expect

name

expect

description

Stock expectation analysis. Combines algorithmic technical scoring + structured news/sentiment + LLM macro/narrative context + multi-horizon directional predictions to emit a deterministic BUY / WATCH / HOLD / AVOID / SELL recommendation per stock. Modes: (1) /expect KR or /expect US auto-discover 5 trending stocks, (2) /expect NVDA or /expect 삼성전자 single-ticker deep dive, (3) /expect NVDA,AMD,AVGO multi-ticker batch, (4) /expect ALL or /expect runs both markets. Triggers: expect, expectation, stock picks, what should I buy, hot stocks, trending stocks, buy or sell, 기대값, 종목 추천, 뭐 사야돼, 매수, 매도, 핫한 종목

Expect — Multi-Horizon Buy/Sell Recommender

What this produces

Per stock:

• A deterministic BUY / WATCH / HOLD / AVOID / SELL label, derived from a fixed point table augmented by an LLM-judged macro/narrative score
• A transmission chain of exactly 3 facts: one technical, one news/fundamental, one risk
• A composite score in the range -11 .. +13 with the components broken out (ALGO + NEWS + LLM_CONTEXT)
• An LLM context score (-5 .. +3) with reasoning text capturing macro regime, sector lifecycle, and narrative that the deterministic table can't see
• Independent multi-horizon predictions (1W / 1M / 6M — the Cycle 1Y call is analysis-only, never logged; see RULE C4) logged to data/predictions.db
• An outcome-telemetry sidecar at state/last-outcome-expect.json for the weekly calibration loop

The label is the headline. The horizon predictions are the audit trail. The sidecar feeds future skill improvement.

Modes

/expect KR              5 trending KR stocks
/expect US              5 trending US stocks
/expect 삼성전자         single-stock deep dive (resolve KR name → ticker)
/expect NVDA            single-stock deep dive (US ticker)
/expect NVDA,AMD,AVGO   multi-ticker batch
/expect ALL  or  /expect    full scan (US 5 + KR 5 = 10 stocks)

Input shape	Mode
Bare KR / US / ALL / empty	Discovery + scan
Korean name	Single (resolve via bin/stock-cli search "<name>" --market KR)
English ticker	Single
Comma-separated tickers	Batch (skip discovery)

---

Workflow

Step 1 — Pre-flight: track record + calibration (1 call)

Always run before scoring, even in single-stock mode:

bin/stock-cli track-record --days 30
bin/stock-cli calibration
bin/stock-cli regime --market US   # and/or --market KR for the market(s) you score

Surface to yourself:

• Recent overconfidence buckets (e.g. "0.70-0.80 confidence → 45% actual win rate" → cap output confidence at 0.65 for that band)
• Per-signal performance (if news signal is hitting <50% lately, weight news_score lower until calibration recovers)
• Open predictions for the same tickers (skip re-prediction; reference the existing one)
• Market regime (regime output label): the hard BULL gate — see RULE R1 below. Fetch once per run per market and reuse for every candidate in that market.

Step 2 — Discovery (Market Scan and ALL modes only)

Per market, use WebSearch to surface 10 candidates:

US:  WebSearch: "trending US stocks today site:finviz.com OR site:reuters.com"
US:  WebSearch: "most active stocks [current month] [current year]"
KR:  WebSearch: "한국 증시 급등 종목 오늘 거래량"
KR:  WebSearch: "코스피 코스닥 주목 종목 [current month] [current year]"

Pick stocks that appear in ≥2 results, with selection bias toward:

• Volume surge or news frequency
• Market cap ≥ $2B (US) or ≥ 1T KRW (KR) — avoid micro-cap noise
• No earnings event within ±2 days (handled later as a hard penalty if present)

For Korean names returned, resolve to ticker codes via bin/stock-cli search "<name>" --market KR before Step 3.

In single-ticker and batch modes, skip Step 2. In ALL mode, run discovery for both markets in sequence.

Step 3 — Bulk technical metrics (1 call per market)

bin/stock-cli horizon-metrics-batch NVDA,AMD,AVGO,MSFT,AAPL --market US --days 400
bin/stock-cli fundamentals-batch    NVDA,AMD,AVGO,MSFT,AAPL --market US

bin/stock-cli horizon-metrics-batch 005930,000660,035420,005380,051910 --market KR --days 400
bin/stock-cli fundamentals-batch    005930,000660,035420,005380,051910 --market KR

Each horizon-metrics-batch row gives you ma20, ma50, ma200, rsi14, return_1w, return_1m, return_6m, return_1y, pct_from_52w_high, pct_from_52w_low, max_drawdown_1y, cycle_risk_flag, vol_ratio, overextension_level. Use these directly — do not recompute. overextension_level (NONE/ELEVATED/EXTREME) drives RULE R2.

Step 4 — News + disclosure fetch (per ticker)

# US: Finnhub primary + Alpha Vantage sentiment merge + FMP/yfinance fallbacks
bin/stock-cli news NVDA --market US --limit 5 --since-days 7

# KR: Naver Finance scrape + Open DART regulatory disclosures
bin/stock-cli news 005930        --market KR --limit 5 --since-days 7
bin/stock-cli disclosure 005930              --since-days 7

# Catalyst event gate (R3) — one call per market, all candidates at once.
# Returns per-ticker earnings cap/trim (US) + market-wide macro_trim (US+KR).
bin/stock-cli catalyst gate NVDA,AMD,AVGO,MSFT,AAPL --market US
bin/stock-cli catalyst gate 005930,000660,035420,005380,051910 --market KR

Each news call returns a generated_at timestamp + items with optional sentiment_score (US Alpha Vantage only) and sentiment_label. Disclosures (KR only) include report_nm to scan for material flags (감자 / 유상증자 / 관리종목 / 거래정지).

