Market Signal Fusion

Adaptive-language stock-analysis skill that interprets macro and political news, fuses it with retail/social sentiment, applies quantified value fallback rules, and outputs machine-readable stock ideas with valuation and technical plans.

yellowzijian@yellowzijian

Install

openclaw skills install @yellowzijian/market-signal-fusion

Market Signal Fusion

Use this skill when the user wants stock analysis driven by macro/political news + market sentiment + value screening + technical timing.

This skill is designed for adaptive language output and for downstream agent processing via a fixed JSON schema.

What this skill does

This skill runs a five-stage workflow:

Interpret recent political/economic/news catalysts that matter for equities.
Analyze market sentiment from retail/social discussion sources such as Reddit WSB and similar public channels.
Fuse step 1 and step 2 to identify candidate sectors and stocks.
Perform quantamental analysis and estimate buy / sell / risk ranges.
Add a short-term technical plan for entries, exits, and invalidation.

This version also adds three functional upgrades:

a structured WSB / retail sentiment data-module contract
a market regime detector to control style bias and sector weighting
confidence gates + reason codes so decisions are auditable and degrade gracefully when data is incomplete

If tools are available, prefer real market data and recent sources. Do not invent numbers.

Core operating rules

Treat anything market-sensitive as time-dependent.
Prefer the most recent 1–7 days for news and sentiment unless the user specifies another window.
Use primary or reputable financial sources whenever possible.
Separate clearly:
- facts
- inference
- opinion / scenario
Never present a stock pick without explaining why it survived the funnel.
If a required dataset is unavailable, say so explicitly and continue with the best available evidence.
Do not output generic investing advice; tie every conclusion to a catalyst, sentiment signal, valuation signal, or technical setup.
When confidence is low, reduce conviction rather than forcing a pick.
Every major conclusion should carry an implicit or explicit confidence gate based on freshness, source quality, and data completeness.
Attach short reason codes and, when relevant, rejection codes to structured outputs so downstream agents can audit why a stock passed or failed.
Preserve standard finance notation for tickers and metrics, e.g. NVDA, Forward P/E, FCF Yield, PEG, RSI, 200DMA.

Language and output rules

Default language behavior

Follow the user's prompt language automatically.
If the user asks in Chinese, output the entire user-facing analysis in Chinese only.
If the user asks in English, output the entire user-facing analysis in English only.
Do not force bilingual output unless the user explicitly requests bilingual output.
If the user does not clearly signal a language preference, use the language of the latest user request.
Keep ticker symbols, sector names, factor names, JSON keys, and key financial metrics in standard English notation.

Single-language formatting rules

For user-facing narrative sections:

Match the user's language in headings and narrative.
In Chinese mode, write headings, explanations, stock theses, and action plans in Chinese.
In English mode, write headings, explanations, stock theses, and action plans in English.
Do not append mirrored translations by default.
For tables or JSON fields, use stable English keys and optionally localized display labels outside the JSON only when needed.

Machine-readable output rule

Whenever the task is analytical and structured, output in two layers:

A human-readable report in the user's language
A strict JSON block that follows the schema below

If the user asks for only JSON, output only the JSON. If the user asks for prose only, still internally follow the schema but omit the visible JSON unless useful.

Inputs to collect from the user request

Extract these if the user provided them; otherwise use sensible defaults:

Market universe: US equities by default
Time horizon:
- macro/news: last 7 days
- sentiment: last 3 trading days if possible
- valuation: trailing + forward where available
- technicals: daily + weekly, and intraday if available
Style bias: value first, but allow momentum confirmation
Max output count:
- hot stocks from sentiment: 10
- final recommended list: 3 to 10 depending on evidence density
Risk style:
- if unspecified, assume balanced swing trader with value discipline

Stage 1 — News and macro-political interpretation

Identify market-relevant developments from areas such as:

central bank / rates / liquidity
inflation / labor / growth data
tariffs / sanctions / export controls
fiscal policy / defense spending / infrastructure
energy policy / OPEC / grid / power demand
AI regulation / chips / cloud capex
healthcare / drug pricing / reimbursement
antitrust / trade restrictions / industrial policy
geopolitical conflict affecting supply chains, commodities, shipping, or defense

For each major development, produce a structured record:

Catalyst: one-line summary
Why the market cares
Affected sectors / industries
Direction: bullish / bearish / mixed
Mechanism: revenue, margin, capex, regulation, cost pass-through, rates, risk premium, etc.
Time horizon: immediate / medium-term / long-term
Confidence: high / medium / low

Then aggregate across catalysts and rank the top favorable sectors.

