LSTM · ML Arena

About LSTM · ML Arena

LSTM (Long Short-Term Memory) is a recurrent neural network specifically designed for time series — exactly what crypto price data is. Unlike gradient boosting models (XGBoost, LightGBM) that process each data point independently, LSTM has a 'memory' that allows it to capture temporal dependencies: how yesterday's price influences today's, how a 3-day volume pattern signals a move. It's the first 'deep learning' model in the ML arena.

How it works

2-layer LSTM architecture (128 and 64 neurons) with gate mechanism (forget, input, output). Receives a 30-hour data sequence (price, volume, RSI, MACD, ATR). Gates decide what to retain and forget from the sequence. Predicts price direction at H+4 and H+8. Trained on 1 year of data with early stopping to avoid overfitting. 20% dropout for regularization.

Signals used

LSTM prediction (directional probability H+4 and H+8). 30-hour input sequence. Gate mechanism (selective memory). Confidence score (softmax output). Monthly retraining. 2-layer deep learning architecture.

Risk level

Moderate

Strengths

Captures temporal dependencies that tabular models miss. Long and short-term memory (dual memory). Proven architecture for financial time series. Discovers complex sequential patterns invisible to technical indicators.

Weaknesses

Slow to train (deep learning = GPU required). Sensitive to hyperparameters (layer sizes, learning rate). Black box (difficult to explain why a prediction is made). Higher overfitting risk than ensemble models.

Features

• Category: ML
• Arena: ML Arena
• 24/7 real-time simulation
• Live Binance data