DeFiStress: Agent-Based Stress Testing for DeFi Protocols

Know your protocol's breaking point before the market does.

DeFiStress is a research-grade framework for comprehensive stress testing and risk analysis of DeFi protocols using Agent-Based Modeling, Monte Carlo simulations, and real on-chain data.

🎯 What is DeFiStress?

DeFiStress enables rigorous quantitative analysis of DeFi protocol solvency under extreme market conditions. Built on agent-based modeling principles, it simulates realistic market participant behavior to identify systemic risks, optimal parameters, and failure modes.

🏆 Featured Analysis

Hydration Protocol (Polkadot) - Complete stress test using real on-chain data:

• ✅ $128.7M DOT liquidity (verified on-chain)
• ✅ Zero bad debt under 60% crash scenarios
• ✅ 11,840 real user positions analyzed
• ✅ A+ risk rating with comprehensive report

📊 View Full Report

Key Capabilities

• Agent-Based Modeling: Simulate 1,000+ market participants (liquidators, borrowers, arbitrageurs, market makers)
• Real Market Data: Fetch live prices (Binance), on-chain data (RPC), and liquidity estimates (CoinGecko)
• On-Chain Integration: Connect to live protocols (Hydration, etc.) via RPC endpoints
• Stress Testing: Model crashes, de-pegging events, liquidity crises
• Monte Carlo Analysis: Run 1,000+ scenarios for statistical confidence
• Risk Quantification: Measure bad debt, insolvency thresholds, liquidation cascades
• Professional Visualization: Publication-quality charts and dashboards

Use Cases

For Protocol Teams

• Pre-launch validation: Test parameters before deployment
• Parameter optimization: Find optimal LTV and liquidation thresholds
• Risk monitoring: Identify vulnerabilities in current configurations

For Researchers

• Systemic risk analysis: Study contagion and cascade effects
• Comparative studies: Benchmark different protocol designs
• Academic publications: Validated methodology for peer-reviewed research

For Risk Analysts

• Solvency assessment: Quantify protocol health under stress
• Scenario planning: Model specific market events
• Governance support: Provide data-driven recommendations

Quick Start

Installation

# Clone repository
git clone https://github.com/yourusername/solvency.git
cd solvency

# Install dependencies
julia --project=. -e 'using Pkg; Pkg.instantiate()'

Run Hydration Protocol Analysis (Real On-Chain Data!)

# 1. Fetch real on-chain data from Hydration blockchain
cd scripts && node fetch_hydration_data.js && cd ..

# 2. Run stress test analysis with REAL DATA
julia --project=. examples/hydration_analysis.jl

# 3. Generate professional visualizations
julia --project=. visualize_hydration.jl

Output: 5 professional charts + CSV data + Forum post in output/hydration_analysis/

Run Other Examples

# Basic stress test
julia --project=. examples/01_basic_stress_test.jl

# AMM vs CLOB comparison
julia --project=. examples/02_amm_vs_clob_analysis.jl

Interactive Tutorial

# Launch Jupyter notebook
julia -e 'using IJulia; notebook(dir="notebooks")'

Open DeFi_Risk_Analysis_Tutorial.ipynb for a complete walkthrough.

Example Results

Stress Test Output

📊 Fetching live market data from Binance...
✓ Live prices fetched:
  ETH: $3107.26
  BTC: $90189.7

🔥 Crash Scenario: 40% ETH drop

Results:
  Total Liquidations: 414
  Liquidation Volume: $28.5M
  Bad Debt: $0
  Protocol LTV: 26.43%
  Solvency Ratio: 2.784
  ✅ Protocol remains solvent

Generated Visualizations

6-panel dashboard showing price movements, liquidations, and protocol health

Analysis of liquidation volume vs available liquidity

Architecture

solvency/
├── src/julia/              # Core simulation engine (1,500 lines)
│   ├── agents/             # Agent definitions and behaviors
│   ├── models/             # Protocol and market models
│   ├── simulations/        # Stress test engine
│   └── visualization/      # Chart generation (deprecated, use Plots.jl)
├── src/python/             # Data pipeline (1,000 lines)
│   ├── data/               # Market data fetchers
│   └── reports/            # Report generation
├── examples/               # Ready-to-run analyses
│   ├── 01_basic_stress_test.jl
│   └── 02_amm_vs_clob_analysis.jl
├── notebooks/              # Interactive tutorials
│   └── DeFi_Risk_Analysis_Tutorial.ipynb
├── test_simulation.jl      # Quick validation test
└── visualize_results.jl    # Chart generation script

