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.

Introduction
Dataset Description
Project Workflow
Model Architecture
Results
How to Run
Future Work
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

Clone the repository:

git clone <repository-url>
cd Bitcoin-Prediction

Install dependencies:
```
pip install -r requirements.txt
```
Run the Jupyter Notebook:
```
jupyter notebook Notebook.ipynb
```
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
PyTorch Documentation: https://pytorch.org/
CoinGecko API: https://www.coingecko.com/

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Bitcoin-Price-Prediction-with-LSTM