Building a Bitcoin Price Forecast with a One-Day LSTM Window

Summary

The document walks through a basic LSTM workflow for forecasting Bitcoin prices from daily market data. It loads open, high, low, close, volume, and weighted-price fields; checks for missing and zero values; replaces zeros with missing values and carries the previous observation forward; then scales the features and splits the series into training and test portions. The target is the next day’s weighted price, constructed from a one-day input window.

The model uses an LSTM layer followed by a single output layer and is trained with mean absolute error and the Adam optimizer. The article plots training and validation losses and compares predictions with test labels, but supplies no quantitative accuracy measure or out-of-sample trading results. Its one-day context and simple architecture do not establish predictive or trading value. Forward-filling zero prices and volumes can also distort the data, and fitting the scaler before splitting risks information leakage. The document itself cautions that long-term Bitcoin forecasting is difficult and presents the example for learning rather than investment use.

Key ideas

• The example uses daily Bitcoin market fields and forecasts the next day’s weighted price.
• It replaces zero observations with missing values and forward-fills them before scaling the data.
• The input window contains one day, and the model consists of an LSTM layer followed by a dense output layer.
• Training and validation losses and predicted versus observed values are plotted, but no numerical forecast evaluation is reported.
• The simple demonstration does not establish a reliable long-term forecasting or trading strategy.

Tags

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