

https://www.kaggle.com/datasets/novandraanugrah/xauusd-gold-price-historical-data-2004-2024

Running

python train_xau_lstm.py --data "XAU_15m_data.csv" --datetime_col "Date" --target "Close" --seq_len 96 --epochs 5 --batch_size 64 --resample 15T

Requirements

  • Python 3.9+
  • TensorFlow 2.x
  • NumPy
  • pandas
  • scikit-learn
  • matplotlib

Install dependencies via:

pip install -r requirements.txt

(You can create requirements.txt with the following:)

tensorflow
numpy
pandas
scikit-learn
matplotlib

Usage

Command-Line Arguments

python train_xau_lstm.py --data "XAU_15m_data.csv" --datetime_col "Date" --target "Close" \
--seq_len 96 --epochs 30 --batch_size 64 --resample 15min --forecast_steps 16

Arguments:

Argument Description Default
--data Path to the CSV file containing historical data XAU_15m_data.csv
--datetime_col Name of the datetime column in CSV (auto-detected if not provided) None
--target Target column for prediction Close
--resample Resample frequency (15min, H, D, etc.) min
--seq_len Sequence length for LSTM input 96
--val_split Fraction of data for validation 0.2
--epochs Number of training epochs 30
--batch_size Training batch size 64
--patience Early stopping patience (number of epochs) 6
--dropout Dropout rate in LSTM layers 0.2
--model_path Path to save the best model checkpoint xau_lstm_best.keras
--final_model Path to save the final trained model xau_lstm_final.keras
--plot_path Path to save validation prediction plot prediction_plot.png
--forecast_steps Number of future steps to forecast 16
--forecast_out CSV output path for forecast results forecast_next.csv

Output

  1. Model files:

    • xau_lstm_best.keras: Best model checkpoint during training.
    • xau_lstm_final.keras: Final trained model.

  2. Forecast CSV: Example forecast_next.csv:

,forecast
2025-09-30 19:45:00,3280.56
2025-09-30 20:00:00,3308.98
2025-09-30 20:15:00,3337.23
...
2025-09-30 23:30:00,3529.38

  1. Plots:

    • prediction_plot.png: Validation actual vs predicted prices.
    • historical_forecast_plot.png: Historical prices + future forecast.

Example Workflow

# Train and forecast
python train_xau_lstm.py --data "XAU_15m_data.csv" --datetime_col "Date" --target "Close" \
--seq_len 96 --epochs 5 --batch_size 32 --resample 15min --forecast_steps 16
  • Model trains on historical XAU/USD prices.
  • Validation predictions are plotted.
  • Next 16 steps forecast is saved to forecast_next.csv.

Notes

  • Make sure your CSV uses ; as a separator.
  • resample should match your data frequency (15min, 1H, etc.).
  • For faster testing, reduce seq_len, epochs, or use a subset of the data.

License

This project is licensed under the MIT License.

Author

Max Base

GitHub: https://github.com/BaseMax/XAUUSD-LSTM

Copyright 2025, Max Base