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lstm-price-prediction-guide

Transform historical OHLCV data into supervised sequences for LSTM price prediction.

5|Updated May 2, 2026
Search Tags:#deployment#feature engineering#time-series#model evaluation#tensorflow#cryptocurrency#lstm
One-click install
npx skills add https://github.com/nirholas/three-ui --skill lstm-price-prediction-guide
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lstm-price-prediction-guide
Source: https://github.com/nirholas/three-ui/tree/main/data/skills/analysis/lstm-price-prediction-guide
Command: npx skills add https://github.com/nirholas/three-ui --skill lstm-price-prediction-guide

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about lstm-price-prediction-guide

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an LSTM model for cryptocurrency price prediction?▼

To build an LSTM cryptocurrency price predictor, transform historical OHLCV data into supervised sequences using MinMaxScaler, train a Keras LSTM architecture with dropout and early stopping, and evaluate using RMSE and MAE metrics.

What technical indicators should I use for feature engineering in time-series crypto forecasting?▼

For time-series crypto forecasting feature engineering, apply technical indicators such as SMA, EMA, RSI, MACD, Bollinger Bands, volume ratios, and log returns to your historical OHLCV data to improve model inputs.

How do I prepare sequence windows for TensorFlow Keras LSTM training?▼

Prepare sequence windows for Keras LSTM training by scaling your OHLCV data with MinMaxScaler and transforming the time-series into supervised learning sequences formatted as overlapping input windows.

Does this LSTM pipeline support backtesting and deployment for intraday crypto trading?▼

Yes, the LSTM pipeline supports intraday crypto backtesting and deployment by evaluating predictions with RMSE and MAE metrics, applying inverse transforms for price-level outputs, and covering deployment paths.

Why does my LSTM model evaluation need inverse transforms for price-level metrics?▼

Inverse transforms are needed for price-level metrics because the LSTM model trains on MinMaxScaler-scaled data, requiring inverse transforms to convert RMSE and MAE evaluation results back into original cryptocurrency price values.

Can I use Binance OHLCV data directly for LSTM sequence windowing and training?▼

Yes, you can use Binance OHLCV data for LSTM training by applying MinMaxScaler, generating technical indicator features, building sequence windows, and fitting a Keras architecture with dropout and early stopping.

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