harb/onchain/analysis

KRAIKEN Liquidity Analysis

Analysis tools for testing the three-position anti-arbitrage strategy using random fuzzing to discover profitable trading scenarios.

Quick Start

1. Run the analysis (includes parameter validation + random fuzzing):

   forge script analysis/SimpleAnalysis.s.sol --ffi --via-ir
   

2. Start visualization server:

   cd analysis && python3 -m http.server 8000
   

3. View results at http://localhost:8000/scenario-visualizer.html

How It Works

The analysis script uses random fuzzing like the historical testScenarioFuzz function to discover profitable trading scenarios:

• Random Parameters: Generates random trading amounts, frequencies, and sentiment parameters
• Historical Logic: Uses exact trading logic from the working historical tests
• Early Exit: Stops immediately after finding the first profitable scenario
• CSV Generation: Creates detailed trading sequence data for visualization

Analysis Commands

Full Analysis (Recommended)

forge script analysis/SimpleAnalysis.s.sol --ffi --via-ir

Runs both parameter validation and random fuzzing analysis.

Parameter Validation Only

Modify the run() function to comment out runSentimentFuzzingAnalysis() if you only want to test parameter configurations.

Files

• SimpleAnalysis.s.sol - Main analysis script with random fuzzing
• scenario-visualizer.html - Web-based position visualization
• profitable_scenario.csv - Generated when profitable scenarios are found
• view-scenarios.sh - HTTP server launcher (alternative to python server)

Analysis Output

Console Output

• Parameter validation results for bull/neutral/bear market conditions
• Random fuzzing attempts with generated parameters
• Profitable scenario detection and stopping logic

CSV Data

When profitable scenarios are found, generates profitable_scenario.csv containing:

• Trading sequence (buy/sell/recenter actions)
• Position data (Floor/Anchor/Discovery liquidity amounts)
• Price tick information
• Token distribution across positions

Visualization

Interactive HTML dashboard showing:

• Position ranges and token distributions
• Uniswap V3 liquidity calculations
• Trading sequence progression
• Anti-arbitrage strategy effectiveness

Random Fuzzing Details

The script generates random parameters for each test:

• Actions: 6-12 trading actions per scenario
• Frequency: 1-5 recenter frequency
• Amounts: 50-255 basis values (scaled to ether)
• Sentiment: Random capital inefficiency, anchor share, width, discovery depth

This approach mirrors the historical testScenarioFuzz function that successfully found profitable scenarios in the original codebase.

Troubleshooting

No Profitable Scenarios Found

• The anti-arbitrage protection is working effectively
• Try increasing maxAttempts in runSentimentFuzzingAnalysis() for more fuzzing attempts
• Check console output for parameter validation results

Script Execution Issues

• Ensure you're using the full script command (not -s function selection)
• The setUp() function is required for proper contract initialization
• Use --via-ir flag for complex contract compilation

Visualization Issues

• Start the HTTP server from the analysis/ directory
• Check that profitable_scenario.csv exists before viewing
• Browser security may block local file access - use the HTTP server