The catalyst gate call returns by_ticker[SYM] = {cap_label, confidence_trim, next_earnings_date, trading_days_until} plus a market-wide macro_trim, gate_unavailable, and the availability fields earnings_source / macro_available / notes. Fetch it once per market (it fetches the FMP earnings + macro windows once and iterates all tickers in memory; a failed FMP earnings fetch falls back to keyless per-ticker yfinance earnings dates — earnings_source names the feed used). This is the input to RULE R3 (Step 7) and the ALGO 'Earnings event' row (Step 5). If gate_unavailable is true (no source produced data), treat every cap/trim as zero and proceed — the gate is fail-open by design. On a partial outage the gate stays live: macro_available: false means only macro_trim is unavailable (per-ticker earnings caps still apply); check notes for what failed.

If Finnhub returns nothing and the fallback chain produces an empty list, treat news_score's sentiment + volume components as 0 — do not invent.

Step 5 — Compute the algorithmic score (deterministic point table)

ALGO_SCORE — sums to a max of +7.0, floor -3.0 (-4.0 with the earnings penalty). Buckets within each component are mutually exclusive — evaluate top-to-bottom and stop at the first match (if/elif):

Component	Bucket (evaluate in order)	Points	Notes
Trend	MA20 > MA50 > MA200 (full bull stack)	+3.0
	MA20 > MA50 and MA50 ≤ MA200	+1.0
	MA50 < MA200 and MA20 ≤ MA50 (full bear stack)	-1.0
	otherwise (mixed)	0
Momentum	RSI14 ∈ [50, 70]	+1.0	down-weighted 2026-07: momentum signal 24.6% 30d win
	RSI14 ∈ [30, 50)	+0.5
	RSI14 > 70	+0.5	overbought, half credit
	RSI14 < 30	-0.5
Return 1M	return_1m ≥ +0.05 (i.e. ≥ +5%)	+1.5
	0 < return_1m < +0.05	+0.5
	-0.10 < return_1m ≤ 0	0
	return_1m ≤ -0.10	-0.5
Volume	vol_ratio > 1.3 (from horizon-metrics-batch; 5d avg / 50d avg)	+0.5	down-weighted 2026-07: volume signal 23.3% 30d win
	vol_ratio is None or ≤ 1.3	0	None happens on listings with < 50 bars or all-zero windows
Cycle	pct_from_52w_high ≥ -0.10	+1.0	within 10% of 52-week high
	max_drawdown_1y ≤ -0.25	-1.0
	else	0
Earnings event (penalty only)	next earnings within 7 days	-1.0	fed by RULE R3 — apply -1.0 when catalyst gate reports the ticker's trading_days_until ≤ 5 (cap or trim band). R3 then also caps/trims per Step 7.
	else	0

Max positive sum: 3 + 1.0 + 1.5 + 0.5 + 1 + 0 = 7.0. Max negative drag: -1 + -0.5 + -0.5 + -1 = -3.0. Effective floor with earnings event also: -4.0.

2026-07 down-weight rationale: 30-day weekly calibration (2026-07-05) grades momentum (24.6%) and volume (23.3%) statistically dead, while sector (52.6%) and fundamental (67.5%) are the only signals beating coin-flip. The ALGO ceiling drops 8.0 → 7.0, so BUY (COMPOSITE ≥ 8.0) now always requires at least +1.0 of combined news/LLM-context confirmation — mechanics alone can no longer print a BUY.

When a horizon-metrics field is null (insufficient bars on a young listing), assign 0 to that component and append (N/A: <field>) to the transmission chain's RISK slot.

Step 5b — LLM macro/narrative context score (mitigates momentum bias)

The deterministic ALGO_SCORE rewards stocks that are already trending (full bull MA stack, RSI 50-70, return_1m ≥ +5%, near 52W high). This is intentional (momentum factor) but creates a structural bias: a parabolic blow-off top scores identically to a healthy mid-stage uptrend. The 5/15/2026 KOSPI key reversal day exposed this — 000660 SK하이닉스 would have scored ALGO 7.0 + NEWS 1.0 = BUY threshold under the pre-2026-07 weights despite +925% YoY and a confirmed macro top signal.

LLM_CONTEXT_SCORE is your explicit channel to encode macro/narrative context the table can't see. Range -5.0 to +3.0 (asymmetric — bigger negative range mitigates the algorithmic momentum bias which dominates upside).

When to evaluate: Always compute, but spend more effort when ALGO_SCORE >= 6.0 (i.e., the stock would otherwise be WATCH+ on raw mechanics). For ALGO_SCORE < 6.0, default to 0 unless something specific is obvious.

