Building and Evaluating an LSTM for Bitcoin Price Forecasting

Summary

This tutorial builds a PyTorch LSTM to forecast the next Bitcoin closing price from hourly open, high, low, close, and volume data. It explains the LSTM input and output dimensions, sequence length, batch ordering, hidden and cell states, then shows standardizing data, forming sequences, and training a two-layer LSTM with a linear output layer and mean squared error. The example uses non-overlapping windows of ten observations and compares predictions with later data.

The plotted forecasts track the training segment but lag when prices rise in the later, unseen period. The author also reports 81.4% directional accuracy on a selected segment, while questioning whether the calculation is sound. The evaluation is limited: scaling is described as rough, the example does not establish robust out-of-sample performance, and the author explicitly says the model is not ready for live trading. The article is useful as an implementation walkthrough, not evidence of a profitable forecasting strategy.

Key ideas

• An LSTM can map sequences of market features to subsequent closing-price targets.
• With batch-first input, tensors are arranged by batch, time step, and feature.
• The example standardizes the data and trains on non-overlapping sequences using squared-error loss.
• The later price rise exposes limitations in the model's generalization and preprocessing.
• The reported directional accuracy is tentative and does not establish live trading value.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.