Portfolio Swarm Review

Multi-agent portfolio review. Structure mirrors the TauricResearch/TradingAgents design — analysts (Tier 1) → researchers (Tier 2) → risk debators + judge (Tier 3). Each tier is distinct in the output; the Judge synthesizes. (The reference repo is no longer cloned locally; the structure is recorded here.)

Required reads

In this order:

/root/.openclaw/workspace/MEMORY.md
/root/.openclaw/workspace/portfolio.json
/root/.openclaw/workspace/INVESTMENT_SOP.md
/root/.openclaw/workspace/TOOLS.md for data chain detail
../daily-deep-brief/references/technical-playbooks.md before any add / average-down synthesis

Active vs exited comes from portfolio.json alone (shares > 0 / == 0); the hand-maintained summary that used to be step 3 drifted 3.5 months and was deleted (#1067).

Fresh data rule

Refresh quotes before producing conclusions:

/root/.local/bin/clawock analyze-us    # US 7-route fallback
/root/.local/bin/clawock analyze-hk    # HK Tencent + Eastmoney full-batch cross-check/fallback → stooq → yfinance

If a leg is stale, name the exact ticker and limit confidence on conclusions involving it. 00100 only has Tencent — flag explicitly if that leg fails. KR linkage: 07709/07747 are exited, but SKHY (SK Hynix ADR) can be held — check portfolio.json rather than assuming the whole chain is dead.

Holdings bucketing — read each run, do not hardcode

Pull live set from portfolio.json (shares > 0). Stable bucket structure; contents drift:

US growth / single-name beta — active US non-leveraged growth names
US leverage ETF — anything is_leveraged_etf: true
US theme / special situation — catalyst-driven names
HK lower-beta core — index/sector ETFs (e.g. 03032, 03033)
HK single-name — individual equities (e.g. 00100 AI, 02208 wind)
HK leverage ETF — 2x/3x recipes (e.g. 07226)

Regime detection (run first)

Before any role analysis, classify current regime — this calibrates everything downstream:

Regime	Trigger	Implication for sizing
Trending up	Index ADX > 25, MA20 > MA50, RSI 50-70 across book	Momentum-friendly — leveraged ETF holdable, trim only on overheats (RSI > 75)
Trending down	Index ADX > 25, MA20 < MA50, broad lower lows	Risk-off — leveraged ETF decay accelerates, prefer cash, no add
Range-bound	ADX < 20, sideways action, RSI mean-reverting around 50	Mean-reversion plays — T-only, fade extremes
Volatile / regime change	High variance, conflicting MA stacks, sentiment chaos	Reduce size, widen stops, no convictions until clarity returns

Index proxies: 纳指 / QQQ for US growth book; ^HSTECH for HK tech-heavy book.

Tier 1 — Analysts (parallel)

Four analyst roles, run independently. Mirrors tradingagents/agents/analysts/.

Analyst 1 — Position / Market

For each active holding: price vs cost, PnL $ and %, distance to breakeven
Technical state: trend, RSI-14, MA20/50 stance, immediate support/resistance
Classify: core / tactical / weak / leverage-risk
Output: one-line verdict per ticker + strongest/weakest called out

Analyst 2 — Fundamentals

Recent earnings / revenue trend for non-ETF names
Valuation snapshot (P/E, P/S vs sector and history)
Balance sheet headlines for special-situation names (cash runway, debt)
For ETFs: underlying basket health, NAV premium/discount, decay since holding date
Output: per non-ETF holding — "fair / stretched / cheap"; per ETF — "structurally OK / decay-risk now"

Analyst 3 — News / Sentiment

Finnhub news from scripts (analyze_*_stocks.py without --no-news already pulls 7 days + keyword sentiment)
For US names: Reddit (r/wallstreetbets + r/stocks JSON, no auth) + Tavily news/X
For HK names: 雪球 HK 评论区 + 富途社区 (scrapling StealthyFetcher) + Tavily 中文搜索
南向资金当日 net (web search) for HK macro tone
Output per holding: sentiment score -1 to +1 + 1-2 narratives + divergence vs price call-out