Inputs to consider (gather before scoring):

1. Macro detector outputs (call when ALGO_SCORE >= 6.0):
   • market-top-detector — distribution days, defensive rotation, leadership breakdown
   • ftd-detector — rally attempt / FTD confirmed / post-FTD health
   • macro-regime-detector — Concentration / Broadening / Contraction / Inflationary / Transitional
2. Sector lifecycle (theme-detector or web search):
   • Early-stage breakout / mid-stage run / late-stage parabolic / decay
3. Recent narrative themes from news + web search:
   • Foreign capital flow direction (KR: 외인 순매수/매도 trend)
   • FX context (USD/KRW > 1500 = capital outflow stress)
   • Supply chain disruptions (strikes, geopolitics)
   • Regulatory / antitrust
   • Earnings season binary events nearby
4. Cross-asset confirmation: bonds, related sectors, SOXL vs SPY divergence

Bull / Bear / Judge — commit a thesis before scoring (staged reasoning, adapted from AutoHedge's Director→Quant→Risk + a Fincept-style adversarial debate). The deterministic ALGO/NEWS tables are the Quant stage (already computed). This is the Director/Judge stage; the C2/C3 gates in Step 9 are the Risk stage. Run these four micro-steps in order and do not skip ahead:

1. Director thesis (commit first). Before building either case, write ONE line: your directional lean (BULL / BEAR / NEUTRAL) and the single biggest risk to it, derived only from the inputs above. Committing first reduces motivated reasoning — the debate must then test this thesis, not rationalize the ALGO score.
2. Bull case. The 2-3 strongest specific reasons price goes up (cite numbers: MA stack, return_1m, FTD status, sector stage, sentiment). No vague adjectives.
3. Bear case. The 2-3 strongest specific reasons price goes down — genuinely adversarial, not a strawman. Pull from macro top signals, late-stage sector, FX/flow stress, event risk, valuation. If you cannot write a real bear case, say so explicitly.
4. Judge. Net the two sides into llm_context_score using the rubric below. State which side won and why in llm_context_reasoning, and carry the surviving bear point into the reasoning text so it is preserved for the postmortem. If the bear case materially contradicts the Director thesis, the thesis loses — do not anchor on it.

This debate produces LLM_CONTEXT_SCORE; it does not add a second override channel. The bounded -5.0..+3.0 range and the composite math are unchanged — a more rigorous score is the only output.

Rigor gate (deterministic). Structure the debate as JSON and lint it before trusting a strong score:

bin/stock-cli lint-llm-context '{"score":-3.0,"winner":"bear",
  "bull_points":[{"claim":"...","evidence":"return_1m=+7%","signal_type":"technical"}],
  "bear_points":[{"claim":"KOSPI parabolic","evidence":"+25%/22d","signal_type":"macro"}]}'

The linter enforces: score ∈ [-5,+3]; winner agrees with the score's sign; a real (non-empty) bear_points always present; and when |score| ≥ 2.0 the winning side has ≥1 point with non-empty evidence and a signal_type in {macro, sector, event, flow, valuation, technical, narrative}. If the lint is not clean, dampen the score toward 0 until it is — a strong score with no cited, typed evidence is exactly the noise that made llm_context underperform. Pass the final score into Step 9's --components so its marginal value is measured (component-contribution).

Scoring rubric (anchors — pick the nearest):

Score	Anchor	Example
+3.0	Major macro tailwind aligned with this stock	FTD confirmed last week + sector early breakout + positive narrative
+1.5	Moderate tailwind	Sector in mid-stage uptrend, no top signal, supportive macro regime
+0.5	Mild tailwind	Favorable macro regime, neutral sector
0	Neutral / no specific context	LLM has no strong reason to adjust either way
-1.5	Mild headwind	Late-stage sector, neutral macro
-3.0	Strong headwind	Macro top signal + late-stage sector + specific event risk (e.g., earnings pre-binary)
-5.0	Crisis	Confirmed bear market entry + sector blow-off + fundamentals deteriorating

Interpolate between anchors (e.g., -2.5 is allowed and common).

Required output — emit both:

"llm_context_score": -3.0,
"llm_context_reasoning": "KOSPI 5/15 key reversal day -6.12% off ATH 8047 + 외인 5.56조 매도 + USD/KRW 1500 돌파 = 매크로 톱 확인. 반도체 섹터 late-stage parabolic (+25% / 22d). 삼성 노조 파업 5/21 임박. 펀더 자체는 견고 (NVDA Blackwell 수요)이므로 -5는 아님."

Reasoning text rules (apply to ALL non-zero scores):

• Quote specific numbers, not adjectives ("외인 -5.56조" not "외인 매도 심함")
• Reference at least one named macro signal source ("market-top-detector triggered defensive rotation", "ftd-detector still in rally attempt", "USD/KRW 1500 stress", "distribution day count = 4")
• Explain why this anchor (and not adjacent ones) — e.g., "펀더는 견고 (NVDA Blackwell 수요)이므로 -5는 아님"
• Korean OK for KR market analysis, English for US
• When LLM_CONTEXT_SCORE == 0.0, emit reasoning text anyway ("Neutral — no specific macro/narrative context.") and do NOT include llm_context in signals_used for the horizon predictions (it would pollute calibration)
• Vague reasoning at |score| >= 2.0 is a Quality Gate failure (Step 11 Rule 5) — dampen the score toward 0 if you cannot cite a concrete signal

Calibration honesty:

• Track record shows valuation/cycle/mean_reversion signals at 0% win rate. LLM_CONTEXT_SCORE is your channel to encode these qualitative signals more carefully. Weekly calibration will measure this signal separately under --signals llm_context — be conservative until 6-8 weeks of data confirms it adds alpha.
• When ALGO_SCORE is high (+6 or +7), be wary of confirming with a positive LLM_CONTEXT_SCORE (avoid double-counting bullish momentum). Most algorithmic BUY candidates should get LLM_CONTEXT in [-2.0, +1.0] range.
• Signal re-weighting (2026-07): 30-day calibration shows sector (52.6%) and fundamental (67.5%) are the only signals beating coin-flip; technical/momentum/news/volume/cross_market grade dead. Ground positive LLM_CONTEXT scores in sector-RS confirmation or a concrete fundamental catalyst — never award positive context on technical/momentum grounds alone (those are already in ALGO and empirically dead).

