656442ceec
chore: update performance tracking 2026-03-03
2026-03-03 13:44:43 +00:00
d3ce8c2ea3
chore: daily discovery 2026-03-03
2026-03-03 13:43:30 +00:00
6996af4577
chore: update performance tracking 2026-03-02
2026-03-02 13:44:36 +00:00
33f932f6e0
chore: daily discovery 2026-03-02
2026-03-02 13:43:55 +00:00
8b8b0ab3f5
chore: update performance tracking 2026-03-01
2026-03-01 13:20:37 +00:00
b12040f3d6
chore: daily discovery 2026-03-01
2026-03-01 13:19:55 +00:00
db332b3033
chore: update performance tracking 2026-02-28
2026-02-28 13:17:50 +00:00
41684c36e0
chore: daily discovery 2026-02-28
2026-02-28 13:17:14 +00:00
151b841fd4
chore: update performance tracking 2026-02-27
2026-02-27 13:40:57 +00:00
dabec2130e
chore: daily discovery 2026-02-27
2026-02-27 13:40:48 +00:00
6273c2c94c
chore: update performance tracking 2026-02-26
2026-02-26 13:53:17 +00:00
4b47f23219
chore: daily discovery 2026-02-26
2026-02-26 13:52:36 +00:00
bd3086f044
chore: update performance tracking 2026-02-25
2026-02-25 13:51:31 +00:00
bc3c5e729e
chore: daily discovery 2026-02-25
2026-02-25 13:51:20 +00:00
3d9c43343b
chore: update performance tracking 2026-02-24
2026-02-24 13:51:30 +00:00
9e9550f4aa
chore: daily discovery 2026-02-24
2026-02-24 13:51:19 +00:00
25de155d0b
chore: update performance tracking 2026-02-23
2026-02-23 13:56:54 +00:00
576f264c85
chore: daily discovery 2026-02-23
2026-02-23 13:55:47 +00:00
ac07e23882
chore: update performance tracking 2026-02-22
2026-02-22 13:31:47 +00:00
41f457bd12
chore: daily discovery 2026-02-22
2026-02-22 13:31:02 +00:00
592fe798ec
chore: update performance tracking 2026-02-21
2026-02-21 13:25:44 +00:00
01e16e987f
chore: daily discovery 2026-02-21
2026-02-21 13:24:48 +00:00
8e2392029a
Update
2026-02-20 08:39:37 -08:00
ec8309a34e
Update
2026-02-20 08:38:15 -08:00
6342090a94
chore: update performance tracking 2026-02-20
2026-02-20 13:46:52 +00:00
4f26536f46
chore: daily discovery 2026-02-20
2026-02-20 13:46:15 +00:00
aadb54e776
chore: update performance tracking 2026-02-19
2026-02-19 13:55:01 +00:00
89c23751fa
chore: daily discovery 2026-02-19
2026-02-19 13:54:25 +00:00
4355f633b3
chore: update performance tracking 2026-02-18
2026-02-18 13:51:57 +00:00
a29cb9ca18
chore: daily discovery 2026-02-18
2026-02-18 13:51:26 +00:00
60a4fdd98d
chore: update performance tracking 2026-02-17
2026-02-17 13:51:48 +00:00
743ef2c56e
chore: daily discovery 2026-02-17
2026-02-17 13:50:40 +00:00
e3f3c02dc9
chore: daily discovery 2026-02-16
2026-02-16 14:32:42 +00:00
18f6907857
chore: daily discovery 2026-02-13
2026-02-13 14:20:20 +00:00
85ec1cb88f
chore: daily discovery 2026-02-12
2026-02-12 14:38:41 +00:00
53078c6319
chore: daily discovery 2026-02-12
2026-02-12 14:22:14 +00:00
54f5af2036
chore: daily discovery 2026-02-12
2026-02-12 06:47:07 +00:00
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
8ebb42114d
Add recommendations folder so that the UI can display it 4
2026-02-10 22:28:52 -08:00
0bc7dda086
Add recommendations folder so that the UI can display it 3
2026-02-10 09:43:54 -08:00
50eee9b945
Add recommendations folder so that the UI can display it 2
2026-02-09 23:53:55 -08:00
8273870e0a
Add recommendations folder so that the UI can display it
2026-02-09 23:41:57 -08:00
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
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
5cf57e5d97
Update
2025-12-02 20:49:42 -08:00
github-actions[bot]
Youssef Aitousarrah