OmniEcon Nexus: Global Microeconomic Simulation Engine
Authors: Vi Nhat Son
Date: 2025-04-07 00:00:00
Proceeding: Independent publication 2025 4
Tasks: Data Integration, Portfolio Optimization, Q-Learning

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.