Recursion guard: this skill can call market-top-detector, ftd-detector, macro-regime-detector, theme-detector. None of those currently call /expect. If that changes, add a --no-gate-recursion flag to suppress the macro context call.

Step 6 — Compute the news score (deterministic point table)

NEWS_SCORE — sums to a max of +3.0, can be hard-capped negative. Read the signal block in the news output (deduped, recency-weighted) — do not re-derive these from raw items. Sentiment buckets are mutually exclusive and use signal.recency_weighted_sentiment (fresh headlines dominate a week-old blip); if it is null (no AV score — KR or US without key), Sentiment = 0.

Component	Bucket (evaluate in order)	Points
Sentiment (signal.recency_weighted_sentiment available)	> +0.15	+2.0
	0 < rw ≤ +0.15	+1.0
	-0.15 ≤ rw ≤ 0	-1.0
	rw < -0.15	-2.0
Sentiment (null)	n/a	0
Headline volume	signal.unique_count ≥ 3 (deduped)	+1.0
	else	0

Then apply hard caps after summing the above:

• Negative catalyst. If signal.has_negative_catalyst is true (the module scans for bankrupt/fraud/lawsuit/downgrade/investigation/recall/delist/guidance cut/etc.) → set NEWS_SCORE = min(NEWS_SCORE, -2).
• KR disclosure flag. If any disclosure report_nm contains: 감자, 유상증자, 관리종목, 거래정지, 상장폐지 → set NEWS_SCORE = min(NEWS_SCORE, -2).

Hard caps override even strongly positive sentiment — that is the whole point of the cap. Pass signal.event_tags into Step 5b: a hard catalyst (earnings/guidance/M&A/regulatory) is exactly the kind of specific, dated fact the LLM_CONTEXT bear/bull debate should weigh.

Max positive sum: 2 + 1 = 3.0. Floor without hard caps: -2 + 0 = -2.0. With a hard cap firing: -2.0 (caps clamp, they don't stack).

Step 7 — Composite + decision label

COMPOSITE = ALGO_SCORE + NEWS_SCORE + LLM_CONTEXT_SCORE
  range:  -11.0 (worst case)  ..  +13.0 (perfect)
  (algo floor -4.0 with earnings penalty + news floor -2.0 + llm_context floor -5.0)

Label mapping (contiguous half-open ranges — every score lands in exactly one bucket):
  COMPOSITE >= 8.0          →  BUY     high-conviction long
  6.0 <= COMPOSITE < 8.0    →  WATCH   wait for pullback or confirmation
  3.0 <= COMPOSITE < 6.0    →  HOLD    no action; keep if already held
  0.0 <= COMPOSITE < 3.0    →  AVOID   do not initiate
  COMPOSITE < 0.0           →  SELL    exit if held; never short

Round COMPOSITE to one decimal in the output.

RULE R1 — Hard regime gate (applies to BULL labels/horizons before logging). Read the regime verdict (Step 1) for the candidate's market and apply:

• RISK_OFF (label == "RISK_OFF"): do not issue new BUY or log BULL horizons. Cap the label at WATCH and resolve would-be BULL horizons to NEUTRAL. Emit "⚠️ REGIME RISK_OFF — new longs gated" in the per-stock detail.
• NEUTRAL: raise the BUY bar from 8.0 to 9.0 (a NEUTRAL regime demands a stronger setup) and trim logged confidence one extra step (cap at 0.60). Emit "⚠️ REGIME NEUTRAL — higher bar".
• RISK_ON: no change.

Rationale: backfilled over the June 2026 drawdown, BULL calls issued while the worse-of-SPY/QQQ (US) or KODEX 200 (KR) regime was NEUTRAL/RISK_OFF won only ~3%; the gate suppresses or hardens exactly those. It is a market-level gate — per-stock overextension is RULE R2. The gate does not raise the bar for BEAR/NEUTRAL calls (those already clear RULE C3).

RULE R2 — Per-stock overextension gate (parabolic/blow-off entries). Read overextension_level from the candidate's horizon-metrics row:

• EXTREME: do not issue BUY or log BULL horizons — cap the label at WATCH, resolve would-be BULL horizons to NEUTRAL. Emit "⚠️ OVEREXTENDED (EXTREME)".
• ELEVATED: raise the BUY bar by +1.0 (BUY needs COMPOSITE ≥ 9.0) and trim logged confidence one step (cap 0.60). Emit "⚠️ OVEREXTENDED (ELEVATED)".
• NONE: no change.

Rationale: on 377 closed BULL, entries with RSI14 > 75 won 26% (vs 47% at RSI < 60) and entries > 15% above MA20 won 30% (vs 66% in the 3–8% band). A blow-off entry is a strong anti-signal that the broad regime gate (R1) cannot see.

RULE R3 — Event-risk gate (imminent binary catalysts). Read the catalyst gate output (Step 4) for the candidate's market. R3 is a risk gate only — it never raises the BUY bar and never issues a BUY.