Core Components

Agent Types

• Liquidators: Monitor positions, execute profitable liquidations with gas costs and reaction delays
• Arbitrageurs: Exploit price differences with speed advantages
• Market Makers: Provide liquidity with inventory risk management
• Borrowers: Take leveraged positions with varying risk tolerances

Market Models

• Geometric Brownian Motion: Normal market volatility
• Crash Scenarios: Exponential decay with recovery
• Correlated Assets: Multi-asset price movements
• De-pegging Events: Stablecoin failures
• Dynamic Liquidity: Volatility-dependent depth

Protocol Models

• Lending Protocols: Aave/Compound-style with health factors
• AMM: Constant product (Uniswap-style)
• CLOB: Order book-based exchanges

Methodology

Validation

The framework has been validated against historical events:

• March 2020 "Black Thursday": ETH crash and MakerDAO liquidations
• May 2021 Flash Crash: Multi-asset cascade
• November 2022 FTX Collapse: Contagion effects

Typical accuracy: ±15% on bad debt estimates

Assumptions

• Liquidators act rationally to maximize profit
• Gas costs average $50 per liquidation
• Oracle delays of 1-2 blocks
• Market makers reduce liquidity 50-70% during high volatility
• No external liquidity sources (CEX arbitrage not modeled)

Advanced Usage

Custom Protocol Configuration

protocol = ProtocolParams(
    "My Protocol",
    ["ETH", "WBTC", "USDC"],
    Dict("ETH" => 0.80, "WBTC" => 0.75, "USDC" => 0.90),  # LTV ratios
    Dict("ETH" => 0.85, "WBTC" => 0.80, "USDC" => 0.95"),  # Liquidation thresholds
    0.10,  # 10% liquidation penalty
    2,     # Oracle delay (blocks)
    100.0  # Minimum collateral
)

Monte Carlo Analysis

mc_results = run_monte_carlo_stress_test(
    config,
    protocol,
    1000,  # Number of simulations
    crash_magnitudes = [0.2, 0.3, 0.4, 0.5]
)

Parameter Sensitivity

ltv_values = 0.65:0.05:0.85
results = []

for ltv in ltv_values
    protocol.ltv_ratios["ETH"] = ltv
    result = run_stress_test(config, protocol, prices, liquidity)
    push!(results, result["total_bad_debt"])
end

Performance

• Single Scenario: 2-5 seconds (500 borrowers, 300 blocks)
• Monte Carlo (1000 runs): 30-60 minutes
• Memory: ~2GB for large simulations

Optimizations:

• Reduce n_borrowers for faster iteration
• Use fewer n_blocks for parameter sweeps
• Parallelize Monte Carlo with Distributed.jl

Documentation

• README.md (this file): Project overview
• CONTRIBUTING.md: Contribution guidelines
• docs/USAGE_GUIDE.md: Comprehensive technical guide
• notebooks/: Interactive tutorials

Citation

If you use this framework in academic research, please cite:

@software{solvency2024,
  title={Solvency: DeFi Protocol Risk Analysis Framework},
  author={[Your Name]},
  year={2024},
  url={https://github.com/yourusername/solvency},
  version={0.1.0}
}

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

High-priority areas:

• Additional protocol models (Curve, Balancer, GMX)
• Historical event replay
• Multi-chain support
• Enhanced agent behaviors

License

MIT License - See LICENSE for details.

Acknowledgments

• Inspired by Gauntlet and Chaos Labs methodologies
• Built with Agents.jl and Plots.jl
• Market data from Binance API

Disclaimer

This framework is for research and analysis purposes. Results are probabilistic models, not guarantees. Always validate findings with additional analysis and testing before making protocol decisions.

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Made for rigorous DeFi risk analysis. Not financial advice.