Market regime detector

Before final sector ranking, classify the current market into one primary regime and optional secondary regime:

risk_on_growth
risk_off_defensive
inflation_reflation
rate_sensitive
commodity_shock
earnings_revision_recovery
policy_transition_mixed

For the chosen regime, provide:

the key evidence
which sectors should receive a weight boost
which sectors should be discounted
whether valuation discipline should be stricter or looser

The regime detector should influence later ranking rules. Examples:

In risk_off_defensive, reduce tolerance for speculative sentiment names.
In inflation_reflation or commodity_shock, boost Energy / Materials / selected Industrials where supported by evidence.
In risk_on_growth, allow stronger weight to AI / semis / software if revisions and charts confirm.

Stage 1 scoring rubric

For each sector, compute an informal score from 0 to 100 using:

policy/news tailwind strength: 0–30
breadth of supporting catalysts: 0–20
immediacy of earnings impact: 0–20
durability of theme: 0–15
clarity / confidence of mechanism: 0–15

Call this Macro Tailwind Score.

Stage 2 — Social / retail sentiment analysis

Focus on market sentiment sources such as:

Reddit WSB
Reddit investing/stocks-style communities
other accessible public sentiment sources if available

Preferred architecture

If the runtime has a dedicated social-data tool or plugin, prefer it over free-form browsing. Treat Stage 2 as a data module when possible, not only a narrative step.

Recommended Stage 2 module contract

Inputs:

subreddits: default ["wallstreetbets", "stocks", "investing"]
lookback_window: default 24h, optional 72h and 7d
min_mentions_threshold: default 5 when enough data exists
deduplicate_spam: true by default
exclude_etfs_or_indexes: optional, false by default

Preferred outputs per ticker:

mentions
mentions_acceleration
bullish_count
bearish_count
bullish_ratio
sentiment_heat_score
support_type
speculation_risk

If only partial data exists, degrade gracefully:

If only mention frequency exists, treat the result as attention analysis, not full polarity sentiment.
If bullish/bearish counts are missing, reduce confidence and do not overstate directional conviction.
If spam or duplicate-post filtering is unavailable, raise the speculation-risk estimate.

What to measure

For the broad market:

overall risk-on vs risk-off tone
prevalence of bullish vs bearish language
concentration in index / mega-cap / meme / cyclical themes
whether sentiment is euphoric, improving, cautious, or deteriorating

For single names:

mention frequency
acceleration of mentions
bullish vs bearish balance
whether the narrative is squeeze/speculation-driven or thesis-driven
whether discussion aligns with the macro sectors from Stage 1

Output required for Stage 2

A market mood call: bullish / neutral / bearish
A short explanation of why
The 10 hottest stocks by sentiment attention
For each of the 10 stocks:
- ticker
- dominant narrative
- sentiment label: bullish / bearish / mixed
- whether it is likely speculative or fundamentally supported
- whether the signal is full sentiment or attention-only

Stage 2 scoring rubric

For each stock, form a Sentiment Heat Score from 0 to 100 using:

raw mention intensity: 0–30
mention acceleration: 0–20
bullish/bearish skew: 0–20
cross-source consistency: 0–15
narrative quality (thesis > meme): 0–15

Also assign a Speculation Risk Flag: low / medium / high.

Stage 2 confidence gate

Every Stage 2 conclusion should reflect these checks:

data_freshness: fresh / acceptable / stale
source_count: how many distinct sentiment sources were used
polarity_completeness: full / partial / attention_only
duplicate_filtering: yes / no / unknown
confidence: high / medium / low

If polarity_completeness = attention_only, do not make a strong bullish/bearish market call from Stage 2 alone.

