📊 Liquidity Risk Intelligence Platform

This platform analyses market data to detect potential liquidity crises in London equities (LSE) and provides actionable RED/AMBER/GREEN alerts with trading recommendations.

✨ Features

• Real-time Risk Monitoring: Continuous analysis of liquidity ratios and volatility spikes
• Alerting System:
  • 🔴 RED (≥85% risk): Immediate liquidation recommended
  • 🟡 AMBER (≥70% risk): Reduce exposure advised
  • 🟢 GREEN (<70% risk): Normal monitoring
• Multi-Asset Analysis: Simultaneous monitoring of TESCO (TSCO.L) and BP (BP.L) liquidity
• Interactive Dashboard: Real-time visualisation of liquidity ratios, risk scores and market indicators
• Automated Recommendations: Actionable trading suggestions based on risk thresholds
• SQL-Powered Feature Engineering: Production-grade liquidity metrics calculated using SQL window functions
• Transmission Latency Analysis: Measures market reaction time to interest rate shocks

🚀 Quick Start

# Clone the repository
git clone https://github.com/yourusername/liquidity-risk-intelligence.git
cd liquidity-risk-intelligence

# Install dependencies
pip install -r requirements.txt

# Run the platform
python liquidity_risk_tester.py

📁 Project Structure

liquidity-risk-intelligence/
├── main.py                     # Main application entry point
├── config.py                   # Configuration module for Liquidity Risk
├── dashboard.py                # Dashboard module for liquidity metrics
├── data_engine.py              # Module for data handling
├── decision_engine.py          # Decision engine for liquidity risk recommendations
├── risk_analyser.py            # Risk Analyser Module for liquidity crisis prediction
├── utils.py                    # Utility functions for terminal management
├── requirements.txt            # Python dependencies
├── LICENSE
└── README.md

⚙️ Configuration

Edit these flags in config.py:

TEST_MODE = True      # Set to False for real market analysis (uses simulated risk scores when True)
SHOW_DASHBOARD = True # Toggle graphical dashboard display
VERBOSE = True        # Show detailed progress messages

💡 Key Insights

• Transmission Latency: Markets take approximately 7.75 days (median: 6 days) to reach 50% of maximum response to interest rate shocks
• Volatility Impact: Rate shocks increase volatility by an average of +30.84%, though correlation with shock magnitude is weak (r ≈ -0.095)
• Risk Thresholds:
  • Liquidity ratio < 0.4 triggers crisis conditions
  • 70/30 weighting: 70% liquidity ratio + 30% volatility change

📈 Sample Output

============================================================
LIQUIDITY RISK INTELLIGENCE PLATFORM
============================================================
Real-time liquidity crisis detection system

⚠️ WARNING: THIS IS A DEMONSTRATION SYSTEM
   Not for actual trading decisions

[PHASE 1] FETCHING REAL MARKET DATA
  • Downloading TESCO (TSCO.L) data from 2019-01-29 to 2024-01-29
  • Downloading BP (BP.L) data
  • Downloading FTSE 100 (^FTSE) data
  • Liquidity features created successfully!

[PHASE 2] ANALYSING LIQUIDITY CONDITIONS
  • Analysed 1,267 trading days of liquidity data
  • Detected 10 historical crisis events

[PHASE 3] GENERATING LIQUIDITY RECOMMENDATION
============================================================
LIQUIDITY ALERT - AMBER LEVEL
============================================================
TIMESTAMP:    2024-01-29 14:32:17 UTC
SECURITY:     BP.L/TSCO.L
RISK SCORE:   78.45%
RECOMMENDATION: REDUCE EXPOSURE | Buy put options on BP.L/TSCO.L
CODE: LIQ_RISK AMBER 78%
============================================================

🛠️ Technologies Used

• Python 3.8+: Core application logic
• yfinance: Real market data retrieval from Yahoo Finance
• SQLite3: Embedded time-series database storage
• pandas & NumPy: Financial time-series manipulation
• matplotlib: Professional visualisations
• scikit-learn (future): ML model integration for crisis prediction

⚠️ Important Notice

This is a demonstration/educational system only. The risk scores are simulated and NOT based on a fully validated production model. DO NOT use this system for actual trading decisions. Always consult with licensed financial advisors before making investment decisions.

📚 References

• Federal Reserve FOMC Calendar: https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm
• Bloomberg Terminal Documentation: Proprietary (simulated interface for educational purposes)

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Developed with ❤️ for educational purposes in Applied AI and Machine Learning
Inspired by Bloomberg's AIMS (Automated Investment Management System) platform