Analyst 4 — Cross-Market Linkage

US side: 纳指 / 罗素 / SOX tone; theme threads (stablecoin reg for CRCL, space/defense for RKLB, AI infra threads)
HK side: 恒科 direction; 南向资金 flow; sector policy (风电 / AI / 监管)
Inter-market: US tech overnight → HK tech open relationship; note when the link breaks
Output: supportive / neutral / weak tag per chain, single most important inter-market signal

Tier 2 — Bull vs Bear Researchers

Mirrors tradingagents/agents/researchers/. Run after Tier 1; each researcher reads all four analyst reports and argues a position.

Bull Researcher

Compose the strongest "hold and add" case using Tier 1 outputs
Cite specific analyst findings as evidence (not gut)
Identify what would have to be true for the position to work out
Call out asymmetric upside specifically (leverage, catalyst dates, sentiment-vs-fundamentals gaps)

Bear Researcher

Compose the strongest "trim and avoid" case
Cite specific risk findings, decay math, sentiment topping signals
Identify the worst plausible outcome and what triggers it
Counter the bull's strongest point directly

The output is a debate snippet (not a checklist), 100-200 words each side.

Tier 3 — Risk Debate + Judge

Mirrors tradingagents/agents/risk_mgmt/ + managers/risk_manager.py.

Aggressive Risk Voice

Argues for upside capture; pushes for full sizing on conviction names
Quotes bull's strongest points
Specifically calls out where the conservative voice misses opportunity cost

Conservative Risk Voice

Argues for capital preservation; pushes for trim on weak structure
Quotes bear's strongest points
Specifically calls out where the aggressive voice underestimates tail risk

Neutral Voice

Calls the middle ground — what specifically should size up, what should size down, what stays
Required: pick a side for each contested holding, no "it depends" outputs

Judge (Risk Manager)

Final synthesis. Weighs the three risk voices given:

The user's documented risk preference: aggressive (per workspace MEMORY.md), so the aggressive voice gets weight unless its structural counter is strong
Current regime (from regime detection above)
Data freshness — any stale leg downgrades confidence

Output strategy decisions per ticker. The same ticker may have separate core_position, intraday_t, and risk_rebalance decisions on the same day:

Hold and watch — thesis intact, no action
Trim on rebound — thesis weakening, wait for strength
T-only — no overnight conviction, fade extremes
Add only on trigger — explicit trigger (price / MA cross / earnings / policy)
Cut — thesis broken, exit on next acceptable bid (use sparingly)

Each item: ticker + concrete reason + concrete trigger/level if applicable.

Signal-source weighting (driven_by edge — REQUIRED)

Not every signal source has earned the right to drive a decision. Read the current v2 ledger metrics (decision_metrics.by_driver, by_strategy, and by_condition) and compare n_episodes, average benefit, and date-cluster CI. Never copy a point-in-time rate from an old report. If n is small or the CI crosses zero, call it directional evidence only. Hard catalysts may drive an event/tactical decision; soft sentiment only nudges confidence. Policy-based deleveraging is a separate risk_rebalance decision with driven_by=risk_rule, not a claim of timing edge.

For active-call sizing, the authored confidence is an audit field, not a win probability. Match the proposed action + driver + condition + regime against decision_metrics.hierarchical_calibration.current_group_calibrators. A missing exact row or abstain=true normally contributes zero incremental size. The sole cold-start exception is one packet-approved technical setup tranche at exactly min_tranche_shares, with thesis/risk/lot gates passing and remaining_tranches > 0; this creates prospective evidence and may not be scaled up or repeated while open. edge_supported=false after sufficient evidence disables that exception. Otherwise multiply proposed signal size by signal_size_multiplier and show the calibrated probability, CI and resolved hierarchy level. The table carries only evidence_sufficient=true rows; omitted counts live in current_group_calibrators_omitted / omitted_abstain_reasons. This never cancels a mandatory risk_rebalance + risk_rule hard-cap action, because that is policy rather than timing alpha.

