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---
license: mit
language:
- en
tags:
- bitcoin
- lstm
- time-series
- price-prediction
- tensorflow
- keras
- finance
---
# 🧠 Bitcoin Price Forecasting using LSTM Neural Network
A deep learning model based on Long Short-Term Memory (LSTM) networks to predict the next-day closing price of Bitcoin (BTC-USD) using historical data from Yahoo Finance.
---
## 🔍 Model Overview
| Feature | Description |
|--------------------|-----------------------------------------------------------------------------|
| 📦 Model Type | LSTM (Long Short-Term Memory), a variant of Recurrent Neural Networks (RNN) |
| 🧠 Frameworks Used | TensorFlow (Keras API), Scikit-learn, NumPy, Pandas, yfinance |
| 📈 Input | Past 60 days of Bitcoin closing prices |
| 🎯 Output | Predicted closing price for the next day |
| 📊 Evaluation | Root Mean Squared Error (RMSE) |
| 🧪 Goal | Short-term (1-day ahead) BTC price forecasting |
---
## 🔧 What the Model Does
- Downloads historical BTC-USD data from Yahoo Finance
- Normalizes the data between 0 and 1 using MinMaxScaler
- Splits into 80% training and 20% test sets
- Creates time-sequenced inputs with a 60-day sliding window
- Trains a 2-layer LSTM model with dropout to prevent overfitting
- Evaluates the model using RMSE
- Plots predicted vs actual prices
- Makes a next-day prediction using the last 60 days of data
---
## 💡 Use Cases
- Educational: Learning time series forecasting and LSTM models
- Research: Benchmarking for financial forecasting models
- Visualization: Analyze model performance on real BTC data
- Academic Support: Useful for papers or prototypes on AI-based financial systems
---
## ⚠️ Limitations
- Uses only the closing price (no volume, indicators, or sentiment data)
- Performs only single-step (1-day ahead) forecasting
- Does not account for sudden market news or shocks
- Not designed for high-frequency or live trading systems
---
## 🚀 Potential Improvements
- Include additional features: volume, RSI, MACD, etc.
- Integrate external signals: news, social media sentiment, macro data
- Add attention or transformer-based layers
- Extend to multi-step forecasting (3-day, 5-day, etc.)
- Deploy as REST API or interactive dashboard
- Connect to Binance or other exchanges for live predictions
---
## 📁 Files
- `lstm_bitcoin_predictor.py`: Full code to train, evaluate, and predict using LSTM
- `data.csv`: (optional) Cached historical BTC-USD data
- `model.h5`: Saved trained model
---
## 📜 License
This project is licensed under the MIT License.
---
## ⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️ Disclaimer⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️
> **This model is intended for educational and research purposes only.**
>
> It is **not** designed for financial or investment decision-making.
> No guarantees are made about the accuracy of the forecasts.
> The authors accept no responsibility for any financial losses incurred from the use of this model.
> **Use at your own risk.**
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