CryptoTrendPredictor_LSTM

CryptoTrendPredictor_LSTM

📈 Overview

This repository hosts a custom Long Short-Term Memory (LSTM) recurrent neural network designed for single-step time-series prediction of cryptocurrency prices. Specifically, this model is trained to predict the normalized closing price of Bitcoin (BTC) for the next day, utilizing a diverse set of technical indicators and on-chain metrics.

🧠 Model Architecture

The model implements a stacked LSTM architecture, which is highly effective for capturing long-term dependencies in sequential data like price time series.

• Architecture: Stacked LSTM (3 layers) with a final Dense layer for regression.
• Input Features (input_size=10): 10 features including price, volume, technical indicators (RSI, MACD), market correlation, and external factors (Google Trends, Social Media Sentiment).
• Sequence Length (sequence_length=60): The model takes the past 60 time steps (days) of feature data as input to make one prediction.
• Hidden State Size (hidden_size=128): 128 units per LSTM layer.
• Output: A single floating-point value representing the normalized predicted closing price for the next time step.
• Loss Function: Mean Squared Error (MSE).

💡 Intended Use

• Price Forecasting: Providing a probabilistic forecast of the next-day price movement for risk assessment.
• Signal Generation: Generating buy/sell/hold signals when the predicted value crosses predefined thresholds.
• Comparative Analysis: Benchmarking against simpler linear or technical analysis models.

Data Requirements for Inference

The model expects a numpy array/tensor of shape (batch_size, sequence_length, input_size). The input features must be normalized using the same min/max scaling applied during training.

How to use (Conceptual Example)

import numpy as np
# Assuming the model is loaded from a framework like PyTorch or TensorFlow
# and inference data is ready and pre-processed.

# Replace with actual data (60 days of 10 features, normalized)
input_data = np.random.rand(1, 60, 10).astype(np.float32)

# prediction = model(input_data) 
# print(f"Normalized Prediction: {prediction.item()}")

# Inverse transform the prediction to get the actual price forecast
# forecasted_price = scaler.inverse_transform(prediction) 
# print(f"Forecasted BTC Price: ${forecasted_price.item():.2f}")