OmniEcon Nexus: Global Microeconomic Simulation Engine

OmniEcon Nexus is an open-source, high-performance simulation engine for global microeconomic and macroeconomic analysis. Built with advanced deep learning, agent-based modeling, and optimization techniques, it enables detailed forecasting, risk analysis, policy generation, and portfolio optimization. This system supports up to 5 million agents and is designed as a comprehensive tool for governments, researchers, and developers to explore economic dynamics.

Core Features

• Economic Forecasting: Predicts short-term and mid-term economic trends using deep learning models.
• Agent-Based Simulation: Models up to 5M agents (citizens, businesses, governments) with behavioral psychology.
• Portfolio Optimization: Optimizes asset allocation using the Sharpe ratio and real-time market data.
• Policy Generation: Automatically generates and evaluates macroeconomic policies with Q-learning.
• Risk Analysis: Assesses market volatility and systemic risk using network analysis.
• Market Psychology: Estimates PMI and agent psychological states (Fear, Greed, Complacency, Hope).

Technical Overview

Deep Learning Components

• MicroEconomicPredictor:

  • Architecture: GRU, LSTM, Transformer Encoder, and a custom QuantumResonanceLayer.
  • Configuration: Default hidden_dim=8192, num_layers=24, input_dim=72.
  • Purpose: Forecasts short-term (short_pred) and mid-term (mid_pred) economic growth.
  • Implementation: See MicroEconomicPredictor.forward() for details.

• QuantumResonanceLayer:

  • Mechanism: Combines linear transformation with sinusoidal phase shifts and layer normalization.
  • Purpose: Enhances prediction accuracy with quantum-inspired dynamics.

Agent-Based Modeling

• HyperAgent:
  • Roles: Citizens, businesses, governments.
  • Attributes: Wealth, innovation, trade flow, resilience, psychological state.
  • Behavior: Updated via interact(), influenced by market data, global context, and policies.
  • Scale: Supports 5M agents with multiprocessing (Pool).

Optimization and Policy

• Portfolio Optimization:

  • Method: Uses scipy.optimize.minimize with SLSQP to maximize Sharpe ratio.
  • Inputs: Short-term/mid-term predictions, volatility, crowd sentiment.
  • Constraints: Total weights = 1, stocks + gold ≤ 80%.
  • See: optimize_portfolio().

• Policy Generation:

  • Algorithm: Q-learning with state hashing (generate_policy()).
  • Inputs: PMI, fear/greed indices, market momentum, volatility.
  • Outputs: Policies like tax reduction, interest rate hikes, subsidies.
  • Evaluation: Assesses impact via evaluate_policy_impact() using resilience, cash flow, consumption metrics.

Network Analysis

• Systemic Risk Network:
  • Structure: Directed graph (networkx.DiGraph) tracking trade dependencies.
  • Metric: Systemic Risk Score (SRS) via calculate_systemic_risk_score() with betweenness centrality.
• Reflexive Network:
  • Storage: Policy history in reflection_network.
  • Retrieval: ANN-based (annoy) policy suggestions in suggest_reflexive_policy().

Real-Time Data Integration

• Sources:
  • Yahoo Finance (yfinance): Market momentum, volatility, commodity prices.
  • Twitter (tweepy): Crowd sentiment via hashtag analysis.
  • World Bank (requests): Historical GDP, trade, inflation.
• Fallback: Simulated data if API keys are unavailable.

Requirements

• Python: 3.8+
• Libraries:
  • Core: numpy, cupy, pandas, torch, scipy, networkx
  • Data Access: yfinance, tweepy, requests
  • Modeling: hmmlearn, filterpy, scikit-learn, annoy
• Hardware:
  • Minimum: Multi-core CPU, 16GB RAM (small-scale).
  • Recommended: GPU (e.g., NVIDIA A100), 128GB+ RAM, 1TB SSD (5M agents).
• Installation:

  pip install numpy cupy-cuda11x pandas torch yfinance hmmlearn scipy networkx tweepy filterpy scikit-learn annoy requests
  

Usage

Prepare Input Data

Prepare Input Data

nations = [
    {
        "name": "Vietnam",
        "observer": {
            "GDP": 450e9,
            "population": 100e6
        },
        "space": {
            "trade": 0.8,
            "inflation": 0.04,
            "institutions": 0.7,
            "cultural_economic_factor": 0.85
        }
    }
]

📤 Outputs

Files

• Format: CSV / JSON
• Example: omniecon_nexus_[nation].csv

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📈 Practical Outputs

• Forecasts:

  • Short-term and mid-term GDP growth
  • Volatility estimates across sectors

• Policy Recommendations:

  • Dynamic strategies for tax, subsidies, or interest rates
  • Tailored to macroeconomic conditions and market sentiment

• Portfolio Allocations:

  • Optimized ratios of stocks, bonds, gold, and cash
  • Based on Sharpe ratio maximization using forward-looking indicators

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🧠 Advanced Capabilities

• Graph Evolution:

  • System graph updates every 200 simulation steps
  • Captures agent-state and policy dynamics over time

• Macro-Strategy Detection:

  • Detects emergent policy clusters and successful intervention patterns
  • Threshold: Success score > 0.025

• Graph Compression:

  • Automatically compresses networks larger than 50,000 nodes
  • Output: Serialized .pkl files for long-term storage and replay

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📌 Notes

• The engine performs best with real-world data (e.g., national statistics, market feeds).
• In the absence of raw data, it can simulate behavior using probabilistic assumptions.
• Architecture is modular, allowing custom extensions and real-time integrations.
• Supports distributed deployment on cloud or on-premise environments.

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📜 License

Licensed under the Apache License 2.0.
See the LICENSE file for full terms and conditions.

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🤝 Contributions

We welcome your ideas and contributions!
Feel free to submit pull requests or open issues to improve the engine further.

Let’s evolve economic simulation together 🌍.