CryptoPriceForecaster: Deep LSTM for Short-Term Price Trend Prediction

📑 Overview

CryptoPriceForecaster is a deep Long Short-Term Memory (LSTM) neural network optimized for predicting the closing price trend (i.e., the price change magnitude) of a major cryptocurrency (e.g., BTC/USD) over a short-term horizon (e.g., next 24 hours). It is trained on historical data including price, volume, and derived technical indicators.

🤖 Model Architecture

This is a custom deep learning model implemented in PyTorch/TensorFlow.

• Type: Recurrent Neural Network (RNN) - LSTM variant.
• Input Features: 10 features (e.g., Open, High, Low, Close, Volume, 7-day EMA, RSI, MACD Histogram, etc.).
• Sequence Length: 30 time steps (i.e., the model looks at the last 30 daily data points).
• Layers: 3 stacked LSTM layers.
  • Hidden Size: 128 units per layer.
  • Dropout: 0.2 applied between layers.
• Output: A single dense layer predicting the scaled normalized closing price. The predicted value needs to be inverse-transformed to get the actual price.

🎯 Intended Use

This model is designed for:

1. Short-Term Trading Strategy: Generating daily signals for predicting price movement direction.
2. Research: Studying the non-linear dependencies between technical indicators and price volatility.
3. Educational Purposes: Demonstrating the use of RNNs for financial time-series forecasting.

⚠️ Limitations

• Prediction vs. Reality: This model predicts price based on historical and technical data; it does not account for Black Swan events, regulatory changes, or breaking news. It is not financial advice.
• Generalization: Trained primarily on BTC/USD daily data, its performance on altcoins or intraday data may be significantly lower.
• Stationarity: Requires pre-processed, potentially stationary, input data (e.g., log returns) for optimal performance.

💻 Example Code

Assuming the input data is a 3D numpy array X_test (batch_size, sequence_length, features):

import torch
import numpy as np
from CryptoPriceForecaster_Model import LSTMForecaster # Custom model class

# Load the model weights and config
config = json.load(open("config.json"))
model = LSTMForecaster(**config) 
model.load_state_dict(torch.load("pytorch_model.bin"))
model.eval()

# Example input: 1 sample, 30 sequence length, 10 features
X_test_sample = np.random.rand(1, 30, 10).astype(np.float32) 
input_tensor = torch.from_numpy(X_test_sample)

with torch.no_grad():
    scaled_prediction = model(input_tensor)

# The output must be inverse-transformed using the original scaler (MinMaxScaler)
# For demonstration:
print(f"Scaled Predicted Price Change: {scaled_prediction.item():.4f}")