- Use contextlib.suppress instead of try/except/pass (SIM105)
- Prefix unused fixture arguments with underscore (ARG002)
- Replace asyncio.TimeoutError with TimeoutError (UP041)
- Apply ruff formatting to all files
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add sniper cluster detection system that identifies wallets exhibiting
coordinated "sniper" behavior - consistently entering markets within
minutes of their creation.
Key features:
- SniperDetector class using DBSCAN clustering algorithm
- Tracks wallet entries across markets with timing analysis
- Feature vector: market hash, entry delta, log position size
- Identifies clusters of wallets with similar timing patterns
- Generates SniperClusterSignal for detected cluster members
- Configurable entry threshold (default 5 minutes), cluster size, DBSCAN params
- Confidence scoring based on cluster size, entry speed, market overlap
Also adds:
- SniperClusterSignal model to detector/models.py
- scikit-learn and numpy dependencies for ML clustering
- 27 comprehensive tests covering clustering logic and edge cases
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement RiskScorer that combines signals from multiple detectors into
a unified risk assessment with weighted scoring and deduplication.
Features:
- SignalBundle for collecting signals for a single trade
- RiskAssessment dataclass with complete scoring metadata
- Configurable weights for each signal type
- Multi-signal bonus (1.2x for 2 signals, 1.3x for 3+)
- Redis-based deduplication (1 hour window by default)
- Alert threshold configuration (default: 0.6)
- Batch assessment for processing multiple trades
- A/B testing support via dynamic weight updates
Default weights:
- fresh_wallet: 0.40
- size_anomaly: 0.35
- niche_market: 0.25
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement SizeAnomalyDetector for identifying trades with unusually
large position sizes relative to market liquidity.
Features:
- Volume impact analysis (trade size / 24h volume)
- Order book impact analysis (trade size / book depth)
- Niche market detection using category heuristics
- Confidence scoring with configurable thresholds
- Batch analysis for processing multiple trades
The detector gracefully handles missing volume/book data by falling
back to category-based heuristics for identifying niche markets
where large trades are more significant.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add FreshWalletDetector class that identifies trades from fresh wallets
and generates alert signals with confidence scores.
Features:
- FreshWalletSignal dataclass with trade/wallet data and confidence
- Configurable thresholds for nonce, age, and minimum trade size
- Confidence scoring based on wallet freshness and trade characteristics
- Batch analysis support with parallel processing
- Integration with WalletAnalyzer from profiler module
Confidence scoring:
- Base: 0.5 (fresh wallet detected)
- +0.2 if nonce == 0 (brand new)
- +0.1 if age < 2 hours (very young)
- +0.1 if trade size > $10,000 (large trade)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add pyproject.toml with core and dev dependencies
- Configure Ruff for linting and formatting
- Configure MyPy for strict type checking
- Configure pytest with asyncio support
- Create src/polymarket_insider_tracker package structure
- Add py.typed marker for PEP 561 compliance
- Add pre-commit hooks configuration
- Add comprehensive .gitignore
- Create test directory structure with basic tests
Closes#25🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Patrick Selamy