---
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.

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## ⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️⚠️ 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.**