A comprehensive machine learning system for stock price prediction and volatility forecasting using LSTM neural networks with integrated sentiment analysis from financial news.

• Overview
• Features
• Architecture
• Installation
• Usage
• Methodology
• Model Details
• Results
• Configuration
• Risk Analysis
• Disclaimer

This project implements an advanced stock forecasting system that combines:

• LSTM Neural Networks: For sequential price pattern recognition
• Sentiment Analysis: Real-time news sentiment from Finnhub API
• Multivariate Analysis: Correlation with other assets (AAPL, SPY)
• Volatility Forecasting: Separate LSTM model for risk assessment
• Technical Indicators: Price, volume, and derived features

The system provides both short-term price predictions and volatility forecasts with integrated alert systems.

• Dual LSTM Models: Separate networks for price and volatility prediction
• Real-time Sentiment Analysis: Integration with Finnhub API for news sentiment
• Multivariate Capability: Optional inclusion of correlated assets
• Intelligent Synthetic Sentiment: Fallback system when news data is limited
• Comprehensive Metrics: MAE, RMSE, MAPE, Directional Accuracy
• Risk Assessment: Volatility forecasting with confidence intervals
• Alert System: Price and volatility threshold monitoring
• Visual Analytics: Comparative plots and correlation matrices

Yahoo Finance Data → Feature Engineering → Sentiment Analysis → 
LSTM Price Model → LSTM Volatility Model → Ensemble Forecasting → Alert System

• Price Prediction: LSTM(72) → Dropout → LSTM(48) → Dropout → Dense(1)
• Volatility Prediction: LSTM(50) → Dropout → LSTM(30) → Dropout → Dense(1)
• Sentiment Integration: TextBlob + VADER hybrid analysis

# Core data science libraries
pip install numpy pandas matplotlib seaborn scikit-learn
# Financial data
pip install yfinance finnhub-python
# Deep learning
pip install tensorflow
# Natural Language Processing
pip install nltk textblob
# HTTP requests
pip install requests

FINNHUB_KEY = "your_finnhub_api_key_here" # Get from https://finnhub.io

# Core parameters
ticker = "TSLA"
data_start = "2023-01-01"
USE_SENTIMENT = True # Enable/disable sentiment analysis
USE_MULTIVARIATE = False # Include other assets
# Model parameters
SEQ_LEN = 60 # Historical sequence length
FUTURE_DAYS = 90 # Forecast horizon
TEST_RATIO = 0.2 # Test set size

1. Data Collection:

   # Automatically downloads historical data from Yahoo Finance
   df_main = yf.download(ticker, start=data_start, progress=False)

2. Sentiment Analysis:

   • Real-time news from Finnhub API
   • Hybrid TextBlob + VADER sentiment scoring
   • Intelligent synthetic sentiment fallback

3. Feature Engineering:

   • Price and volume data
   • Log returns and rolling statistics
   • Sentiment scores integration

4. Model Training:

   • Automatic LSTM architecture selection
   • Early stopping and validation
   • Separate models for price and volatility

5. Forecast Generation:

   • Next-day price prediction
   • 30-day volatility forecast
   • Trend analysis and alerts

• Price Features: Close prices, log returns, percentage changes
• Volume Features: Trading volumes with normalization
• Technical Indicators: Rolling means, standard deviations
• Sentiment Features: News sentiment scores (-1 to +1 scale)

def analyze_sentiment_enhanced(text):
 """Enhanced sentiment analysis with financial terminology"""
 # Combines TextBlob, VADER, and financial keyword analysis
 # Returns normalized score between -1.0 and +1.0

Price Model:

Input: (60, n_features) → LSTM(72) → Dropout(0.2) → 
LSTM(48) → Dropout(0.2) → Dense(1)

Volatility Model:

Input: (30, 1) → LSTM(50) → Dropout(0.2) → 
LSTM(30) → Dropout(0.2) → Dense(1)

• Input Sequence: 60 days of historical data
• Features: Price, volume, sentiment, correlated assets
• Output: Next-day price prediction
• Loss Function: Mean Squared Error (MSE)
• Optimizer: Adam (lr=0.001)

• Input Sequence: 30 days of volatility data
• Calculation: Rolling standard deviation of log returns
• Output: Future volatility estimates
• Application: Risk assessment and position sizing