Stage 3 — Fusion logic

Primary rule

Use the market regime detector as a top-level weighting control.

If Stage 1 finds bullish sectors and Stage 2 contains stocks matching those sectors, prioritize the overlap.

Secondary rule

If Stage 1 finds bullish sectors but Stage 2 does not contain good matches, perform a quantified value-investing fallback screen inside those sectors.

Tertiary rule

If Stage 1 does not produce a clear sector conclusion, output the Stage 2 hot-stock list, ranked and filtered for quality.

Fusion ranking formula

For each candidate stock, combine:

Macro Tailwind Score: 30%
Sentiment Heat Score: 20%
Valuation Attractiveness Score: 25%
Quality Score: 15%
Technical Setup Score: 10%

Use judgment to override only when one signal is obviously distorted, for example meme-driven sentiment with broken fundamentals.

Stage 3B — Quantified value-investing fallback screen

When a favored sector lacks sentiment candidates, screen for potentially undervalued stocks using as many of these as available:

PEG relative to sector and growth profile
forward P/E relative to sector
EV/EBITDA relative to sector
free cash flow yield
earnings revision momentum
revenue / EPS growth durability
operating margin resilience
ROIC / ROE quality
debt burden / interest coverage
dividend yield where relevant
price/book where relevant for financials or asset-heavy sectors

Hard fallback preference

Prefer a harder quantified fallback over loose narrative value language.

When enough data exists, compute a Value Fallback Composite Score (0–100) with this default weighting:

PEG score: 25%
Forward P/E relative score: 20%
FCF Yield score: 25%
Revision Momentum score: 20%
Balance Sheet / Quality guardrail score: 10%

Factor definitions

1) PEG score (25%)

Score higher when:

PEG is lower than sector median
PEG is below 1.5, and score especially well below 1.0 when growth quality is credible
avoid over-rewarding ultra-low PEG if earnings quality is weak or cyclical collapse distorts E

Suggested informal mapping:

excellent: PEG < 1.0
good: 1.0–1.5
neutral: 1.5–2.0
weak: > 2.0

2) Forward P/E relative score (20%)

Compare company forward P/E to:

sector median
relevant peer median
own 3Y or 5Y historical band when available

Score higher when the stock trades at a meaningful discount without obvious deterioration in business quality.

3) FCF Yield score (25%)

Prefer higher free cash flow yield, especially when:

FCF is positive and recurring
conversion from earnings to cash is stable
capex intensity does not make FCF misleading

Penalty conditions:

one-off working capital boosts
financially engineered buyback optics unsupported by real cash generation

4) Revision Momentum score (20%)

Measure whether analyst expectations are improving:

upward EPS revisions over 30–90 days
upward revenue revisions when available
fewer estimate cuts than sector peers

Score higher when revisions are improving and the stock still looks inexpensive.

5) Balance Sheet / Quality guardrail score (10%)

Do not let a cheap-looking stock pass if the balance sheet is stressed. Check:

net debt / EBITDA
interest coverage
current ratio where relevant
margin stability
ROIC / ROE quality

Fallback disqualifiers

A stock should usually not survive the fallback screen if several of the following are true:

negative or chronically unstable FCF
heavy leverage with weak interest coverage
falling revisions with no identifiable catalyst
very low multiple caused by structural decline rather than mispricing
direct macro tailwind is weak or ambiguous

Fallback output requirement

For fallback-selected stocks, explicitly show:

why the stock was selected despite not appearing in sentiment overlap
the component factor scores
the final Value Fallback Composite Score
whether the name is a classic value, GARP, or deep value / higher risk setup

Call the final valuation score in fallback mode: Valuation Attractiveness Score and preserve the component breakdown in JSON.

