43bdd6de11
feat: discovery pipeline enhancements with ML signal scanner
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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
369f8c444b
feat: discovery system code quality improvements and concurrent execution
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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
ea4ee9176b
Update
2025-12-09 23:16:53 -08:00
5cf57e5d97
Update
2025-12-02 20:49:42 -08:00
a6734d71bc
WIP
2025-09-26 16:17:50 +08:00
718df34932
Merge pull request #29 from ZeroAct/save_results
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Save results
2025-06-26 00:28:30 -04:00
43aa9c5d09
Local Ollama ( #53 )
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- Fix typo 'Start' 'End'
- Add llama3.1 selection
- Use 'quick_think_llm' model instead of hard-coding GPT
2025-06-26 00:27:01 -04:00
26c5ba5a78
Revert "Docker support and Ollama support ( #47 )" ( #57 )
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This reverts commit 78ea029a0b
2025-06-26 00:07:58 -04:00
78ea029a0b
Docker support and Ollama support ( #47 )
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- Added support for running CLI and Ollama server via Docker
- Introduced tests for local embeddings model and standalone Docker setup
- Enabled conditional Ollama server launch via LLM_PROVIDER
2025-06-25 23:57:05 -04:00
ee3d499894
Merge branch 'TauricResearch:main' into save_results
2025-06-25 08:43:19 +09:00
52284ce13c
fixed anthropic support. Anthropic has different format of response when it has tool calls. Explicit handling added
2025-06-21 12:51:34 -07:00
da84ef43aa
main works, cli bugs
2025-06-15 22:20:59 -07:00
417b09712c
refactor
2025-06-12 13:53:28 +09:00
9647359246
save reports & logs under results_dir
2025-06-12 11:25:07 +09:00
99789f9cd1
Add support for other backends, such as OpenRouter and olama
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This aims to offer alternative OpenAI capable api's.
This offers people to experiment with running the application locally
2025-06-11 14:19:25 +02:00
cc97cb6d5d
chore(release): v0.1.0 – initial public release of TradingAgents
2025-06-05 04:27:57 -07:00
Youssef Aitousarrah
luohy15
Yijia Xiao
Max Wong
Geeta Chauhan
Huijae Lee
Edward Sun
maxer137
Yijia-Xiao