• Data Source: Finnhub company news API
• Methods: TextBlob (lexicon-based) + VADER (rule-based)
• Fallback: Synthetic sentiment from price movements
• Normalization: Tanh activation for -1 to +1 range

• Price Prediction: MAE, RMSE, MAPE, Directional Accuracy
• Volatility Prediction: MSE on normalized volatility
• Sentiment Analysis: Correlation with price movements

📌 Previsão preço TSLA próximo pregão: 245ドル.32
Preço Atual: 240ドル.50
Variação: 4ドル.82 (+2.00%)
Tendência: 📈 FORTE ALTA
🎯 PREVISÃO DE VOLATILIDADE (Próximos 5 dias):
Volatilidade Atual: 0.023456 (2.3456%)
Volatilidade Prevista: 0.028901 (2.8901%)
Mudança: +0.005445 (+23.22%)
Tendência: 📈 AUMENTO SIGNIFICATIVO DE VOLATILIDADE

# Data Parameters
ticker = "TSLA"
extra_tickers = ["AAPL", "SPY"] # Correlated assets
data_start = "2023-01-01"
# Model Parameters
SEQ_LEN = 60 # Price model sequence length
SEQ_LEN_VOL = 30 # Volatility model sequence length
FUTURE_DAYS = 90 # Forecast horizon
TEST_RATIO = 0.2 # Test set percentage
# Feature Flags
USE_SENTIMENT = True # Enable sentiment analysis
USE_MULTIVARIATE = False # Use multiple assets

# Price Alerts
HIGH_PRICE_THRESHOLD = df_full[price_col].quantile(0.75)
LOW_PRICE_THRESHOLD = df_full[price_col].quantile(0.25)
# Volatility Alerts
HIGH_VOL_THRESHOLD = df_vol['Volatility'].quantile(0.75)
LOW_VOL_THRESHOLD = df_vol['Volatility'].quantile(0.25)

• Input: 30-day rolling volatility
• Output: 30-day future volatility predictions
• Application: Risk management and position sizing

• Price Alerts: High/Low price levels based on historical percentiles
• Volatility Alerts: Significant changes in market volatility
• Trend Alerts: Strong upward/downward movements

(Optional extension) For probabilistic forecasting and confidence intervals.

Important: This tool is for educational and research purposes only. The forecasts are statistical estimates based on historical data and should not be considered financial advice.

• Always conduct your own research and due diligence
• Consider macroeconomic factors and company fundamentals
• Implement proper risk management strategies
• Past performance does not guarantee future results
• The developers are not responsible for investment decisions made using this tool

# Example: Add technical indicators
def add_technical_indicators(df):
 df['SMA_20'] = df['Close'].rolling(20).mean()
 df['RSI'] = calculate_rsi(df['Close'])
 df['MACD'] = calculate_macd(df['Close'])
 return df

# Add custom financial terminology
custom_positive_terms = {
 'breakout': 0.8, 'momentum': 0.6, 'oversold': 0.3
}
custom_negative_terms = {
 'resistance': -0.5, 'distribution': -0.6
}

# Deeper LSTM architecture
model = Sequential([
 LSTM(128, return_sequences=True, input_shape=(SEQ_LEN, n_features)),
 Dropout(0.3),
 LSTM(64, return_sequences=True),
 Dropout(0.3),
 LSTM(32),
 Dropout(0.2),
 Dense(1)
])

For issues and questions:

1. API Keys: Ensure Finnhub API key is valid
2. Data Availability: Check Yahoo Finance data for your ticker
3. Dependencies: Verify all packages are correctly installed
4. Memory Issues: Reduce sequence length or historical period for large datasets

• Transformer architecture for better sequence modeling
• Integration with fundamental analysis data
• Portfolio optimization capabilities
• Real-time trading interface
• Advanced risk metrics (VaR, CVaR)
• Multi-timeframe analysis

Note: This system is designed for quantitative analysis and requires understanding of financial markets, machine learning, and Python programming. Always test with paper trading before live deployment.

stock-prediction, lstm, sentiment-analysis, time-series-forecasting, 
financial-machine-learning, python, tensorflow, finnhub-api, 
volatility-forecasting, algorithmic-trading, quantitative-finance,
risk-management, neural-networks, nltk, yfinance