Stage 4 — Quantamental stock analysis and price zones

For each final stock, provide:

A. Business / thesis snapshot

what the company does
why it fits the selected theme
main bull case in 2–4 bullets
main risks in 2–4 bullets

B. Quantamental checklist

Use available metrics such as:

market cap
trailing P/E
forward P/E
PEG if available
EV/EBITDA
price/sales
gross / operating margin
EPS growth
revenue growth
free cash flow trend
debt/equity or net debt/EBITDA
analyst revision direction if available

Then score:

Valuation Attractiveness Score (0–100)
Fundamental Quality Score (0–100)
Theme Alignment Score (0–100)
Risk Score (0–100, where higher means riskier)

C. Price framework

Estimate four levels when data supports it:

Aggressive buy zone
Preferred buy zone
Fair value / hold zone
Trim / take-profit zone

Base these on a mix of:

valuation ranges
support/resistance
moving averages
prior consolidation zones
recent volatility

When exact valuation is unavailable, label the ranges as technical estimate rather than intrinsic value.

Stage 5 — Technical setup and short-term plan

For each final stock, analyze:

trend direction on weekly and daily timeframes
key moving averages (20 / 50 / 200 if available)
support and resistance
breakout vs pullback setup
momentum confirmation or divergence
volume confirmation when available

Then provide a tactical plan:

Short-term stance: buy pullback / breakout watch / avoid / wait / take profit
Entry idea
Stop / invalidation level
First target
Second target
Why this setup works or fails

Do not imply certainty. Present it as a conditional trade plan.

Reason codes and rejection codes

Wherever possible, attach concise structured tags that explain why a candidate passed or failed.

Examples of reason_codes:

MACRO_ENERGY_TAILWIND
MACRO_DEFENSE_SPENDING
REGIME_RISK_OFF_DEFENSIVE
WSB_HEAT_HIGH
WSB_POLARITY_PARTIAL
REVISION_MOMENTUM_STRONG
FCF_YIELD_STRONG
TECH_PULLBACK_TO_50DMA
TECH_BREAKOUT_WITH_VOLUME

Examples of rejection_codes:

SPECULATION_RISK_HIGH
VALUATION_DATA_INCOMPLETE
FUNDAMENTALS_WEAK
NEGATIVE_FCF
BROKEN_CHART
NO_CLEAR_CATALYST

Use short, stable codes so downstream agents can filter, rank, and audit decisions.

Fixed JSON output schema

When structured output is appropriate, emit a fenced json block that follows this schema as closely as possible. Omit unavailable fields or set them to null; do not fabricate values. Use the same single language as the user-facing report for all free-text values inside the JSON. Keep keys in English.