• Earnings (per-ticker, US only). From by_ticker[SYM]:
  • cap_label == "WATCH" (next earnings trading_days_until ≤ 2): cap the label at WATCH, resolve would-be BULL horizons to NEUTRAL. Emit "⚠️ EARNINGS in N td — entry gated". Don't hold a directional thesis through a coin-flip gap.
  • confidence_trim == 0.05 (2 < td ≤ 5): trim logged confidence by 0.05. Emit "⚠️ earnings in N td — confidence trimmed".
• Macro (market-wide, US + KR). If macro_trim == 0.05 (a High-impact FOMC/CPI/NFP within 2 td), trim every pick in that market by 0.05 and emit "⚠️ macro event () in N td". KR consumes the US macro stream (it transmits via FX / SOXL); KR never gets a per-ticker earnings cap (no forward KR EPS feed).
• Macro risk-off switch (market-wide, US + KR). Run bin/stock-cli macro-news once per run and read risk.risk_level — a deterministic keyword tripwire over global macro/geopolitical headlines (war/invasion, oil supply shock, emergency central-bank action, market crash / circuit-breaker / sovereign default, tariff/sanctions escalation).
  • RISK_OFF: log no new BULL predictions this run — cap every label at WATCH, resolve would-be BULL horizons to NEUTRAL. Emit "⚠️ MACRO RISK_OFF" citing the matched evidence headlines.
  • ELEVATED: trim every pick's logged confidence by an additional 0.05 (stacks with the R3 earnings/macro trims above). Emit "⚠️ MACRO ELEVATED".
  • Fail-open: if the fetch errors or no headlines are available, the output says NORMAL with a note — treat as NORMAL.
• Fail-open, visibly. If gate_unavailable is true (no source produced data), treat all caps/trims as zero. If only macro_available is false (partial outage — e.g. the FMP economic calendar 402s), only macro_trim is unavailable; per-ticker earnings caps still apply (earnings_source names the feed used — "fmp" or the keyless "yfinance" fallback; notes says what failed).

Rationale: the deterministic ALGO/NEWS/LLM_CONTEXT score cannot see dated binary events. A BUY logged the day before the report or the Fed gaps on the print, not the thesis — exactly the entries R3 caps or trims.

R1 + R2 + R3 stacking (explicit): apply all three gates cumulatively.

• A WATCH cap from any rule wins: if R1 is RISK_OFF or R2 is EXTREME or R3 earnings is WATCH, the label is WATCH (no BULL logged), full stop.
• The BUY-bar raises are additive and come only from R1/R2: base 8.0, +1.0 if R1 NEUTRAL, +1.0 if R2 ELEVATED → e.g. NEUTRAL regime and ELEVATED stock requires COMPOSITE ≥ 10.0. R3 never raises the bar.
• Confidence is pulled down by the strictest of all applicable caps/trims: take the minimum of the R1/R2 caps (each 0.60) and then subtract the R3 earnings trim (−0.05) and the R3 macro trim (−0.05) that apply to the pick (a pick can eat both R3 trims at once).

Threshold rationale: BUY at 8.0 with the ALGO ceiling at 7.0 (2026-07 down-weight) means even a perfect algorithmic setup needs at least +1.0 of combined news/LLM-context confirmation (e.g. NEWS ≥ +1 or LLM_CONTEXT ≥ +1) — and a strongly bearish LLM_CONTEXT (-3) is sufficient on its own to downgrade an otherwise-BUY signal to HOLD. This is the explicit anti-momentum-bias circuit.

Worked example — 000660 SK하이닉스 on 2026-05-14 (pre-crash):

• ALGO = 6.5 (trend +3, momentum +0.5, return_1m +1.5, volume +0.5, cycle +1.0; 2026-07 weights)
• NEWS = 1.0 (5 headlines/7d, no AV sentiment, no hard cap)
• LLM_CONTEXT = -3.0 (KOSPI parabolic +25% / 22d, magazine cover "1만피" target, FX 1500 stress, sector late-stage)
• COMPOSITE = 4.5 → HOLD (macro context overrides an otherwise WATCH-adjacent setup)

Under the pre-2026-07 weights the system would have labelled this BUY at the very top of a blow-off without LLM_CONTEXT; with the down-weighted table it sits at WATCH (7.5) on mechanics alone, and the context score pushes it firmly to HOLD — two independent layers now block the same failure mode.

Step 8 — Transmission chain (exactly 3 facts)

For every BUY / WATCH / SELL stock, emit a transmission chain — three lines, one each from technical, news/fundamental, and risk slots:

TECH:  RSI14=62 above midline; MA20 > MA50 > MA200 stack
NEWS:  Finnhub avg sentiment +0.21 across 5 articles in last 7d
RISK:  earnings 18d out — no event risk; cycle_risk_flag=False

Format rules:

• Specific numbers, not adjectives. "RSI14=62" not "RSI is bullish."
• One line per slot. If a slot has no relevant fact, write "—" rather than padding.
• Quote the data field name when relevant (return_1m=+7.4%) so a future weekly aggregator can parse it.
• For HOLD / AVOID labels, the transmission chain is optional but recommended.
• The 3 transmission chain slots (tech/news/risk) are separate from llm_context_reasoning — do NOT merge the macro narrative into the RISK slot. RISK should cover stock-specific risks (earnings event, cycle_risk_flag, drawdown); macro/sector lifecycle lives in llm_context_reasoning only.

Step 9 — Multi-horizon predictions (existing logic, preserved)

Independent of the composite label, produce 4 horizon-level direction calls per stock so the prediction DB tracks calibration over time:

Horizon	Timeframe	Inputs	Direction logic
Short	1W	RSI14, MA20 position, return_1w, news sentiment	Momentum
Medium	1M	MA50 position, return_1m, P/E, upcoming earnings	Trend
Long	6M	MA200 position, return_6m, sector	Trend confirmation
Cycle	1Y	return_1y, pct_from_52w_high, max_drawdown_1y, valuation	Mean-reversion (analysis-only — never logged; RULE C4)

Direction = BULL / BEAR / NEUTRAL. Confidence ∈ [0.50, 0.85] using the 4-signal-alignment rule (4 aligned → 0.75-0.85, 3 → 0.60-0.74, 2 → 0.50-0.59, mixed → 0.50 NEUTRAL).

