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Agentic AI-Powered Wavelet Financial Network Analysis

A revolutionary framework combining Agent-Based Modeling (ABM) with Wavelet Quantile Transfer Entropy (WaveQTE) analysis for comprehensive financial network analysis. Features autonomous AI agents, dynamic spillover detection, multi-asset analysis, and publication-quality visualizations.

🚀 Features

🤖 Autonomous AI Capabilities

  • Self-optimizing AI agents with adaptive parameter selection
  • Intelligent pattern recognition and insight generation
  • Continuous learning and performance improvement
  • Multi-objective analysis (exploration, optimization, prediction)

👥 Agent-Based Modeling

  • 6 heterogeneous agent types with realistic behavioral patterns
  • Multi-layer network interactions and social influence
  • Crisis-dependent behavior adaptation
  • Wealth distribution dynamics with inequality analysis

🌊 Dynamic Network Analysis

  • Real-time spillover detection across multiple time scales
  • Contagion episode identification with consensus algorithms
  • Regime-switching dynamics and structural break detection
  • Multi-scale temporal decomposition

📊 Multi-Asset Support

  • Equities: Global indices, individual stocks
  • Commodities: Energy, metals, agriculture
  • Cryptocurrencies: Bitcoin, altcoins, DeFi tokens
  • Fixed Income: Government and corporate bonds
  • Real Estate: REITs and property indices

🎨 Publication-Quality Visualizations

  • ggraph-based network plots with professional styling
  • Interactive dashboards with real-time updates
  • Comprehensive reporting with automated insights
  • Customizable themes and export options

📦 Installation

Quick Install 

# Install from source
source("install.R")

# Or using devtools
devtools::install()

Manual Installation 

# Install dependencies
install.packages(c(
  "shiny", "shinydashboard", "ggplot2", "ggraph", "igraph", 
  "dplyr", "R6", "viridis", "DT", "plotly"
))

# Install package
devtools::install_local("AgenticWaves")

🎯 Quick Start

Interactive Launcher 

library(AgenticWaves)

# Launch interactive menu
launch_agentic_waves()

# Or launch Shiny dashboard directly
run_agentic_waves_app()

Basic Analysis 

# Load sample data
data <- get_sample_data("global_markets")

# Create autonomous AI agent
agent <- create_autonomous_agent("explorer")

# Run autonomous analysis
results <- agent$analyze_autonomously(data)

# View insights
results$insights

Full Simulation 

# Create agent population
agents <- create_enhanced_agent_population(
  n_agents = 500,
  behavioral_heterogeneity = 0.7
)

# Run market simulation
sim_results <- simulate_enhanced_market_dynamics(
  agents = agents,
  asset_data = data,
  n_periods = 1000
)

# Analyze spillovers
spillover_results <- calculate_dynamic_spillover_networks(
  sim_results,
  window_size = 100
)

# Generate publication dashboard
plots <- generate_publication_dashboard(
  sim_results, 
  spillover_results,
  save_plots = TRUE
)

🔬 Core Components

Agent Types

  • Momentum Traders: Follow price trends and market momentum
  • Contrarian Traders: Trade against prevailing market trends
  • Fundamentalist Traders: Base decisions on fundamental analysis
  • Noise Traders: Make random or irrational trading decisions
  • Herding Traders: Follow crowd behavior and social signals
  • Sophisticated Traders: Use complex multi-factor strategies

Analysis Methods

  • Wavelet Decomposition: Multi-scale temporal analysis
  • Quantile Transfer Entropy: Tail-dependent spillover detection
  • Network Metrics: Centrality, clustering, modularity analysis
  • Contagion Detection: Multiple consensus methodologies
  • Regime Identification: Structural break and changepoint detection

Visualization Suite

  • Network Diagrams: Professional ggraph-based layouts
  • Spillover Heatmaps: Time-varying connectivity matrices
  • Agent Performance: Risk-return scatter plots by type
  • Market Dynamics: Multi-asset price and volatility evolution
  • Wealth Distribution: Inequality evolution over time

📱 Interactive Dashboard

The Shiny dashboard provides a comprehensive interface for:

  • Data Upload: CSV/Excel files or built-in datasets
  • AI Analysis: Autonomous agent configuration and execution
  • Network Analysis: Interactive network visualization and metrics
  • Agent Simulation: Population setup and market dynamics
  • Spillover Analysis: Real-time spillover and contagion detection
  • Visualization Gallery: Publication-quality plot generation
  • Report Generation: Automated comprehensive reports

🧪 Testing 

# Run comprehensive tests
test_all_functions()

# Or use testthat
devtools::test()

📚 Documentation

Function Help 

?AgenticWaves              # Package overview
?create_autonomous_agent   # AI agent creation
?simulate_enhanced_market_dynamics  # Market simulation
?calculate_dynamic_spillover_networks  # Spillover analysis

Vignettes

  • Getting Started: Basic usage and examples
  • Agent-Based Modeling: Detailed ABM methodology
  • Network Analysis: Spillover and contagion detection
  • Visualization Guide: Creating publication-quality plots

🔧 Advanced Usage

Custom Data Analysis 

# Load your own data
data <- read.csv("your_data.csv")
processed_data <- process_financial_data(data)

# Validate data quality
quality <- validate_data_quality(processed_data)
print(quality)

# Run analysis
agent <- create_autonomous_agent("optimizer")
results <- agent$analyze_autonomously(processed_data)

Network Customization 

# Create custom network
network <- create_dynamic_multilayer_network(
  agents,
  network_types = c("trading", "information", "social"),
  density = 0.1
)

# Analyze network properties
metrics <- calculate_network_metrics(network$networks$trading$graph)

Visualization Customization 

# Custom network plot
plot <- plot_enhanced_network(
  network_obj,
  layout = "stress",
  node_size_var = "betweenness",
  color_scheme = "viridis"
)

# Save high-resolution plot
ggsave("network.png", plot, width = 12, height = 8, dpi = 300)

🎨 Example Gallery

The package includes comprehensive examples:

# Generate visualization gallery
generate_visualization_gallery()

# Create sample report
create_sample_report()

# Run complete demo
run_complete_demo()

📊 Research Applications

AgenticWaves is designed for:

  • Systemic Risk Analysis: Financial contagion and spillover effects
  • Market Microstructure: Agent behavior and market dynamics
  • Portfolio Management: Dynamic risk assessment and optimization
  • Regulatory Analysis: Market stability and intervention effects
  • Academic Research: Publication-ready analysis and visualizations

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Development Setup 

# Clone repository
git clone https://github.com/avishekb9/AgenticWaves.git

# Install development dependencies
devtools::install_dev_deps()

# Run tests
devtools::test()

# Build documentation
devtools::document()

📄 Citation

If you use AgenticWaves in your research, please cite:

@software{agenticwaves2025,
  title = {AgenticWaves: Agentic AI-Powered Wavelet Financial Network Analysis},
  author = {Bhandari, Avishek},
  year = {2025},
  url = {https://github.com/avishekb9/AgenticWaves},
  version = {1.0.0}
}

📧 Support

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Agent-Based Modeling: Inspired by research in computational economics
  • Network Analysis: Built on advances in financial network theory
  • Visualization: Leverages the powerful ggraph/igraph ecosystem
  • AI Agents: Incorporates modern autonomous system design principles

AgenticWaves: Revolutionizing financial network analysis through autonomous AI

GitHub