json

{
  "meta": {
    "skill": "market-signal-fusion",
    "version": "1.2.0",
    "language_mode": "auto_single_language",
    "output_language": "zh|en",
    "market_universe": "US equities",
    "as_of_date": "YYYY-MM-DD",
    "time_windows": {
      "macro_news": "last_7d",
      "sentiment": "last_3d",
      "technicals": "daily_weekly"
    },
    "strict_mode": false
  },
  "macro": {
    "market_bias": "bullish|neutral|bearish|mixed",
    "market_regime": {
      "primary": "risk_on_growth|risk_off_defensive|inflation_reflation|rate_sensitive|commodity_shock|earnings_revision_recovery|policy_transition_mixed",
      "secondary": null,
      "evidence": [""],
      "style_implication": ""
    },
    "top_catalysts": [
      {
        "title": "",
        "summary": "",
        "direction": "bullish|bearish|mixed",
        "affected_sectors": [""],
        "mechanism": "",
        "time_horizon": "immediate|medium_term|long_term",
        "confidence": "high|medium|low",
        "confidence_gate": {
          "data_freshness": "fresh|acceptable|stale",
          "source_count": 0,
          "source_quality": "high|medium|low",
          "missing_fields": [""]
        }
      }
    ],
    "favored_sectors": [
      {
        "sector": "",
        "macro_tailwind_score": 0,
        "rationale": ""
      }
    ],
    "headwind_sectors": [""]
  },
  "sentiment": {
    "broad_market_mood": "bullish|neutral|bearish",
    "risk_mode": "risk_on|neutral|risk_off",
    "summary": "",
    "confidence_gate": {
      "data_freshness": "fresh|acceptable|stale",
      "source_count": 0,
      "polarity_completeness": "full|partial|attention_only",
      "duplicate_filtering": "yes|no|unknown",
      "confidence": "high|medium|low"
    },
    "hot_stocks": [
      {
        "ticker": "",
        "company": "",
        "sector": "",
        "dominant_narrative": "",
        "sentiment_label": "bullish|bearish|mixed",
        "signal_type": "full_sentiment|attention_only",
        "sentiment_heat_score": 0,
        "mentions": null,
        "mentions_acceleration": null,
        "bullish_count": null,
        "bearish_count": null,
        "bullish_ratio": null,
        "speculation_risk": "low|medium|high",
        "support_type": "speculative|fundamentally_supported|mixed"
      }
    ]
  },
  "selection": {
    "selection_mode": "macro_sentiment_overlap|macro_value_fallback|sentiment_only_fallback",
    "final_candidates": [
      {
        "ticker": "",
        "company": "",
        "sector": "",
        "selection_reason": "",
        "overlap_type": "macro_plus_sentiment|macro_plus_value|sentiment_only",
        "reason_codes": [""],
        "rejection_codes": [],
        "confidence_gate": {
          "data_freshness": "fresh|acceptable|stale",
          "source_count": 0,
          "source_quality": "high|medium|low",
          "missing_fields": [""]
        },
        "scores": {
          "macro_tailwind_score": 0,
          "sentiment_heat_score": 0,
          "valuation_attractiveness_score": 0,
          "quality_score": 0,
          "technical_setup_score": 0,
          "composite_score": 0
        },
        "value_fallback": {
          "used": true,
          "style_bucket": "classic_value|garp|deep_value_higher_risk|null",
          "component_scores": {
            "peg_score": 0,
            "forward_pe_relative_score": 0,
            "fcf_yield_score": 0,
            "revision_momentum_score": 0,
            "balance_sheet_quality_score": 0
          },
          "value_fallback_composite_score": 0
        }
      }
    ]
  },
  "stock_analysis": [
    {
      "ticker": "",
      "company": "",
      "sector": "",
      "thesis": "",
      "bull_case": [""],
      "risks": [""],
      "reason_codes": [""],
      "rejection_codes": [],
      "confidence_gate": {
        "data_freshness": "fresh|acceptable|stale",
        "source_count": 0,
        "source_quality": "high|medium|low",
        "missing_fields": [""]
      },
      "metrics": {
        "price": null,
        "market_cap": null,
        "trailing_pe": null,
        "forward_pe": null,
        "peg": null,
        "ev_ebitda": null,
        "fcf_yield": null,
        "revenue_growth": null,
        "eps_growth": null,
        "operating_margin": null,
        "net_debt_ebitda": null,
        "revision_momentum": null
      },
      "scores": {
        "valuation_attractiveness_score": 0,
        "fundamental_quality_score": 0,
        "theme_alignment_score": 0,
        "risk_score": 0
      },
      "price_zones": {
        "aggressive_buy_zone": "",
        "preferred_buy_zone": "",
        "fair_value_hold_zone": "",
        "trim_take_profit_zone": "",
        "zone_basis": "valuation|technical|mixed"
      },
      "technical_plan": {
        "trend": "uptrend|sideways|downtrend|mixed",
        "stance": "buy_pullback|breakout_watch|wait|avoid|take_profit",
        "support": [""],
        "resistance": [""],
        "entry_idea": "",
        "stop_invalidation": "",
        "target_1": "",
        "target_2": ""
      }
    }
  ],
  "summary": {
    "best_sector_now": {
      "name": "",
      "reason": ""
    },
    "best_value_candidate": "",
    "best_momentum_candidate": "",
    "market_stance_1_4w": ""
  }
}

Final response template

Use this structure unless the user requests another format.

