Market analyze
A-Stockit —— 面向 Agent 框架的 A 股量化分析技能库,提供多样化市场操作,无需配置独立 trading bot
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Assess trend, regime, and risk state for one A-share symbol. Use when user wants current market interpretation from the normalized snapshot without action sizing, execution planning, or thesis narration.
SKILL.md
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Market Analyze
Assess scored market state for: $ARGUMENTS
Overview
- Implementation status: code-backed
- Local entry script:
<bundle-root>/market-analyze/run.py - Primary purpose: turn normalized market data into a readable current-state interpretation
- Research layer: descriptive analysis only
- Workflow stage: stage 4
Signal and State Interpretation - Local executor guarantee: compute and return the current market snapshot summary, including score, bias, trend, regime, key levels, notes, and risk flags
Use When
- The user wants current trend, regime, support, resistance, and risk-state interpretation.
- The user wants descriptive market-state context before moving into
decision-support,strategy-design, ortechnical-scan. - The user needs a bounded summary of the current snapshot rather than a thesis memo or full report.
Do Not Use When
- The user wants a full report with decision and strategy sections. Use
market-brief. - The user wants a direct trading action or quantity. Use
decision-support. - The user wants technical-only pattern language or broader chart commentary beyond the runtime snapshot summary. Use
technical-scan. - The user wants raw normalized data only. Use
market-data. - The user wants broader catalyst, thesis, or variant-view analysis. Use
analysis.
Inputs
- Normal case: one stock symbol.
- Optional
--csv PATH: use a local CSV instead of the default market source. - Optional
--start,--end,--source: constrain the data-loading path. - If
symbolis omitted, the skill may reuselast_symbolfrom the same execution context. - Data note:
- the local runtime derives its state from the normalized daily-bar frame
- the user should not assume an intraday, benchmark-relative, or multi-factor attribution engine exists here
Execution
Step 1: Confirm the descriptive boundary
Use market-analyze for current-state interpretation only. It describes what the normalized snapshot currently looks like; it does not approve a position, design an execution plan, or validate a thesis.
Step 2: Run the local executor
python3 <bundle-root>/market-analyze/run.py <symbol> [--csv PATH]
Step 3: Deliver the snapshot honestly
The local output should be interpreted as a bounded state summary built from the enriched market frame. The agent should state:
- the data source and date window
- whether the symbol was explicit or reused from session context
- that
scoreandbiassummarize current state heuristically - that the skill does not imply portfolio action, catalyst interpretation, or execution readiness
Output Contract
- Minimum local executor output: human-readable text beginning with
市场分析. - Core fields: symbol, board, score, bias, trend, regime, support, resistance, notes, and risk flags.
- Side effects: updates session memory for the current execution context.
- Caller-facing delivery standard:
- identify the data source and date window
- treat score and bias as descriptive state summaries, not expected-return claims
- keep the answer interpretive and current-state oriented
- say explicitly when the result does not incorporate fundamentals, catalysts, portfolio constraints, or execution assumptions
Failure Handling
- Parse and argument errors: non-zero exit with a readable
命令错误message. - Data-loading or normalization errors: readable failure text beginning with
执行失败:. - Missing symbol with no reusable session symbol: readable guidance instead of a traceback.
- If the available data window is too thin for robust interpretation, say so directly rather than pretending high confidence.
Key Rules
- Keep this skill interpretive rather than prescriptive.
- Use it as the clean descriptive anchor for
technical-scan,decision-support, andstrategy-design. - Do not let a scored snapshot summary masquerade as a trading instruction.
- Do not imply indicators or controls that the current runtime does not actually expose in this skill’s local output.
Composition
- Usually follows
market-dataor shares the same upstream loaders. - Often feeds
technical-scan,decision-support,strategy-design, andmarket-brief.