stock-analysis

name	stock-analysis
description	Run a Goldman Sachs + Citadel combined fundamental and technical analysis on a specific ticker - a quick analytical deep-dive. Use when the user asks about a specific stock, wants a deep dive, or asks for entry/exit points. For a full bull/bear multi-agent debate hand off to adversarial-research.

Stock Analysis (Goldman Sachs + Citadel)

Data grounding (REQUIRED - anti-hallucination contract)

Every numeric claim (price, P/E, RSI, target, FCF, weight) MUST come from a tool call in THIS run. After fetching, restate the verified figures in a short "Verified data" block and cite ONLY those numbers downstream. If a figure is not in any tool response, say "not available" - never estimate, recall, or invent it. WebSearch is allowed only for narrative (earnings quotes, upgrades), not for numbers that a tool already provides.

Stage 0 - Decision Memory (load BEFORE forming any verdict)

Before fetching market data, load prior decisions on this ticker so the verdict stays consistent across sessions:

• mcp__aifolimizer__get_ticker_decision_history with ticker=TICKER, max_decisions=5 - prior actions, outcomes, reflections
• mcp__aifolimizer__get_ticker_reflection with symbol=TICKER, n=3 - prior recs + realized alpha
• mcp__aifolimizer__get_cross_ticker_lessons with max_lessons=3 - portfolio-level win/loss patterns

Reconciliation rule: if a prior decision exists and your new read flips it, state explicitly WHY it changed (new data / catalyst / price move). Never silently contradict a logged decision - that drift is exactly what this prevents.

How to run

1. Call mcp__aifolimizer__get_profile - account types, cash balances, total capital. Frame tax placement recommendation at end
2. Identify ticker user is asking about (or use largest position if unspecified)
3. Call mcp__aifolimizer__get_portfolio - confirm ticker is in portfolio + get cost basis and current weight
4. Call mcp__aifolimizer__get_fundamentals with symbols=[ticker] - P/E, EPS, dividend yield, market cap, analyst target, institutional ownership, beta
5. Call mcp__aifolimizer__get_technicals with symbols=[ticker] - SMA20/50/200, RSI, MACD, Bollinger Bands, trend signal
6. Call mcp__aifolimizer__get_news_headlines with ticker=ticker - recent news
7. Call mcp__aifolimizer__get_positioning_signals with symbols=[ticker] - crowding score, institutional ownership, short interest, headline velocity. Flag "edge already priced" before issuing buy
8. Call mcp__aifolimizer__get_insider_sentiment with ticker=ticker - insider MSPR (net buying-pressure) trend; feeds fundamental item 6 (insider trend). US-listed only
9. Call mcp__aifolimizer__get_finnhub_news with ticker=ticker - news bull/bear tally + net_sentiment; cross-check the get_news_headlines narrative for divergence
10. Call mcp__aifolimizer__get_recent_filings with ticker=ticker - recent material SEC filings; flag any 8-K filed in the last 5 days as event risk before issuing a call. US-listed only
11. Call mcp__aifolimizer__get_factor_exposure with ticker=ticker - dominant style factor (value/momentum/quality/size); use to pick which INVESTOR LENS applies 11b. (US tickers only, when Buffett lens applies) Call mcp__aifolimizer__get_dcf_valuation with symbol=ticker for the FCF history (owner-earnings anchor) and mcp__aifolimizer__get_sec_financials with symbols=[ticker] for the 3-4yr revenue/income/EPS trend (capital-allocation read). Skip for .TO names - EDGAR has no Canadian filings
12. Use MCP data as primary source. WebSearch only for: recent earnings call quotes, analyst upgrade/downgrade news, or gaps in MCP response

Investor profile

• Canadian retail investor
• Time horizons: short-term trading + long-term (10yr+) holding
• Account types and capital: always read from get_profile - never hardcode

Output structure

FUNDAMENTAL (Goldman Sachs)

1. Business model and primary revenue streams
2. Financial health: revenue trend, margins, cash flow (3yr)
3. Competitive moat rating (none/narrow/wide) with reasoning
4. Growth catalysts (next 12 months) and key headwinds
5. Valuation vs sector peers: P/E, P/S, EV/EBITDA
6. Insider trading and institutional ownership trend - cite get_insider_sentiment avg_mspr + net_signal (bullish/bearish/neutral)
7. Bear case + bull case with 12-month price targets
8. Recommendation: Buy / Hold / Sell with entry zone and stop-loss

TECHNICAL (Citadel)

9. Trend on daily and weekly timeframes
10. Key support/resistance levels - use pivot_levels.s1/s2 (support) and pivot_levels.r1/r2 (resistance) from technicals data directly. These are classic floor pivots from the last closed bar. Do NOT invent levels.
11. RSI, MACD, Bollinger Bands - plain English
12. Volume trend - use volume_score (current vol / 20d avg). >1.5 = above-avg conviction, >2.0 = surge, <0.5 = low-conviction move. Buyer vs seller dominance
13. Chart pattern (if any)
14. Minervini stage + score - stage (1=basing, 2=uptrend, 3=distribution, 4=decline), minervini_score /7. Score ≥5 = institutional-quality setup
15. 52-week context - pct_from_52w_high and pct_from_52w_low from technicals data
16. Technical composite score - technical_score /1.0 (0.40×Minervini + 0.25×trend + 0.20×RSI position + 0.10×MACD + 0.05×volume). ≥0.65 = strong setup, 0.45-0.65 = mixed, <0.45 = weak
17. Ideal entry: use pivot_levels.s1 as initial support; stop-loss below pivot_levels.s2; profit target at pivot_levels.r1 (conservative) or r2 (extended)
18. Risk-to-reward ratio (entry→target / entry→stop). Minimum 2:1 to recommend
19. Confidence rating: Strong Buy / Buy / Neutral / Sell / Strong Sell

INVESTOR LENSES

Apply the 2 most relevant lenses for this stock type. Skip inapplicable ones - state why in one line.