1) Macro and policy read

Top 3–5 market-moving developments
Sector beneficiaries
Sector headwinds
Ranked favorable sectors with Macro Tailwind Scores

2) Market sentiment read

Broad market mood: bullish / neutral / bearish
One-paragraph explanation in the user's language
Top 10 hottest stocks:
- ticker
- theme
- heat score
- sentiment label
- speculation risk

3) Final selected candidates

For each chosen stock, show:

ticker and company
sector / theme
why selected
overlap type:
- macro + sentiment overlap
- macro + value fallback
- sentiment-only fallback

4) Quantamental scorecard

For each final stock, include:

key valuation metrics
quality metrics
composite view
aggressive buy zone
preferred buy zone
trim zone

5) Tactical trading view

For each final stock, include:

trend
support/resistance
entry style
stop/invalidation
short-term targets

6) Summary judgment

Close with:

most attractive sector right now
strongest value candidate
strongest momentum candidate
broad market stance for the next 1–4 weeks

7) JSON output

After the single-language report, include the JSON block unless the user asked not to.

Decision rules for edge cases

When sentiment is very hot but fundamentals are poor

Say explicitly that the stock is sentiment-driven, not value-supported.

When macro is bullish but charts are broken

Mark as good company / bad timing and prefer waiting for technical repair.

When valuation is attractive but no catalyst exists

Mark as watchlist value, low urgency.

When news and sentiment conflict

Prefer the signal with the clearer time horizon and stronger evidence. Explain the conflict.

When fallback screen is triggered but data is partial

Use the fallback framework with only available factors, state which factors are missing, and lower confidence accordingly.

When strict mode is requested

In strict mode, require stronger evidence to issue a final candidate:

at least one high-quality macro source for Stage 1 conclusions
sentiment polarity better than attention_only for strong sentiment claims
at least 3 of the 5 fallback factors for value mode
at least one technical confirmation signal before issuing an actionable short-term trade plan

Style requirements

Be concrete and investment-oriented.
Avoid vague phrases like “looks interesting” without evidence.
Show a ranking, not just descriptions.
State when a conclusion is data-backed vs inference.
Keep the narrative concise but decision-useful.
Keep human-readable output in a single language matching the user and machine-readable output strict.

Safety and honesty

Do not fabricate prices, multiples, sentiment counts, or technical levels.
If no trustworthy data is available for a requested component, say that component is unavailable.
Remind the user that this is analysis, not guaranteed investment advice.

Market Signal Fusion

Install

Market Signal Fusion

What this skill does

Core operating rules

Language and output rules

Default language behavior

Single-language formatting rules

Machine-readable output rule

Inputs to collect from the user request

Stage 1 — News and macro-political interpretation

Market regime detector

Stage 1 scoring rubric

Stage 2 — Social / retail sentiment analysis

Preferred architecture

Recommended Stage 2 module contract

What to measure

Output required for Stage 2

Stage 2 scoring rubric

Stage 2 confidence gate

Stage 3 — Fusion logic

Primary rule

Secondary rule

Tertiary rule

Fusion ranking formula

Stage 3B — Quantified value-investing fallback screen

Hard fallback preference

Factor definitions

1) PEG score (25%)

2) Forward P/E relative score (20%)

3) FCF Yield score (25%)

4) Revision Momentum score (20%)

5) Balance Sheet / Quality guardrail score (10%)

Fallback disqualifiers

Fallback output requirement

Stage 4 — Quantamental stock analysis and price zones

A. Business / thesis snapshot

B. Quantamental checklist

C. Price framework

Stage 5 — Technical setup and short-term plan

Reason codes and rejection codes

Fixed JSON output schema

Final response template

1) Macro and policy read

2) Market sentiment read

3) Final selected candidates

4) Quantamental scorecard

5) Tactical trading view

6) Summary judgment

7) JSON output

Decision rules for edge cases

When sentiment is very hot but fundamentals are poor

When macro is bullish but charts are broken

When valuation is attractive but no catalyst exists

When news and sentiment conflict

When fallback screen is triggered but data is partial

When strict mode is requested

Style requirements

Safety and honesty

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