Bitcoin Price Prediction with Sentiment Analysis Using XGBoost

Predicting cryptocurrency prices has long been a challenge due to their inherent volatility and sensitivity to market sentiment. However, combining machine learning with financial modeling techniques offers a promising approach. This article explores a comprehensive script that forecasts Bitcoin (BTC) prices using historical price data, sentiment analysis, technical indicators, and advanced modeling with XGBoost and GARCH volatility forecasting.

The methodology integrates data science and financial econometrics to deliver actionable predictions—making it highly relevant for traders, analysts, and developers interested in algorithmic forecasting.

Data Collection: Building the Foundation

Purpose: Gather and Align Historical Price and Sentiment Data

Accurate predictions start with high-quality data. The first step involves collecting two critical datasets:

• Historical Bitcoin price data (Open, High, Low, Close, Volume) is pulled using the yfinance library from Yahoo Finance for the BTC-USD pair.
• Sentiment data is loaded from a preprocessed CSV file (bitcoin_sentiments_21_24.csv), which contains sentiment scores derived from news, social media, or other textual sources, aligned by date.

To ensure alignment, the script calculates a three-year (1041-day) historical window ending at the last available sentiment date. The datasets are then merged on the Date column using an inner join. Missing sentiment values are filled via linear interpolation, defaulting to 0.5 (neutral sentiment) if gaps remain.

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The final output—a clean, time-aligned dataset—is saved as bitcoin_historical.csv, forming the foundation for all downstream analysis.

Feature Engineering: Enhancing Predictive Power

Purpose: Create Meaningful Inputs for Machine Learning Models

Raw price data alone is insufficient for robust forecasting. This phase transforms the dataset by generating technical, behavioral, and statistical features.

Technical Indicators (via ta library)

These widely used tools help identify trends and momentum:

• SMA (Simple Moving Averages): 5-day, 20-day, and 50-day averages of closing prices.
• RSI (Relative Strength Index): 14-day momentum oscillator indicating overbought or oversold conditions.
• MACD: Tracks trend changes through the convergence and divergence of moving averages.
• ATR (Average True Range): Measures market volatility over 30 days.
• Bollinger Bands: Upper and lower bands derived from 20-day rolling standard deviations.

Additional Features

• Daily Return: Percentage change in closing price.
• Lagged Close Prices: Lagged_Close_1 and Lagged_Close_3 capture short-term price memory.
• Volume Features: Normalized trading volume and its interaction with absolute price movement to detect volume-driven trends.

Volatility Modeling with GARCH

A GARCH(1,1) model with Student-t distribution is fitted to historical returns over three years. This captures time-varying volatility—a key characteristic of crypto markets—providing more realistic future simulations.

Finally, missing values are backfilled (bfill) to preserve data continuity without introducing bias.

Data Preprocessing: Preparing for Machine Learning

Purpose: Structure Data for Model Training

Before training begins, the dataset must be properly formatted:

• Target Variable (y): Next day’s closing price (Close.shift(-1)).
• Feature Set (X): Includes all engineered variables—price, technicals, sentiment, lagged values, and volatility estimates.

The data is split into:

• 80% training set
• 20% testing set

All features are scaled to the [0, 1] range using MinMaxScaler, ensuring numerical stability during model training.

Additionally, a second feature set is created excluding sentiment to evaluate its incremental value in prediction accuracy.

Model Training: Leveraging XGBoost for Regression

Purpose: Train High-Performance Predictive Models

The core predictive engine uses XGBoost, a powerful gradient boosting algorithm known for its speed and accuracy in regression tasks.

Key steps include:

• Hyperparameter Optimization: RandomizedSearchCV runs 50 iterations with 7-fold cross-validation to tune parameters like:

  • n_estimators
  • learning_rate
  • max_depth
  • subsample

• Two Models Are Trained:

  1. Without Sentiment: Baseline model using only technical and price features.
  2. With Sentiment: Full model incorporating sentiment scores.
• Both use the objective function reg:squarederror to minimize prediction error.

This dual-model approach allows for direct comparison of sentiment’s impact on forecasting performance.

Model Evaluation: Measuring Accuracy and Insight

Purpose: Quantify and Compare Model Performance

After training, both models generate predictions on the test set. Their performance is assessed using multiple metrics:

• MSE (Mean Squared Error)
• RMSE (Root Mean Squared Error)
• MAE (Mean Absolute Error)
• MAPE (Mean Absolute Percentage Error)
• R-squared: Indicates how much variance in price is explained by the model.

Results are printed and saved to sentiment_comparison_metrics.csv, enabling easy benchmarking.

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Additionally, feature importance plots (XGBoost_Feature_Importance_With_Sentiment.png) reveal which inputs drive predictions—offering transparency into model behavior.

Preliminary findings often show sentiment contributing meaningfully during high-volatility periods, such as regulatory announcements or macroeconomic shifts.

Combined Historical and Future Prediction Workflow

Purpose: Forecast Both Past and Future Prices

This section validates the model on recent history and projects forward:

Historical Prediction (Last 180 Days)

• Actual prices are compared against predictions from both models.
• Visualizations highlight where sentiment inclusion improves fit.

Future Forecast (Next 90 Days)

A dynamic simulation framework predicts BTC prices ahead:

• Starts with the latest 180 days of data.
• Iteratively predicts one day at a time, updating features dynamically (rolling window).
• Uses GARCH-based volatility forecasts (Normal and Student-t) to simulate realistic high/low ranges.
• Applies a drift rate based on exponential moving average of returns.
• Simulates future volume and sentiment with controlled noise.

Outputs include:

• CSV files: bitcoin_predictions_90d_with_sentiment.csv and counterpart without sentiment.
• Visualization: combined_historical_and_future_prediction.png
• Printed table of upcoming predictions

Frequently Asked Questions

Q: Can sentiment really influence Bitcoin price predictions?
A: Yes—especially during major news events. Sentiment acts as a behavioral proxy, capturing market psychology that technical indicators alone may miss.

Q: Why use GARCH alongside machine learning?
A: GARCH models volatility clustering—a hallmark of crypto markets. Integrating it with XGBoost improves realism in price range simulations.

Q: Is this model suitable for live trading?
A: While powerful, it should be validated in real-time environments and combined with risk management strategies before deployment.

Q: How often should the model be retrained?
A: Weekly or bi-weekly retraining is recommended to adapt to evolving market dynamics.

Q: What libraries are essential for running this script?
A: Key dependencies include pandas, numpy, yfinance, scikit-learn, ta, arch, and matplotlib.

Q: Can this framework be adapted for other cryptocurrencies?
A: Absolutely—by changing the ticker symbol and sourcing corresponding sentiment data, this system works for Ethereum, Solana, or any major digital asset.

Core Components Summary

This Bitcoin forecasting system combines:

• Data Science Tools: Pandas, NumPy, Scikit-learn
• Financial Libraries: yfinance (data), ta (indicators), arch (GARCH)
• Machine Learning: XGBoost for regression
• Behavioral Finance: Sentiment integration
• Visualization: Matplotlib for performance tracking

It exemplifies modern quantitative analysis—merging traditional econometrics with AI-driven prediction.

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Final Thoughts

This script represents a state-of-the-art approach to cryptocurrency forecasting. By integrating sentiment analysis, technical indicators, and hybrid modeling (XGBoost + GARCH), it delivers nuanced insights beyond simple trend-following systems.

Whether you're building a personal trading bot or researching market dynamics, this framework offers a scalable, transparent, and data-rich foundation for Bitcoin price prediction in 2025 and beyond.