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, or technical-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 symbol is omitted, the skill may reuse last_symbol from 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 score and bias summarize 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, and strategy-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-data or shares the same upstream loaders.
Often feeds technical-scan, decision-support, strategy-design, and market-brief.