Bitcoin Price Prediction with LSTM

Project Overview

Table of Contents

1. Introduction
2. Dataset Description
3. Project Workflow
4. Model Architecture
5. Results
6. How to Run
7. Future Work
8. References

Introduction

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

• 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

   1git clone <repository-url> 2cd Bitcoin-Prediction
2. Install dependencies:
   pip install -r requirements.txt
3. Run the Jupyter Notebook:
   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
• PyTorch Documentation: https://pytorch.org/
• CoinGecko API: https://www.coingecko.com/