Position / leverage hard caps (REQUIRED — overrides signal logic AND regime)

The drawdown was a construction problem (US β≈4.4, 73% leveraged ETFs, HK 85% one factor), not a signal problem. Before signal-source weighting even applies, the Judge must check these hard caps against the live book and emit a disciplinary trim/cut for any breach:

Cap	Threshold	If breached
Non-leveraged single core	35–60% review; ≤60% mandatory	Review thesis in band; trim only above 60%
Leveraged single name	≤35%	Swap/reduce the leveraged leg
Measured correlated cluster	≤70% with ≥80% book coverage	Reduce the cluster, leveraged member first; one-name clusters do not count
Leveraged ETFs (per leg)	≤50%	Trim leverage to ≤50%
US β vs S&P	≤3.0	De-lever (cut leveraged ETFs first, not the high-conviction single)
Single leveraged ETF stop	−18% vs cost	Hard stop → swap 2x→1x same factor (keep exposure, stop decay)

Rules: every breach must produce a concrete action in Judge synthesis (no "watch"). A 35–60% non-leveraged review-band row is advisory, not a breach, and must not be converted into a trim merely for diversification. Tag true breaches driven_by=risk_rule (disciplinary rebalancing, not news). This is the one exemption from a trending-up/risk-on HOLD default — in a melt-up you trim leverage into strength, not after the drawdown. De-lever by cutting leveraged ETFs (the β source), never by gutting a high-conviction single's thesis. Leveraged-leg directives use the 2x→1x same-factor swap (not liquidation) per brief_preflight.LEV_1X_SWAP; 1x→2x re-entry only on 🧭 regime green. These caps mirror brief_preflight.compute_risk_guardrail / the brief's 「🚦 仓位/杠杆硬闸」 — keep thresholds in sync.

Read risk_discipline.records alongside the detector output and report each open breach's stable ID, age, acknowledgement and execution-evidence state. A plan-local override has no authority; only a durable, reasoned, unexpired override does. While a critical/high record remains open, do not recommend adding the same name, leveraged sleeve or factor. Exits remain legal, as does a same-plan 2×→1× pair whose factor-adjusted exposure decreases.

Confidence scoring

End the report with a confidence score per major call, 0-100%:

Confidence	Calibration
80-100%	All four analysts align, both researchers' strongest cases converge, fresh data, regime clear
60-79%	Most analysts align, one analyst dissents, regime clear
40-59%	Analysts split, regime mixed, or one major data leg stale
20-39%	Conflicting signals, regime change suspected, multiple stale legs
< 20%	Don't act on this read; wait for clarity

Final output structure

Header

Regime: {trending up / trending down / range / volatile}
Data freshness: timestamp + any stale ticker flagged with ⚠️
Book summary: total US PnL, total HK PnL, biggest winner/loser

Tier 1 — Analyst reports

Four sub-sections (Market, Fundamentals, News/Sentiment, Cross-Market). Each terse — tables where data, prose where judgment.

Tier 2 — Bull vs Bear

Two paragraphs, side by side framing.

Tier 3 — Risk debate

Aggressive voice (paragraph)
Conservative voice (paragraph)
Neutral voice (paragraph)

Judge synthesis

Strategy decisions with ticker, strategy_id, action, condition, driver, and reason. Preserve simultaneous strategies instead of forcing one blended verdict.

Confidence calls

Bullet list: "{Action} {Ticker} — confidence XX% — {one-line reason}"

Next-session plan

Concrete plan: what to watch first at the open, which holdings matter most, price/macro triggers that flip the stance.

Style rules

Practical, not academic
Every claim tied to a real ticker in the current book
The four analysts' outputs must be DIFFERENT angles, not the same content reformatted
Bull/Bear must actually disagree on at least one position — if they fully agree, the debate failed and the user should know
Don't let high-conviction theme hide a bad structure (e.g. "RKLB story is strong" doesn't mean today's RSI 75 is a buy)
Tables for any 3+ data points
⚠️ stale data flagged before any conclusion uses it
Final plan concise enough to trade from

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