Overview

The BTC Prediction project is a machine learning system designed to forecast Bitcoin price movements using historical data. The goal is to provide traders and investors with data-driven insights for making more informed decisions in the volatile cryptocurrency market.

Project Structure

Data Collection & Processing

The project begins with comprehensive data collection:

  • Historical price data: OHLCV (Open, High, Low, Close, Volume) data at various intervals
  • Data preprocessing: Handling missing values, normalization, and feature scaling
  • Time series validation: Creating appropriate train/test splits that respect temporal order

Data Visualization

Visualizations are created to understand Bitcoin’s behavior:

  • Price trends: Time series plots of Bitcoin price movements
  • Volatility analysis: Visualization of price volatility over different periods
  • Correlation studies: Relationships between Bitcoin and other financial metrics
  • Technical indicator visualization: Visual representation of engineered features

Feature Engineering

The system leverages various technical indicators as features:

  • Moving averages: Simple and exponential moving averages across different windows
  • Daily returns: Percentage and logarithmic returns
  • Momentum indicators: RSI, MACD, and other technical analysis metrics
  • Volatility measures: Bollinger bands, ATR, and standard deviation of returns

Model Building

The project employs multiple predictive models:

  • Time series models: ARIMA/SARIMA for baseline predictions
  • Machine learning classifiers: For directional price movement prediction
  • Regression models: For price value forecasting
  • Deep learning approaches: LSTM networks for capturing complex temporal patterns

Evaluation & Prediction

Model performance is evaluated using:

  • Directional accuracy: Ability to predict price movement direction
  • RMSE/MAE: Error metrics for price value predictions
  • Backtesting: Simulated trading strategies based on model predictions
  • Comparison with baselines: Performance against random and naive forecasting methods

Technical Implementation

The entire system is implemented in Python using key libraries:

  • Pandas: For data manipulation and preprocessing
  • Matplotlib: For data visualization and insights
  • Scikit-learn: For machine learning algorithms and evaluation
  • NumPy: For numerical operations

Future Improvements

Planned enhancements include:

  • Incorporating additional data sources like market sentiment
  • Implementing more sophisticated deep learning architectures
  • Creating a real-time prediction API
  • Developing risk management overlays for trading strategies