Clean up test suite organization and eliminate duplicate code

- Remove duplicate test files with overlapping functionality:
  * Delete VWAPDoubleOverflowAnalysis.t.sol (155 lines) - functionality already covered by VWAPTracker.t.sol with proper assertions
  * Delete ModularComponentsTest.t.sol (57 lines) - meaningless tests redundant with build process

- Improve code organization:
  * Move CSVHelper.sol and CSVManager.sol from test/helpers/ to analysis/ folder to reflect actual usage
  * Update import path in SimpleAnalysis.s.sol from ../test/helpers/CSVManager.sol to ./CSVManager.sol
  * Remove deprecated uintToStr() and intToStr() wrapper functions from CSVHelper.sol

- Update documentation:
  * Mark completed cleanup tasks in testing_todos.md
  * Add code organization improvements section showing eliminated duplicate functionality

Result: Cleaner test suite with 92 meaningful tests (vs 95 with noise), better file organization reflecting actual usage patterns, and zero dead code remaining.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

onchain/analysis/README.md

@@ -1,28 +1,92 @@
 # KRAIKEN Liquidity Analysis
 
-Quick analysis tools for testing the three-position anti-arbitrage strategy.
+Analysis tools for testing the three-position anti-arbitrage strategy using random fuzzing to discover profitable trading scenarios.
 
-## Usage
+## Quick Start
 
-1. **Run sentiment analysis** to find profitable scenarios:
+1. **Run the analysis** (includes parameter validation + random fuzzing):
    ```bash
-   forge script analysis/SimpleAnalysis.s.sol:SimpleAnalysis -s "runSentimentFuzzingAnalysis()" --ffi --via-ir
+   forge script analysis/SimpleAnalysis.s.sol --ffi --via-ir
    ```
 
 2. **Start visualization server**:
    ```bash
-   ./view-scenarios.sh
+   cd analysis && python3 -m http.server 8000
    ```
 
-3. **View results** at `http://localhost:8001/scenario-visualizer.html`
+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)
+```bash
+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 sentiment fuzzing
-- `scenario-visualizer.html` - Web-based position visualization
-- `view-scenarios.sh` - HTTP server launcher
-- `profitable_scenario.csv` - Generated results (if profitable scenarios found)
+- `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
 
-The sentiment analysis tests bull/neutral/bear market conditions and generates CSV data for any profitable trading scenarios found. The visualizer shows position ranges, token distributions, and Uniswap V3 liquidity calculations.
+### 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