# Financial Intelligence System An institutional-grade financial intelligence and advisory platform that generates multi-layered market intelligence, signals, and forward-looking insights using only free APIs. ## Positioning **This is NOT a trading platform.** It is a sophisticated financial intelligence system designed to: - Generate market insights based on technical, fundamental, and macro indicators - Provide opportunity signals with probability-based evaluation - Assess risk and exposure - Generate forward-looking scenarios based on economic conditions - Offer strategic allocation suggestions for short, medium, and long-term horizons **All outputs use advisory language:** "consider", "potential opportunity", "risk scenario" - never "execute trade". ## Features ### Core Intelligence - **Composite Scoring System**: Unified 0-100 score (30% Technical + 25% Momentum + 20% Macro + 15% Fundamental + 10% ML) - **40+ Signal Analysis**: Macro, Technical, Fundamental, Sentiment, and Management categories - **Investment Opportunities**: Scan multiple assets for high-probability setups - **Market Overview**: Track major indices and macro indicators - **Smart Scoring**: Weighted signal scoring with conflict detection - **Forecasts**: Short-term (0-3 months) and long-term (1-5 years) predictions ### Advanced Analytics - **Momentum Prediction**: ROC analysis, momentum oscillator, trend strength, velocity, volume momentum - **Chart Pattern Recognition**: Double top/bottom, head & shoulders, triangles, flags - **Support/Resistance Levels**: Automatic identification with strength scoring - **Investment Horizon Analysis**: Separate short-term (3-6 months) and long-term (3-5 years) opportunities - **Action Advisor**: Buy/sell/hold recommendations with timing advice ### Multi-Asset Coverage - **Equities**: US and Indian stocks with full fundamental analysis - **Bonds**: Government and corporate bonds with yield analysis - **Commodities**: Gold, oil, metals, agriculture with seasonal patterns - **Crypto**: Bitcoin, Ethereum with on-chain metrics - **Forex**: Currency pairs with rate differential analysis ### Bank-Level Macro Analysis - **Interest Rates**: Fed, ECB, BOE, BOJ policy rates and stances - **Inflation**: CPI, PPI trends and expectations - **Central Bank Policies**: Hawkish/dovish stances, meeting schedules - **Yield Curve**: 2y/10y spread, recession indicators - **Risk Sentiment**: VIX analysis, risk-on/risk-off indicators - **Economic Cycle**: Expansion, peak, contraction, trough identification ### Scenario Analysis - **Forward-Looking Scenarios**: Base case, bull case, bear case with probabilities - **Macro Scenarios**: Disinflation, stagflation, growth scare, policy error - **IF-THEN Logic**: "IF inflation rises → THEN bonds fall, USD rises" - **Cross-Asset Implications**: Each scenario's impact on all asset classes ### Portfolio Management - **Asset Allocation Models**: 60/40, Risk Parity, All-Weather, Tactical, Economic Cycle - **Diversification Analysis**: Correlation matrix, concentration risk - **Portfolio Optimization**: Mean-variance optimization, rebalancing - **Risk Assessment**: VaR calculation, exposure analysis, position sizing ### Sector Analysis - **US Sectors**: Technology, Financials, Healthcare, Energy, etc. - **Indian Sectors**: NIFTY IT, Bank, FMCG, Auto, Pharma - **Sector Rotation**: Identify overweight/underweight sectors - **Allocation Recommendations**: Strategic sector positioning ### Monitoring - **Alert System**: Price alerts, signal alerts, pattern detection - **Risk Management**: Trade validation, exposure limits ### Free APIs Only - Uses yfinance (unlimited) and Alpha Vantage (free tier) - No paid API subscriptions required ## Architecture ### Intelligence Platform Layers ``` Data Sources ├── Market Data (price, volume) - yfinance ├── Macro Data (rates, inflation) - yfinance ├── Fundamentals (earnings) - yfinance, Alpha Vantage └── Alternative Data (sentiment) - yfinance news ↓ Processing Layers ├── Technical Engine (indicators, patterns) ├── Fundamental Engine (valuation, growth) ├── Macro Engine (rates, central banks, cycle) └── ML Prediction Layer (probabilistic signals) ↓ Insight Engine ├── Signal Scoring (composite 0-100) ├── Scenario Analysis (IF-THEN logic) └── Risk Evaluation (VaR, exposure) ↓ Output ├── Market Insights ├── Opportunity Signals ├── Risk Alerts └── Forward Scenarios ``` ### Components #### Core Intelligence 1. **Composite Scorer** (`composite_scorer.py`) - Unified 0-100 score - Weights: 30% Technical + 25% Momentum + 20% Macro + 15% Fundamental + 10% ML - Trend, momentum, macro alignment, fundamental strength assessments - Insight generation with strategic considerations 2. **Macro Analyzer** (`macro_analyzer.py`) - Interest rate analysis (Fed, ECB, BOE, BOJ) - Inflation trends (CPI, PPI) - Central bank policy stances - Yield curve dynamics (2y/10y spread) - Risk sentiment (VIX, risk-on/risk-off) - Economic cycle identification - Forward-looking macro scenarios 3. **Multi-Asset Analyzer** (`multi_asset_analyzer.py`) - Equities (stocks) - Bonds (government, corporate) - Commodities (gold, oil, metals, agriculture) - Crypto (BTC, ETH) - Forex (currency pairs) - Asset-class-specific analysis 4. **Scenario Analyzer** (`scenario_analyzer.py`) - Base case, bull case, bear case generation - Probability-based scenarios - IF-THEN forward logic - Cross-asset implications - Key monitoring points 5. **Correlation Analyzer** (`correlation_analyzer.py`) - Cross-asset correlation matrix - Rolling correlation analysis - Regime change detection - Diversification assessment - Concentration risk identification 6. **Asset Allocator** (`asset_allocator.py`) - 60/40 portfolio - Risk parity - All-weather portfolio - Tactical asset allocation - Economic cycle positioning - Strategy comparison #### Core Analysis (Existing) 7. **Data Layer** (`data_layer.py`) - yfinance for stock data (unlimited, free) - Alpha Vantage for fundamentals (25 requests/day free tier) - News sentiment via yfinance news 8. **Signal Engine** (`signal_engine.py`) - 40 signals (20 bullish, 20 bearish) - Categories: Macro, Technical, Fundamental, Sentiment, Management 9. **Technical Indicators** (`technical_indicators.py`) - Moving averages (50/200 day) - RSI, MACD, ADX, Bollinger Bands - Support/Resistance levels - ATR for volatility 10. **Scoring Model** (`scoring_model.py`) - Weighted score = Strength × Confidence - Conflict detection and adjustment 11. **Decision Logic** (`decision_logic.py`) - Net score interpretation - Verdict generation (Strong Buy to Strong Sell) 12. **Context Integration** (`context_integration.py`) - Geopolitical context adjustments - Sentiment credibility weighting #### Advanced Analytics 13. **Momentum Predictor** (`momentum_predictor.py`) - Rate of Change (ROC) analysis - Momentum oscillator - Trend strength and velocity - Volume momentum 14. **Chart Analyzer** (`chart_analyzer.py`) - Chart pattern recognition - Support/resistance level detection - Trend analysis - Volume analysis 15. **Investment Horizon Analyzer** (`investment_horizon_analyzer.py`) - Short-term opportunities (3-6 months) - Long-term opportunities (3-5 years) - Entry/target/stop-loss levels 16. **Action Advisor** (`action_advisor.py`) - Buy/sell/hold recommendations - Timing advice - Position sizing #### Market Coverage 17. **Indian Market Analyzer** (`indian_market_analyzer.py`) - NSE/BSE stock analysis - Indian sector indices 18. **Sector Analyzer** (`sector_analyzer.py`) - US sector ETFs - Indian sector indices - Sector rotation signals #### Portfolio Management 19. **Portfolio Optimizer** (`portfolio_optimizer.py`) - Mean-variance optimization - Rebalancing recommendations - Diversification assessment 20. **Risk Manager** (`risk_manager.py`) - Position sizing - Stop-loss calculation - Portfolio risk assessment - VaR calculation 21. **Backtester** (`backtester.py`) - Strategy backtesting - Performance metrics 22. **Alert System** (`alert_system.py`) - Price alerts - Signal alerts - Pattern alerts #### API 23. **FastAPI Server** (`api.py`) - REST API endpoints - JSON and