|
|
--- |
|
|
license: mit |
|
|
language: |
|
|
- en |
|
|
metrics: |
|
|
- mse |
|
|
tags: |
|
|
- LSTM |
|
|
- PyTorch |
|
|
- RNN |
|
|
- DeepLearning |
|
|
--- |
|
|
# Bitcoin Price Prediction with LSTM |
|
|
|
|
|
## Project Overview |
|
|
This project aims to predict Bitcoin (BTC) prices for the next 60 days using a Long Short-Term Memory (LSTM) neural network. The dataset used contains historical BTC/USD prices from 2014 to early 2024. The project leverages PyTorch for deep learning and includes data preprocessing, feature engineering, and model evaluation. |
|
|
|
|
|
--- |
|
|
|
|
|
## Table of Contents |
|
|
1. [Introduction](#introduction) |
|
|
2. [Dataset Description](#dataset-description) |
|
|
3. [Project Workflow](#project-workflow) |
|
|
4. [Model Architecture](#model-architecture) |
|
|
5. [Results](#results) |
|
|
6. [How to Run](#how-to-run) |
|
|
7. [Future Work](#future-work) |
|
|
8. [References](#references) |
|
|
|
|
|
--- |
|
|
|
|
|
## Introduction |
|
|
Bitcoin is a highly volatile cryptocurrency, making price prediction a challenging task. This project uses sequential data modeling with LSTM to capture patterns in historical BTC prices and provide reliable predictions. |
|
|
|
|
|
--- |
|
|
|
|
|
## Dataset Description |
|
|
- **Source**: Kaggle |
|
|
- **File**: `Dataset/BTC-USD.csv` |
|
|
- **Columns**: `Date`, `Open`, `High`, `Low`, `Close`, `Adj Close`, `Volume` |
|
|
- **Timeframe**: 2014 to early 2024 |
|
|
- **Frequency**: Minute-level data aggregated to daily prices. |
|
|
|
|
|
--- |
|
|
|
|
|
## Project Workflow |
|
|
### 1. Data Preparation |
|
|
- Import libraries and load the dataset. |
|
|
- Perform initial exploration to understand the data structure. |
|
|
|
|
|
### 2. Data Cleaning |
|
|
- Handle missing values and duplicates. |
|
|
- Normalize and standardize the data for better model performance. |
|
|
|
|
|
### 3. Exploratory Data Analysis (EDA) |
|
|
- Visualize trends in BTC prices and trading volume. |
|
|
- Analyze correlations between features. |
|
|
|
|
|
### 4. Feature Engineering |
|
|
- Create sequences of 30 days as input features. |
|
|
- Scale features using `MinMaxScaler`. |
|
|
|
|
|
### 5. Modeling |
|
|
- Build LSTM and GRU models using PyTorch. |
|
|
- Train the models with Mean Squared Error (MSE) loss and Adam optimizer. |
|
|
|
|
|
### 6. Evaluation |
|
|
- Evaluate the model using Root Mean Squared Error (RMSE). |
|
|
- Visualize predictions against actual prices. |
|
|
|
|
|
### 7. Prediction |
|
|
- Predict BTC prices for the next 60 days. |
|
|
- Compare predictions with actual future prices. |
|
|
|
|
|
--- |
|
|
|
|
|
## Model Architecture |
|
|
The LSTM model consists of: |
|
|
- **Input Layer**: Sequence of 30 days of closing prices. |
|
|
- **Hidden Layers**: 2 LSTM layers with 64 hidden units. |
|
|
- **Output Layer**: Single neuron for predicting the next day's price. |
|
|
|
|
|
--- |
|
|
|
|
|
## Results |
|
|
- **LSTM Test RMSE**: ~1,118 USD |
|
|
- **GRU Test RMSE**: ~21,445 USD |
|
|
- The LSTM model outperformed the GRU model, demonstrating its ability to capture sequential patterns in BTC prices. |
|
|
|
|
|
 |
|
|
|
|
|
--- |
|
|
|
|
|
## How to Run |
|
|
1. Clone the repository: |
|
|
```bash |
|
|
git clone <repository-url> |
|
|
cd Bitcoin-Prediction |
|
|
``` |
|
|
|
|
|
2. Install dependencies: |
|
|
```bash |
|
|
pip install -r requirements.txt |
|
|
``` |
|
|
|
|
|
3. Run the Jupyter Notebook: |
|
|
```bash |
|
|
jupyter notebook Notebook.ipynb |
|
|
``` |
|
|
|
|
|
4. Follow the steps in the notebook to train the model and visualize predictions. |
|
|
|
|
|
--- |
|
|
|
|
|
## Future Work |
|
|
- Add additional features such as macroeconomic indicators, Moving Average, RSI or sentiment analysis. |
|
|
- Perform hyperparameter tuning to further improve model performance. |
|
|
- Deploy the model as a web application for real-time predictions. |
|
|
|
|
|
--- |
|
|
|
|
|
## References |
|
|
- Kaggle Dataset: [BTC-USD Historical Data](https://www.kaggle.com/) |
|
|
- PyTorch Documentation: [https://pytorch.org/](https://pytorch.org/) |
|
|
- CoinGecko API: [https://www.coingecko.com/](https://www.coingecko.com/) |
