Bitcoin Price Predictor App

Welcome to the Bitcoin Price Predictor App! This interactive data application is designed to provide insights into Bitcoin price trends, analyze technical indicators, and predict future prices using machine learning models. The app is built with Streamlit, making it easy to use and visually appealing.

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Features

📈 Overview

• Price Trends: Visualize Bitcoin price trends over time.
• Seasonality Analysis: Decompose the time series into trend, seasonal, and residual components.
• Volatility Analysis: Analyze rolling volatility to understand price fluctuations.

📊 Technical Indicators

• Explore key technical indicators such as:
  • Simple Moving Averages (SMA)
  • Relative Strength Index (RSI)
  • Moving Average Convergence Divergence (MACD)
• View all indicators combined for a comprehensive market analysis.

🔮 Model Prediction

• Predict Bitcoin prices for the next 1 to 30 days using an LSTM (Long Short-Term Memory) model.
• Visualize predictions alongside historical data.
• Key metrics include:
  • Next Day Prediction
  • Forecast Average Change

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How to Run the App

1. Clone the Repository:

   git clone https://github.com/lis-r-barreto/bitcoin-price-predictor-app/
   cd bitcoin-price-predictor-app/src/bitcoin_price_predictor_app/

2. Install Dependencies: Ensure you have Python installed. Then, use Poetry to install the required libraries:

   poetry install

3. Run the App: Start the Streamlit app by running:

   streamlit run app.py

4. Access the App: Open your browser and navigate to the URL provided by Streamlit (usually http://localhost:8501).

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Data Source

The app uses Bitcoin price data from Yahoo Finance, spanning from 2010 to March 2025. The data is preprocessed and stored in a Parquet file for efficient loading.

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App Structure

Tabs

1. Overview:

   • Visualize price trends, seasonality, and volatility.
   • Customize analysis parameters (e.g., rolling window size, seasonal decomposition period).

2. Technical Indicators:

   • Analyze individual and combined technical indicators for market insights.

3. Model Prediction:

   • Generate forecasts for the next 1 to 30 days.
   • View predictions in a table and interactive chart.

Utilities

• Data Visualization: Custom plots for trends, seasonality, and technical indicators.
• Machine Learning: LSTM model for time series forecasting.

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License

This project is licensed under the MIT License. See the LICENSE file for details.