lstm-price-prediction-guide

What problem does it solve?

This guide solves the problem of turning historical cryptocurrency data into usable LSTM models that can be trained, evaluated, and deployed for price forecasting.

Core Features & Use Cases

• End-to-end LSTM Pipeline: Covers data collection, feature engineering, preprocessing into sequences, model training, evaluation, and deployment paths.
• Feature Engineering for Trading Signals: Adds technical indicators such as SMA/EMA, RSI, MACD, Bollinger Bands, volume ratios, returns, and log returns for improved model inputs.
• Practical Training & Evaluation: Uses common deep learning components (LSTM layers, dropout, early stopping) and evaluates with RMSE/MAE while noting backtesting-style metrics.
• Use Case: Build an LSTM model for intraday crypto price prediction using Binance OHLCV data, indicator-derived features, and sequence windows for supervised learning.

Quick Start

Use the guide to implement the full LSTM workflow for your target coin by following the data collection, indicator feature creation, sequence building, and Keras training steps described in the document.