a-a-share-short-term-decision

# A-Share Short-Term Decision Skill Implement in sequence: 1. Run `short_term_signal_engine(analysis_date)` for target date. 2. If needed, persist prediction with `run_prediction_for_date(analysis_date)`. 3. Compare prediction vs actual market with `compare_prediction_with_market(prediction_date, actual_date)`. 4. Output report with `generate_daily_report(analysis_date)`. ## Tool Contracts ### `short_term_signal_engine(analysis_date=None)` - `analysis_date`: `YYYY-MM-DD` or `YYYYMMDD` - Returns weighted short-term score and recommendation status. - Always returns friendly `no_recommendation_message` when no tradable candidate exists. ### `run_prediction_for_date(analysis_date)` - Runs signal engine for the specified date. - Appends decision snapshot into `data/decision_log.jsonl`. ### `compare_prediction_with_market(prediction_date, actual_date=None)` - Loads prediction from log (or auto-generates if missing). - Compares predicted candidates against real market closes on `actual_date`. - Returns per-stock return and summary statistics. ## No-Recommendation Behavior Required behavior: - Never return empty output. - If `candidates` is empty or signal is `NO_TRADE`, explicitly say: `当前暂无可执行短线买入标的`. - Include reason and next action. ## Runtime ```bash python3 main.py short_term_signal_engine --date 2026-02-12 python3 main.py run_prediction_for_date --date 2026-02-12 python3 main.py compare_prediction_with_market --prediction-date 2026-02-12 --actual-date 2026-02-13 python3 main.py generate_daily_report --date 2026-02-12 ``` ## Subskills Workflow For recurring optimize-then-recommend flow, run: ```bash python3 subskills/config-optimization/optimize_from_aggressive.py --analysis-period "2026-02-01 to 2026-02-12" python3 subskills/daily-recommendation/generate_daily_recommendation.py --date 2026-02-14 ``` All generated artifacts are stored under `data/`.