Stock Price Prediction
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Stock price prediction using machine learning involves using historical stock market data and various predictive models to estimate future stock prices. Here’s an overview of the process:
1. Data Collection
- Obtain historical stock price data (Open, High, Low, Close, Volume).
- Additional features: moving averages, RSI, MACD, news sentiment, financial reports.
- Sources: Yahoo Finance, Alpha Vantage, Quandl, Bloomberg.
2. Data Preprocessing
- Cleaning: Remove null values, handle missing data.
- Normalization: Scale data using MinMaxScaler or StandardScaler.
- Feature Engineering: Create new features like moving averages, Bollinger Bands.
- Train-Test Split: Typically 80%-20% or use time series split.
3. Machine Learning Models for Stock Prediction
Traditional Models
- Linear Regression: Predicts price based on historical trends.
- Random Forest: Captures non-linear relationships in stock data.
- XGBoost: Boosted decision tree model for better accuracy.
Deep Learning Models
- LSTM (Long Short-Term Memory): Captures long-term dependencies in time-series data.
- GRU (Gated Recurrent Unit): A simpler alternative to LSTM.
- Transformer Models: Advanced NLP-based models for sequential data.
Hybrid Approaches
- Combine technical indicators with news sentiment analysis.
- Use an ensemble of models to improve prediction accuracy.
4. Evaluation Metrics
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- R-Squared (R²) for model performance.
5. Deployment & Real-Time Prediction
- Deploy models using Flask/FastAPI for real-time predictions.
- Integrate with stock trading platforms or dashboards.
Challenges & Considerations
- Market Volatility: Stocks are influenced by unpredictable events.
- Overfitting: Avoid fitting too closely to past data.
- Data Quality: Noisy data can reduce accuracy.
- Regulatory & Ethical Concerns: Ensure compliance with financial regulations.