Conflict gate (RULE C1 — Cycle vs Short): If cycle.direction == BEAR and short.direction == BULL, cap composite confidence at 0.60 and emit "⚠️ CYCLE RISK" in the per-stock detail.

Risk/edge gate (RULE C2 — reward:risk floor): Before saving any directional horizon, compute reward:risk from the entry/target/stop you would log: reward = |target − entry|, risk = |entry − stop|. If reward / risk < 1.5, the edge is too thin — do not log a directional row; downgrade that horizon to NEUTRAL (which is not saved). This kills low-edge predictions that dilute the track record.

BEAR edge bar (RULE C3 — higher bar for downside calls): Our measured BEAR hit rate is ~6% vs ~61% for BULL — the system cannot reliably forecast declines. Therefore a BEAR horizon may only be logged when all of: (a) ≥3-signal alignment, (b) macro/cycle confirmation present (LLM_CONTEXT_SCORE ≤ −2.0 or cycle_risk_flag == True), and (c) reward:risk ≥ 2.0. Otherwise emit NEUTRAL instead of BEAR. Note: under --source LIVE the prediction store hard-rejects BEAR rows (they error out), so for scheduler/cron runs always resolve a would-be BEAR to NEUTRAL rather than attempting to save it.

1Y logging gate (RULE C4 — Cycle horizon is analysis-only): Our measured LIVE 1Y track record is 0/12 hits with average outcome return −23.8% (vs 6M −7.1%, 1M −3.3%, 1W −0.8%) — the system cannot forecast a year out. Still compute the Cycle (1Y) direction (it feeds RULE C1 and the per-stock narrative), but never save a 1Y prediction row — cap the longest logged horizon at 6M. Note: the prediction store hard-rejects LIVE 1Y rows (they error out); INTERACTIVE remains a deliberate manual override.

Save each horizon ≥ 0.60 confidence (after RULE C2/C3/C4) as a separate prediction row, all sharing the same --analysis-group-id UUID per stock. Pass your raw confidence and add --recalibrate — the CLI maps it through the isotonic recalibration curve deterministically before storing (e.g. raw 0.62 in an overconfident band → logged ~0.50), so logged confidence reflects observed accuracy rather than a near-constant ~0.6. Do not hand-apply the map yourself; the flag does it consistently (and is a safe no-op until ≥30 closed predictions of that source exist):

GROUP_ID=$(uv run python -c "import uuid; print(uuid.uuid4())")

bin/stock-cli predict create \
  --ticker NVDA --market US --direction BULL \
  --confidence 0.72 --timeframe 1W \
  --entry-price 130.50 --target-price 138 --stop-price 125 \
  --reasoning "RSI 62, MA20+7.3%, Finnhub sentiment +0.21, no earnings risk" \
  --signals technical,news,momentum \
  --components '{"algo":7.0,"news":1.0,"llm_context":-1.5,"overextension":"NONE","regime":"RISK_ON","news_signal":{"unique_count":4,"mean_sentiment":0.15,"recency_weighted_sentiment":0.21,"event_tags":["earnings"],"has_positive_catalyst":false,"has_negative_catalyst":false}}' \
  --recalibrate \
  --analysis-group-id "$GROUP_ID"

Always pass --components with the per-pillar contributions for this call (algo = ALGO_SCORE, news = NEWS_SCORE, llm_context = LLM_CONTEXT_SCORE, overextension = the R2 level, regime = the R1 label). It is stored for capability-contribution analysis (bin/stock-cli component-contribution) and a future blended confidence — so the three capabilities can be measured separately.

Also include news_signal — from the signal block of the Step 4 news output, copy exactly these six keys (unchanged values) into --components as news_signal: unique_count, mean_sentiment, recency_weighted_sentiment, event_tags, has_positive_catalyst, has_negative_catalyst. Omit every other key (raw_count, etc.). The collapsed NEWS_SCORE is graded near-dead; persisting the raw fields lets bin/stock-cli news-tag-performance learn which catalyst tags actually predict.

The JSON output echoes raw_confidence and recalibration_applied so you can confirm the transform. --signals should be a comma-separated subset of: technical, news, fundamental, momentum, volume, disclosure, llm_context. Do not record cycle, valuation, or mean_reversion — these are graded statistically dead (0% hit rate) and only pollute per-signal calibration; route any such qualitative read through LLM_CONTEXT_SCORE instead. Include llm_context whenever LLM_CONTEXT_SCORE is non-zero so the weekly calibration aggregator can isolate its contribution to forecast accuracy. The weekly calibration aggregator (Stage 6) decomposes these to find which signals over- or under-perform.

Generate a fresh GROUP_ID for each stock — never reuse across tickers.

Step 10 — Outcome telemetry sidecar (mandatory)

After the run completes (all stocks scored, all predictions saved), write state/last-outcome-expect.json:

{
  "run_id": "<UUID>",
  "generated_at": "2026-05-10T19:42:11+09:00",
  "mode": "ALL",
  "markets": ["US", "KR"],
  "track_record_snapshot": {
    "win_rate_30d": 0.58,
    "brier_30d": 0.21,
    "overconfident_buckets": ["0.70-0.80"]
  },
  "picks": [
    {
      "ticker": "NVDA",
      "market": "US",
      "label": "BUY",
      "composite": 10.0,
      "algo_score": 7.0,
      "algo_components": {"trend": 3.0, "momentum": 1.0, "return_1m": 1.5, "volume": 0.5, "cycle": 1.0, "earnings": 0},
      "news_score": 3.0,
      "news_components": {"sentiment": 2.0, "headline_volume": 1.0, "neg_keyword_cap": false, "disclosure_cap": false},
      "llm_context_score": 0.0,
      "llm_context_reasoning": "Neutral macro: FTD post-confirmation health intact, AI sector mid-stage, no specific event risk. Algo + news already capture the bull case so no further uplift warranted.",
      "transmission_chain": {
        "tech": "RSI14=62 above midline; MA20>MA50>MA200 stack",
        "news": "Finnhub avg sentiment +0.21 across 5 articles in last 7d",
        "risk": "earnings 18d out — no event risk; cycle_risk_flag=False"
      },
      "horizons_logged": ["1W", "1M"],
      "analysis_group_id": "<UUID>"
    }
  ]
}

Use Bash + jq (or a small Python heredoc) to construct it. The sidecar is the input to Stage 6's weekly calibration — every component score is captured so we can attribute drift later.

