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TradingAgents
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11 Commits

Author SHA1 Message Date
Youssef Aitousarrah ec8309a34e Update 2026-02-20 08:38:15 -08:00
Youssef Aitousarrah fd951be8bc Update 2026-02-17 12:07:07 -08:00
Youssef Aitousarrah 457d650e42 Update 2026-02-17 10:27:13 -08:00
Youssef Aitousarrah 8d3205043e Update 2026-02-16 14:17:41 -08:00
Youssef Aitousarrah f4aceef857 feat: add daily discovery workflow, recommendation history, and scanner improvements 
- Add GitHub Actions workflow for daily discovery (8:30 AM ET, weekdays)
- Add headless run_daily_discovery.py script for scheduling
- Expand options_flow scanner to use tickers.txt with parallel execution
- Add recommendation history section to Performance page with filters and charts
- Fix strategy name normalization (momentum/Momentum/Momentum-Hype → momentum)
- Fix strategy metrics to count all recs, not just evaluated ones
- Add error handling to Streamlit page rendering

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-11 22:07:02 -08:00
Youssef Aitousarrah ab8d174990 Add recommendations folder so that the UI can display it 5 2026-02-10 22:43:46 -08:00
Youssef Aitousarrah 8ebb42114d Add recommendations folder so that the UI can display it 4 2026-02-10 22:28:52 -08:00
Youssef Aitousarrah cb5ae49501 chore: linter formatting + ML scanner logging, prompt control, ranker reasoning 
- Add ML signal scanner results table logging
- Add log_prompts_console config flag for prompt visibility control
- Expand ranker investment thesis to 4-6 sentence structured reasoning
- Linter auto-formatting across modified files

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-09 23:04:38 -08:00
Youssef Aitousarrah 43bdd6de11 feat: discovery pipeline enhancements with ML signal scanner 
Major additions:
- ML win probability scanner: scans ticker universe using trained
  LightGBM/TabPFN model, surfaces candidates with P(WIN) above threshold
- 30-feature engineering pipeline (20 base + 10 interaction features)
  computed from OHLCV data via stockstats + pandas
- Triple-barrier labeling for training data generation
- Dataset builder and training script with calibration analysis
- Discovery enrichment: confluence scoring, short interest extraction,
  earnings estimates, options signal normalization, quant pre-score
- Configurable prompt logging (log_prompts_console flag)
- Enhanced ranker investment thesis (4-6 sentence reasoning)
- Typed DiscoveryConfig dataclass for all discovery settings
- Console price charts for visual ticker analysis

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-09 22:53:42 -08:00
Youssef Aitousarrah 369f8c444b feat: discovery system code quality improvements and concurrent execution 
Implement comprehensive code quality improvements and performance optimizations
for the discovery pipeline based on code review findings.

## Key Improvements

### 1. Common Utilities (DRY Principle)
- Created `tradingagents/dataflows/discovery/common_utils.py`
- Extracted ticker parsing logic (eliminates 40+ lines of duplication)
- Centralized stopwords list (71 common non-ticker words)
- Added ReDoS protection (100KB text length limit)
- Provides `validate_candidate_structure()` for output validation

### 2. Scanner Output Validation
- Two-layer validation approach:
  - Registration-time: Check scanner class structure
  - Runtime: Validate each candidate dictionary
- Added `scan_with_validation()` wrapper in BaseScanner
- Validates required keys: ticker, source, context, priority
- Graceful error handling with structured logging

### 3. Configuration-Driven Design
- Moved magic numbers to `default_config.py`:
  - `ticker_universe`: Top 20 liquid options tickers
  - `min_volume`: 1000 (options flow threshold)
  - `min_transaction_value`: $25,000 (insider buying filter)
- Fixed hardcoded absolute paths to relative paths
- Improved portability across development environments

### 4. Concurrent Scanner Execution (37% Performance Gain)
- Implemented ThreadPoolExecutor for parallel scanner execution
- Configuration: `scanner_execution.concurrent`, `max_workers`, `timeout_seconds`
- Performance: 42s vs 67s (37% faster with 8 scanners)
- Thread-safe state management (each scanner gets copy)
- Per-scanner timeout with graceful degradation
- Error isolation (one failure doesn't stop others)

### 5. Error Handling Improvements
- Changed bare `except:` to `except Exception:` (avoid catching KeyboardInterrupt)
- Added structured logging with `exc_info=True` and extra fields
- Implemented graceful degradation throughout pipeline

## Files Changed

### Core Implementation
- `tradingagents/__init__.py` (NEW) - Package initialization
- `tradingagents/default_config.py` - Scanner execution config, magic numbers
- `tradingagents/graph/discovery_graph.py` - Concurrent execution logic
- `tradingagents/dataflows/discovery/common_utils.py` (NEW) - Shared utilities
- `tradingagents/dataflows/discovery/scanner_registry.py` - Validation wrapper
- `tradingagents/dataflows/discovery/scanners/*.py` - Use common utilities

### Testing & Documentation
- `tests/test_concurrent_scanners.py` (NEW) - Comprehensive test suite
- `verify_concurrent_execution.py` (NEW) - Performance verification
- `CONCURRENT_EXECUTION.md` (NEW) - Implementation documentation

## Test Results

All tests passing (exit code 0):
- ✅ Concurrent execution: 42s, 66-69 candidates
- ✅ Sequential fallback: 56-67s, 65-68 candidates
- ✅ Timeout handling: Graceful degradation with 1s timeout
- ✅ Error isolation: Individual failures don't cascade

## Performance Impact

- Scanner execution: 37% faster (42s vs 67s)
- Time saved: ~25 seconds per discovery run
- At scale: 4+ minutes saved daily in production
- Same candidate quality (65-69 tickers in both modes)

## Breaking Changes

None. Concurrent execution is opt-in via config flag.
Sequential mode remains available as fallback.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
 2026-02-05 23:27:01 -08:00
Youssef Aitousarrah 5cf57e5d97 Update 2025-12-02 20:49:42 -08:00