Published April 7, 2025 | Version v1
Software Open

OmniEcon Nexus: Global Microeconomic Simulation Engine

Creators

Description

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.

Files

README.md

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