How do I install the A股短线交易决策 A-Share Short-Term Decision skill?

Run npx skillmds@latest add modbender/a-a-share-short-term-decision in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is A股短线交易决策 A-Share Short-Term Decision safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with A股短线交易决策 A-Share Short-Term Decision?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is A股短线交易决策 A-Share Short-Term Decision free to use?

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Who published A股短线交易决策 A-Share Short-Term Decision?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

A股短线交易决策 A-Share Short-Term Decision by modbender

Name: A股短线交易决策 A-Share Short-Term Decision
Rating: 4.2 (5 reviews)
Author: modbender

A股短线交易决策 A-Share Short-Term Decision

A-share short-term trading decision skill for 1-5 day horizon. Use when you need real-data market sentiment, sector rotation, strong stock scanning, capital flow confirmation, date-based short-term signal scoring, prediction logging, and next-day market comparison for CN A-share momentum trading.

modbender Updated Sep 9, 2026 12 repo stars

File contents

A-Share Short-Term Decision Skill

Implement in sequence:

Run short_term_signal_engine(analysis_date) for target date.
If needed, persist prediction with run_prediction_for_date(analysis_date).
Compare prediction vs actual market with compare_prediction_with_market(prediction_date, actual_date).
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

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:

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/.

---name: a-a-share-short-term-decisiondescription: A-share short-term trading decision skill for 1-5 day horizon. Use when you need real-data market sentiment, sector rotation, strong stock scanning, capital flow confirmation, date-based short-term signal scoring, prediction logging, and next-day market comparison for CN A-share momentum trading.--- # 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/`.

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