🧠 LSTM Model Summary – Bitcoin Price Forecasting
📌 This repository focuses on Bitcoin price forecasting from a stock market perspective using LSTM, covering the period 2017–2024.
A separate version treating Bitcoin as a currency will be provided in a future repository.
This project is part of a dual-perspective study on Bitcoin:
- Perspective 1: Bitcoin as a Stock
- Perspective 2: Bitcoin as a Currency (this code + dataset)
While this notebook uses the currency view, the same code can be applied to other datasets prepared under the stock-based perspective or different time splits (e.g., 2017–2020, 2020–2021, etc.).
📦 Model Architecture:
- Input Shape: (60, 1) → sequences of 60 daily prices
- 🧩 Layer 1: LSTM (tunable units: 32–128), return_sequences=True
- 🔽 Dropout (tunable rate: 0.1–0.5)
- 🧩 Layer 2: LSTM (same unit config), return_sequences=False
- 🔽 Dropout (same rate)
- 🔢 Dense Layer: (tunable units: 16–64), ReLU activation
- 🎯 Output Layer: Dense(1) → predicted price
🛠 Tunable Hyperparameters:
lstm_units: [32, 64, 96, 128]dropout_rate: [0.1 to 0.5]dense_units: [16, 32, 48, 64]learning_rate: [0.0001 to 0.01] (log scale)
🧪 Loss Function:
- Mean Squared Error (MSE)
🚀 Optimizer:
- Adam (tunable learning rate)
🎯 Target:
- Predict the next day's Bitcoin price in USD
🔗 Real-time Integration:
- Fetches live Bitcoin price using CoinGecko API (used in validation or to compare with predictions)
📊 Sequence Length Used:
- 60-day window to predict the 61st value
This architecture is designed to balance complexity and generalization by using dropout, sequence-aware layers, and flexible hyperparameter search via KerasTuner.