Field names are part of the contract — use them exactly. Stage 6 (and any future aggregator) parses these JSON keys by name, so the freeform tendency to shorten algo_score/news_score to algo/news, or horizons_logged to horizons, will silently break the downstream consumer. The schema is:

Top-level	Type	Notes
run_id	str (UUID)	one per /expect invocation
generated_at	ISO-8601 with offset	e.g. "2026-05-11T10:42:11+09:00"
mode	"US" / "KR" / "ALL" / "single" / "batch"	matches invocation form
markets	list of "US" / "KR"	which markets were scanned
track_record_snapshot	object	see example above
picks	list of pick objects	one per ticker (BUY/WATCH/HOLD/AVOID/SELL all included)

Each pick object:

Field	Type	Required?
ticker	str	yes
market	"US" / "KR"	yes
label	"BUY" / "WATCH" / "HOLD" / "AVOID" / "SELL"	yes
composite	float, 1 decimal	yes
algo_score	float	yes — NOT algo
news_score	float	yes — NOT news
algo_components	object with trend, momentum, return_1m, volume, cycle, earnings	yes
news_components	object with sentiment, headline_volume, neg_keyword_cap, disclosure_cap	yes
llm_context_score	float in [-5.0, +3.0], 1 decimal	yes — NOT llm_score or context_score
llm_context_reasoning	str, 1-3 sentences citing macro/sector/narrative signals	yes (use "Neutral; no specific context." when score is 0)
transmission_chain	object with tech, news, risk	yes for BUY/WATCH/SELL; optional for HOLD/AVOID
horizons_logged	list of "1W"/"1M"/"6M" ("1Y" is never logged — RULE C4)	yes (empty list if none ≥ 0.60 conf)
analysis_group_id	UUID str or null	yes — null when no horizons were logged

Before writing the file, mental-check the keys against this table. If you used algo instead of algo_score, fix it before serialising — downstream parsers don't gracefully degrade.

Step 11 — Quality gate (run before output)

Before printing the final markdown, sanity-check each pick. If any check fails, fix the inconsistency and re-derive — do not paper over:

1. Sign agreement: label = BUY/WATCH ⇒ composite ≥ 6; label = SELL/AVOID ⇒ composite ≤ 2. Inconsistencies indicate a scoring bug.
2. Target separation: if a horizon prediction is logged, target_price and stop_price differ from entry_price by at least one ATR-equivalent (use max_drawdown_1y / 12 as a rough proxy if ATR not available).
3. Transmission chain hygiene: each slot quotes a specific number or named fact. No bare adjectives.
4. Fresh-data check: every news and horizon-metrics-batch generated_at is within 1 hour of now. If older, re-fetch.
5. LLM_CONTEXT_SCORE justification: if |llm_context_score| >= 2.0, llm_context_reasoning must cite at least one specific macro/sector/narrative signal (named detector output, FX number, sector lifecycle phase, etc.) — no vague "market feels risky" justifications. If reasoning is weak, dampen score toward 0.
6. Double-counting guard: if algo_score >= 6.0 and llm_context_score >= +2.0, recheck — you may be double-counting bullish momentum already captured by trend/momentum/cycle algo components. Most algorithmic BUY candidates should get llm_context_score in [-2.0, +1.0].

Step 12 — Bias checklist (informs LLM_CONTEXT_SCORE; final audit)

The 4 retail biases below are inputs into your LLM_CONTEXT_SCORE — they should already be reflected in the score and reasoning text. This step is a final cross-check that the score wasn't infected by these biases:

• Recency: am I weighting yesterday's news above the 1Y trend? If yes, my LLM_CONTEXT_SCORE may be over-negative (or over-positive on a 1-day pop). Dampen toward 0.
• Confirmation: did I read past contradicting headlines? If only positives went into the reasoning, recheck and rebalance.
• Anchoring: is the prediction anchored to the discovery price rather than current price? Cross-check with entry_price field.
• Overconfidence: is the composite ≥ 10 with weak news + thin volume + minimal LLM_CONTEXT justification? Step down to WATCH.

Note in the per-stock detail if any of these fired and what was adjusted in the LLM_CONTEXT_SCORE.

---

Output format

## [US|KR|ALL] Market Expectation — YYYY-MM-DD

### Headline picks

| # | Ticker | Price | Label | Composite | Algo / News / Context | Horizons logged |
|---|--------|-------|-------|-----------|------------------------|-----------------|
| 1 | NVDA   | $130  | **BUY**   | 10.0 | 7.0 / 3.0 / 0.0 | 1W, 1M |
| 2 | 000660 | ₩1.82M| HOLD  | 4.5 | 6.5 / 1.0 / **-3.0** | — (LLM context downgraded a WATCH-adjacent setup) |
| 3 | XYZ    | $42   | SELL  | -1.5 | -0.5 / -1.0 / 0.0 | — |

Track record: 58% win rate over last 30 days, Brier 0.21.
Overconfidence flag in the 0.70-0.80 bucket — output confidence reduced 5%.