Markdown output formats - CORS enabled - Interactive Swagger documentation ## Setup ### Prerequisites - Python 3.8 or higher - pip ### Installation 1. Clone the repository: ```bash cd quant-investment-ai ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Configure environment variables (optional): ```bash cp .env.example .env ``` Edit `.env` and add your free API keys: - **Alpha Vantage** (optional): Get free key at https://www.alphavantage.co/support/#api-key - Without this, the system falls back to yfinance for fundamentals - **NewsAPI** (optional): Get free key at https://newsapi.org/register - Without this, uses yfinance news Note: The system works with just yfinance (no API keys required). Alpha Vantage and NewsAPI are optional enhancements. ## Usage ### Start the Server ```bash python api.py ``` The server will start at `http://localhost:8000` ### API Endpoints #### 1. Analyze a Company **POST** `/analyze` ```json { "symbol": "AAPL", "format": "json" } ``` **GET** `/analyze/{symbol}?format=json` ``` GET /analyze/AAPL?format=json ``` **Response:** ```json { "symbol": "AAPL", "company": { "name": "Apple Inc.", "sector": "Technology", "current_price": 150.25, ... }, "scores": { "bullish_score": 45.5, "bearish_score": 20.3, "net_score": 25.2, "signal_ratio": 0.69 }, "bullish_indicators": [...], "bearish_indicators": [...], "forecast": { "short_term": {...}, "long_term": {...} }, "key_drivers": {...}, "verdict": { "verdict": "Buy", "direction": "Bullish", "probability": 0.65 } } ``` #### 2. Market Overview **GET** `/market-overview` Returns major indices (S&P 500, NASDAQ, DOW, NIFTY) and macro indicators. #### 3. Scan Opportunities **POST** `/scan` ```json { "symbols": ["AAPL", "GOOGL", "MSFT", "TSLA"] } ``` **GET** `/opportunities?symbols=AAPL,GOOGL,MSFT,TSLA` Returns symbols sorted by net score (highest first). #### 1. Composite Intelligence (NEW) **GET** `/intelligence/{symbol}` ``` GET /intelligence/AAPL ``` Returns composite score (0-100) with component breakdown: - Technical score (30%) - Momentum score (25%) - Macro alignment (20%) - Fundamental strength (15%) - ML probability (10%) **Response:** ```json { "composite_score": { "total_score": 74, "technical_score": 78, "momentum_score": 72, "macro_score": 65, "fundamental_score": 80, "ml_probability": 68, "trend": "bullish", "momentum": "strong", "macro_alignment": "aligned", "fundamental_strength": "strong", "confidence": 0.72 }, "insight": { "assessment": "Favorable setup with moderate strength across factors", "strategic_consideration": "Potential opportunity exists, consider selective exposure with risk management", "key_drivers": ["Strong technical trend", "Positive momentum", "Solid fundamentals"], "key_risks": [] } } ``` #### 2. Macro Intelligence (NEW) **GET** `/macro` Returns comprehensive macro analysis: - Interest rates (Fed, ECB, BOE, BOJ) - Inflation trends - Central bank policies - Yield curve dynamics - Risk sentiment (VIX) - Economic cycle phase **GET** `/macro/scenarios` Returns forward-looking macro scenarios: - Disinflation (40% prob) - Stagflation (15% prob) - Growth scare (25% prob) - Policy error (10% prob) - Supply shock (10% prob) Each scenario includes cross-asset implications. #### 3. Multi-Asset Analysis (NEW) **GET** `/multi-asset/{symbol}` Analyze any asset class: - Equities: AAPL, GOOGL, etc. - Bonds: TLT, IEF, LQD - Commodities: GLD, USO, SLV - Crypto: BTC-USD, ETH-USD - Forex: EURUSD=X, GBPUSD=X **Response:** ```json { "asset": "BTC-USD", "asset_class": "crypto", "current_price": 45000, "technical": {...