Concluded hypotheses already live in concluded/ — keeping them in active.json
causes the registry to grow unboundedly. Runner now removes them at the end
of each cycle. Also cleaned up the existing social_dd concluded entry.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Statistical hypotheses now conclude immediately on the next runner cycle
without counting toward max_active. Only implementation hypotheses occupy
runner slots. Added conclude_statistical_hypothesis() for instant analysis
against existing performance data with Gemini LLM enrichment.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Uses google-genai SDK with gemini-2.5-flash-lite — same model already
used by the discovery pipeline, so no new secret needed (GOOGLE_API_KEY).
Removed ANTHROPIC_API_KEY from hypothesis-runner.yml.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
days_elapsed counts entries in picks_log, so running on weekends would
inflate the counter with noise picks. Exit early on Saturday/Sunday.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Before concluding a hypothesis, check if the scanner's source file
changed on main since created_at. If it did, the baseline picks in
performance_database.json reflect the updated code for the later part
of the experiment, which can confound the comparison.
When drift is detected, a warning is embedded in:
- the concluded .md doc (blockquote below Decision)
- the PR comment (blockquote in the conclusion body)
The programmatic decision is not overridden — the warning is purely
informational, allowing the reviewer to judge whether the result is
trustworthy.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
load_dotenv() in tradingagents/config.py searches the cwd for .env.
Worktrees in /tmp/ don't have one, so symlink the main repo's .env
into the worktree root before running discovery.
In CI, secrets are passed as env vars directly — symlink is a no-op.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When ANTHROPIC_API_KEY is set, conclude_hypothesis now:
- Loads the scanner domain file for context
- Calls claude-haiku-4-5-20251001 for a 3–5 sentence interpretation
- Embeds the analysis in the concluded .md doc and PR comment
The LLM enriches the conclusion with sample-size caveats, market
context, and a follow-up hypothesis suggestion — without overriding
the programmatic accept/reject decision.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Implements compute_7d_return, compute_metrics, load_baseline_metrics,
and make_decision functions with full TDD coverage (11 tests passing).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Implements compute_7d_return, compute_metrics, load_baseline_metrics,
and make_decision functions with full TDD coverage (11 tests passing).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
minervini.py existed but was never committed. Without the file on the
remote, the __init__.py import added in the previous fix causes an
ImportError in CI.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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>
- 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>
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>
Youssef Aitousarrah