Building an LSTM Forex Model: From Data Fetching to Prediction
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Embark on a comprehensive journey to understand the nuances of constructing a deep learning model for the EUR/USD exchange rate using an LSTM network. This tutorial covers every step in detail, starting from installing necessary libraries like pmdarima and pandas-datareader, to fetching, processing, and visualizing the EUR/USD stock data. We dive deep into the mechanics of creating trading signals based on moving averages, followed by data filtering, scaling, and preparation for training and testing the LSTM model.
Key Highlights of the Video:
Introduction to the required libraries and their installation process.
Detailed steps for fetching and visualizing the EUR/USD exchange rate data from Yahoo Finance.
Insightful explanation on generating and visualizing trading signals based on moving average crossovers.
Methodical approach to data preprocessing, including scaling and sequence creation for LSTM readiness.
Construction and training of a sophisticated LSTM model, followed by its evaluation and prediction generation.
Practical demonstration of saving the model and preparing test data for evaluating the model's performance.
By the end of this tutorial, viewers will gain a solid understanding of how to implement an LSTM network for time series prediction, specifically applied to forex data. This knowledge can be extended to various other domains and datasets, providing a robust foundation for anyone interested in deep learning and financial time series analysis.
00:00:00 Install pmdarima library and pandas-datareader
00:00:37 Imports libraries for neural network model
00:02:36 Retrieve and display stock data
00:03:53 Retrieve the dimensions of the dataframe
00:04:31 Plotting stock's close price history
00:05:14 Calculates trading signals based on moving averages
00:07:58 Visualizes stock data and trading signals
00:09:36 Filter data and split into training data
00:10:27 Scales and creates training data sequences
00:11:47 Converts data to numpy arrays
00:12:13 Reshape x_train array by adding dimension
00:13:04 Create a deep learning model using LSTM
00:14:28 Compiles the model with Adam optimizer
00:15:22 Train model for 1 epoch
00:16:22 Save a model using joblib
00:17:19 Prepare test data for LSTM model
00:19:21 Converts x_test to numpy array
00:19:57 Reshape x_test with one additional dimension
00:20:40 Make predictions and scale them back
00:21:45 Calculates the root mean squared error
00:22:39 Displays model's predictions on stock data