}, "volatility": 0.85, "insight": "Positive trend with high volatility - size positions accordingly", "strategic_consideration": "High volatility asset - limit allocation, use risk management" } ``` #### 4. Scenario Analysis (NEW) **GET** `/scenarios/{symbol}` Returns forward-looking scenarios for a specific asset: - Base case (most likely) - Bull case (optimistic) - Bear case (pessimistic) - Recommended positioning - Key monitoring points #### 5. Correlation Analysis (NEW) **GET** `/correlation?assets=SPY,TLT,GLD,BTC-USD` Returns cross-asset correlation matrix with: - Average correlation - Highest/lowest correlations - Diversification opportunities - Concentration risks **POST** `/diversification` ```json { "holdings": {"AAPL": 0.3, "GOOGL": 0.2, "TLT": 0.3, "GLD": 0.2} } ``` Assesses portfolio diversification and provides recommendations. #### 6. Asset Allocation (NEW) **GET** `/allocation/{strategy}` Strategies: - `60_40` - Classic 60/40 portfolio - `risk_parity` - Equal risk contribution - `all_weather` - All-weather portfolio - `tactical` - Dynamic based on signals - `economic_cycle` - Based on current cycle **GET** `/allocation/compare` Compare all strategies by expected return, volatility, and Sharpe ratio. #### 7. Risk Sentiment (NEW) **GET** `/risk-sentiment` Returns current risk-on/risk-off indicators: - VIX level - Sentiment interpretation - Asset implications #### 8. Enhanced Analysis **GET** `/analyze/{symbol}/enhanced` Comprehensive analysis including: - Base signal analysis - Momentum prediction - Chart pattern recognition - Action advice with timing - Short-term and long-term investment opportunities #### 9. Indian Market Analysis **GET** `/indian/analyze/{symbol}` ``` GET /indian/analyze/RELIANCE.NS ``` **GET** `/indian/overview` - NIFTY 50, NIFTY Bank, NIFTY IT, sector indices #### 10. Sector Analysis **GET** `/sectors/rotation?market=US` - Analyze sector rotation across US or Indian markets **GET** `/sectors/recommendations?market=US` - Get sector allocation recommendations #### 11. Portfolio Management **POST** `/portfolio/optimize` ```json { "holdings": {"AAPL": 0.3, "GOOGL": 0.2, "MSFT": 0.15}, "expected_returns": {"AAPL": 0.15, "GOOGL": 0.12, "MSFT": 0.10} } ``` **POST** `/portfolio/risk` ```json { "holdings": {"AAPL": 100, "GOOGL": 50}, "prices": {"AAPL": 150.0, "GOOGL": 120.0}, "volatilities": {"AAPL": 0.25, "GOOGL": 0.30} } ``` **POST** `/position-size` ```json { "portfolio_value": 100000, "entry_price": 150.0, "stop_loss": 140.0, "confidence": 0.7 } ``` #### 12. Backtesting **GET** `/backtest/{symbol}?period=1y` - Backtest strategy on historical data #### 13. Interactive API Documentation Visit `http://localhost:8000/docs` for interactive Swagger UI. ## Example Usage ### Python Script ```python from analyzer import FinancialAnalyzer # Initialize analyzer analyzer = FinancialAnalyzer() # Basic analysis result = analyzer.analyze_company("AAPL", format="json") print(f"Verdict: {result['verdict']['verdict']}") print(f"Net Score: {result['scores']['net_score']}") # Enhanced analysis with momentum and chart patterns enhanced = analyzer.analyze_company_enhanced("AAPL") print(f"Momentum: {enhanced['momentum']['current_momentum']['direction']}") print(f"Action: {enhanced['action_advice']['action']}") # Indian market analysis indian_result = analyzer.analyze_indian_stock("RELIANCE.NS") print(f"Indian Stock: {indian_result.get('name', 'N/A')}") # Sector rotation sectors = analyzer.analyze_sector_rotation(market="US") print(f"Top Sector: {sectors['top_sectors'][0]['sector']}") # Portfolio optimization holdings = {"AAPL": 0.3, "GOOGL": 0.2, "MSFT": 0.15} returns = {"AAPL": 0.15, "GOOGL": 0.12, "MSFT": 0.10} optimization = analyzer.optimize_portfolio(holdings, returns) print(f"Optimized Sharpe: {optimization['optimized_portfolio']['sharpe_ratio']}") # Position sizing position = analyzer.calculate_position_size(100000, 150, 140, 0.7) print(f"Recommended shares: {position['shares']}") print(f"Position size: {position['position_size_pct']}%") # Backtesting backtest = analyzer.backtest_strategy("AAPL", period="1y") print(f"Strategy Return: {backtest['total_return']}%") print(f"Sharpe Ratio: {backtest['sharpe_ratio']}") ``` ### cURL ```bash # Analyze Apple curl -X POST "http://localhost:8000/analyze" \ -H "Content-Type: application/json" \ -d '{"symbol": "AAPL", "format": "json"}' # Enhanced analysis curl "http://localhost:8000/analyze/AAPL/enhanced" # Indian market analysis curl "http://localhost:8000/indian/analyze/RELIANCE.NS" # Sector rotation curl "http://localhost:8000/sectors/rotation?market=US" # Portfolio optimization curl -X POST "http://localhost:8000/portfolio/optimize" \ -H "Content-Type: application/json" \ -d '{"holdings": {"AAPL": 0.3, "GOOGL": 0.2}, "expected_returns": {"AAPL": 0.15, "GOOGL": 0.12}}' # Position sizing curl -X POST "http://localhost:8000/position-size" \ -H "Content-Type: application/json" \ -d '{"portfolio_value": 100000, "entry_price": 150, "stop_loss": 140, "confidence": 0.7}' # Backtesting curl "http://localhost:8000/backtest/AAPL?period=1y" ``` ## Signal Categories ### Macro (5 bullish, 5 bearish) - Interest rates, liquidity, fiscal stimulus, currency, sector tailwinds ### Technical (5 bullish, 5 bearish) - Golden/Death cross, RSI, MACD, breakouts, trend strength, volatility ### Fundamental (5 bullish, 5 bearish) - Revenue growth, margins, cash flow, debt levels, ROE/ROIC ### Sentiment (3 bullish, 3 bearish) - Institutional flow, earnings revisions, positioning, news sentiment ### Management (2 bullish, 2 bearish) - Leadership track record, strategic expansion, capital allocation ## Scoring System - **Strength**: 0-5 scale per signal - **Confidence**: 0-1 scale (adjusted for geopolitical risk) - **Weighted Score**: Strength × Confidence - **Net Score**: Bullish Score - Bearish Score **Verdict Scale:** - Net Score > +25: Strong Buy - Net Score +10 to +25: Buy - Net Score -10 to +10: Hold - Net Score -10 to -25: Sell - Net Score < -25: Strong Sell ## Free API Limits | API | Free Tier | Limit | |-----|-----------|-------| | yfinance | Unlimited | None | | Alpha Vantage | Free | 25 requests/day, 5/minute | | NewsAPI | Free | 100 requests/day | **Note**: The system is designed to work with just yfinance. Alpha Vantage and NewsAPI are optional for enhanced data. ## Supported Markets - US Stocks (e.g., AAPL, GOOGL, TSLA) - Indian Stocks (e.g., RELIANCE.NS, TCS.NS) - Global markets (use appropriate ticker suffixes) ## Project Structure ``` quant-investment-ai/ ├── api.py # FastAPI server with 15+ endpoints ├── analyzer.py # Main analyzer orchestrator ├── config.py # Configuration ├── requirements.txt # Dependencies ├── .env.example # Environment variables template ├── test_analyzer.py # Test suite │ ├── Core Analysis ├── data_layer.py # Data fetching (yfinance, Alpha Vantage) ├── signal_engine.py # Signal generation (40 signals) ├── technical_indicators.py # Technical calculations ├── scoring_model.py # Scoring logic ├── decision_logic.py # Decision making ├── output_formatter.py # Output formatting ├── context_integration.py # Geopolitical/sentiment context │ ├── Advanced Analytics ├── momentum_predictor.py # Momentum prediction and change detection ├── chart_analyzer.py # Chart pattern recognition and analysis ├── investment_horizon_analyzer.py # Short/long-term opportunities ├── action_advisor.py # Buy/sell/hold with timing advice │ ├── Market Coverage ├── indian_market_analyzer.py # NSE/BSE specialized analysis ├── sector_analyzer.py # Sector rotation and allocation │ ├── Portfolio Management ├── portfolio_optimizer.py # Portfolio optimization and rebalancing ├── risk_manager.py # Risk management and position sizing ├── backtester.py # Strategy backtesting ├── alert_system.py # Alert system for monitoring │ ├── Configuration ├── engine/ # Signal definitions (markdown) │ ├── signal_definitions.md │ ├── scoring_model.md │ └── decision_logic.md ├── data/ # Context data │ ├── sentiment_rules.md │ └── geopolitical_context.md ├── templates/ # Output templates │ └── output_format.md └── config/ # Investment profile └── investment_profile.json ``` ## Development ### Running Tests ```bash # Test with a single company python -c "from analyzer import FinancialAnalyzer; print(FinancialAnalyzer().analyze_company('AAPL'))" # Test market overview python -c "from analyzer import FinancialAnalyzer; print(FinancialAnalyzer().get_market_overview())" ``` ## License MIT ## Disclaimer This tool is for educational and informational purposes only. Not financial advice. Always do your own research before making investment decisions. # Finance