### Per-stock detail

#### 1. NVDA ($130) — BUY (composite 10.0)

**Transmission chain:**
- TECH: RSI14=62 above midline; MA20>MA50>MA200 stack
- NEWS: Finnhub avg sentiment +0.21 across 5 articles in last 7d
- RISK: earnings 18d out — no event risk; cycle_risk_flag=False

**Algo (7.0/7):** Trend +3.0, Momentum +1.0, Return_1M +1.5, Volume +0.5, Cycle +1.0
**News (3.0/3):** Sentiment +2.0, Headline_volume +1.0, no hard caps fired
**LLM Context (0.0):** Neutral macro — FTD post-confirmation health intact, AI sector mid-stage. No specific event risk.

**Horizons:**
- Short (1W) — BULL 0.72 → logged
- Medium (1M) — BULL 0.65 → logged
- Long (6M) — NEUTRAL 0.55 → not logged
- Cycle (1Y) — BULL 0.62 → narrative only (RULE C4: LIVE 1Y store-gated)

**Bias check:** none triggered.

#### 2. 000660 SK하이닉스 (₩1,819,000) — HOLD (composite 4.5, LLM_CONTEXT downgrade)

**Transmission chain:**
- TECH: MA20>MA50>MA200 stack; RSI14=71.9 OVERBOUGHT; return_1m=+64.9%
- NEWS: 5 headlines/7d including "검은 월요일" macro panic; treasury share disposal 5/13
- RISK: cycle_risk_flag=True; parabolic blow-off context; US semis -4% Fri

**Algo (6.5/7):** Trend +3.0, Momentum +0.5, Return_1M +1.5, Volume +0.5, Cycle +1.0
**News (1.0/3):** Headline_volume +1.0
**LLM Context (-3.0):** KOSPI 5/15 key reversal day -6.12% off ATH 8047 + 외인 -5.56조 매도 + USD/KRW 1500 돌파 = market-top-detector defensive. Sector late-stage parabolic (+25% / 22d). Samsung 노조 파업 5/21 imminent. Fundamentals intact (NVDA Blackwell demand, HBM cycle), so -3.0 (not -5.0).

**Under pre-2026-07 weights this printed a raw BUY at the very top** (now WATCH 7.5 on mechanics alone). Anti-momentum-bias circuit fired correctly.

#### 3. MU ($426) — WATCH (composite 7.0, capped by RULE C1)
...

After printing, sidecar JSON path is shown to the user:

state/last-outcome-expect.json written.

Output verbosity by mode

To prevent long outputs in /expect ALL (10 stocks × 15 lines = ~150 lines of detail), scale the per-stock detail by mode:

Mode	Per-stock detail
/expect <single ticker>	Full block as shown above (transmission chain + components + horizons + bias check).
/expect <comma-list> (≤ 5 tickers)	Full block.
/expect KR / /expect US (5 each)	Full block.
/expect ALL (10 stocks)	Abbreviated block per stock: 1-line transmission chain (semicolon-joined), 1-line component summary, 1-line horizons summary. Reserve full per-stock detail for any pick that triggered RULE C1 or has a hard-cap news flag.

In ALL mode, also drop AVOID rows below the table (a one-line note per skipped ticker is enough — the sidecar JSON has the details for anyone who wants them).

---

Important notes

1. No news available. If bin/stock-cli news returns 0 items and the fallback chain is exhausted, set sentiment + headline-volume components to 0 and flag in the bias check ("Insufficient news — relying on technical analysis only").
2. KR small caps. Market cap under 500B KRW → annotate "Liquidity risk" and cap label at WATCH (no BUY).
3. Earnings within ±2 days. Cap label at WATCH; the -1 algorithmic penalty is not enough on its own for short horizons.
4. Duplicate predictions. If predict list --status OPEN --ticker <t> returns a row, do not create a new horizon prediction for that timeframe; reference the existing one in the per-stock detail.
5. Korean text encoding. Stock CLI emits UTF-8. If passing through tools that mangle Korean characters, prefer the ticker code over the company name.
6. Never short. SELL means "exit if held," never "open short position." This is a long-only system.

---

Calling skills from inside /expect (composition, not absorption)

/expect orchestrates other active skills as gates rather than re-implementing their logic. These now feed into LLM_CONTEXT_SCORE (Step 5b), so the call threshold has moved earlier in the pipeline:

• market-top-detector and ftd-detector: invoke when ALGO_SCORE >= 6.0 (i.e., any candidate that would land WATCH+ on mechanics). Their outputs are the primary input to a negative llm_context_score when defensive signals fire.
• macro-regime-detector: invoke once per /expect run (or once per market) and cite the regime explicitly in llm_context_reasoning for every pick.
• theme-detector: if a stock's primary theme is in late-stage decay, push llm_context_score toward -1.5 or worse and note the theme phase.
• prediction-review: surface specific tickers with poor recent calibration — adjust their confidence floor (separate from LLM_CONTEXT_SCORE).
• position-sizer: link out for actual share-count math; do not output share counts directly.

Latency budget: macro detector calls are amortised across all picks in a market scan — call once per /expect invocation, reuse the output for each ticker. The goal is /expect ALL finishing in under 5 minutes even with macro context enabled.

Recursion guard: none of the gate skills above currently call /expect. If any of them are extended in future to invoke /expect, this composition pattern would loop — track this in a CLAUDE.md note before adding a back-reference, or add a --no-gate-recursion flag to suppress the macro-context call.