Graham (Deep Value): P/E < 15? Debt/equity < 1? Positive net current assets? 3/3 pass = "Graham would buy at this price." 1-2/3 = note which criteria miss. 0/3 = "Too expensive for value mandate."

Buffett (Quality Moat + Owner Earnings + Management): Three-part read.

• Moat: rating wide/narrow (from fundamental section)? Profit margins stable or expanding over 3yr? ROIC proxy = profit_margin × revenue / market_cap-style qualitative read.
• Owner earnings: Buffett's real cash to owner ≈ operating cash flow - maintenance capex; proxy with FCF from get_dcf_valuation fcf_history (US only). State FCF yield = latest FCF / market_cap as %. If FCF runs persistently below net income, flag "accounting earnings overstate cash - lower quality." If FCF ≥ net income and growing: "earnings are real cash."
• Management quality (capital allocation): Is payout_ratio sustainable (<60% mature co, <80% REIT/utility)? Share count discipline - in get_sec_financials, EPS growing faster than net income = buybacks (good when cheap); EPS lagging net income = dilution (flag). Net insider buying from get_insider_sentiment = alignment. Rate: rational allocator / mixed / value-destroyer.
• Verdict: wide moat + owner earnings ≥ reported + rational allocator → "Buffett-quality compounder - hold forever at right price." Any leg fails → name the failing leg, downgrade to "Pass - [reason]."

Lynch (GARP): Compute PEG = pe_ratio / (eps_growth_yoy × 100). PEG < 1.0 = undervalued grower, 1.0-2.0 = fairly priced growth, > 2.0 = growth already priced in. State PEG explicitly. If eps_growth_yoy null, state "PEG unavailable."

Druckenmiller (Macro Momentum): Does get_macro_snapshot (rates/CPI/CAD-USD) support this sector's tailwind? Does the chart (stage 2 uptrend + volume confirmation) validate the macro thesis? Risk/reward ≥ 3:1 AND macro + chart aligned → "Druck would size up." Misalignment → "Wait for macro confirmation before entering."

Lens selection guide (use as default, override with judgment):

• Dividend / value stock → Graham + Buffett
• Large-cap compounder → Buffett + Lynch
• Growth / tech → Lynch + Druckenmiller
• Macro-sensitive (energy, banks, rates, commodities) → Druckenmiller + Graham

CROWDING (Goldman / BlackRock 2025 - AI consensus risk)

19. Crowding score /100 + label (consensus / neutral / contrarian) from get_positioning_signals
20. Edge-already-priced flag - if consensus_flag=True, downgrade confidence by 1 notch and state "AI/retail consensus already long; late entry has negative expected alpha"
21. Contrarian opportunity flag - if contrarian_flag=True AND fundamentals + technicals strong, upgrade confidence by 1 notch
22. Headline velocity ratio - >2.0 = retail attention surge, late-cycle; <0.5 = forgotten name, potential setup

After output - log decision

Call mcp__aifolimizer__log_recommendation with action (BUY/HOLD/SELL/ADD/TRIM), conviction (HIGH/MED/LOW), target_pct + stop_pct (percent from entry - entry captured live at call time), 1-line rationale, skill="stock-analysis". Feeds forward win-rate / track-record loop.

Rules

• Under 600 words
• Cite user's actual cost basis from portfolio data to frame recommendation
• For Canadian tickers (.TO suffix), use TSX context

Gotchas

• get_fundamentals cached 6h - analyst_target can be stale within trading day; flag if last update >24h.
• get_technicals cached 1h - entry zones/stop-loss stale on high-volatility days. Mention timestamp.
• Never invent price target - only quote analyst_target from MCP or derive from explicit valuation math you show.
• minervini_score requires all 7 sub-criteria - if any field null, score invalid; state "incomplete data".
• pct_from_52w_high/low from technicals - use directly, do NOT recompute from price guess.
• For .TO tickers, yfinance fundamentals sparse - institutional ownership and analyst recs often empty. Note "TSX coverage gap" rather than fabricating.
• crowding_score uses 4 weighted signals; if 3+ inputs null (common for small caps/TSX), label unreliable - state "positioning data sparse".
• Crowding ≠ overvalued. Consensus name can still grind higher on earnings beats. Flag adjusts conviction, doesn't invert call.
• Headline velocity counts yfinance news only - misses Reddit/X chatter. Underestimates retail surge.
• pivot_levels null for symbols with <2 trading days of data (new listings, halted). State "pivot data unavailable" rather than guessing.
• volume_score null when volume data missing (common for some TSX ETFs). Do not comment on volume conviction in that case.
• get_insider_sentiment / get_recent_filings are US-only (Finnhub/EDGAR) - for .TO tickers they return no_api_key/no_cik; state "US-only data unavailable for TSX name", don't fabricate.
• get_finnhub_news sentiment is a crude keyword tally, not NLP - use as a tie-breaker, not a primary signal. get_factor_exposure low R² (<0.2) = factor model doesn't fit; skip the lens-selection use.
• technical_score weights are fixed (40/25/20/10/5). Treat as screening signal, not a precise model output.
• Owner-earnings FCF yield + share-count read are US-only (get_dcf_valuation / get_sec_financials via EDGAR). For .TO names these return empty - fall back to payout_ratio + the cash-flow narrative in fundamental item 2; state "FCF/share-count detail unavailable for TSX name", do NOT fabricate FCF.
• get_dcf_valuation returns a note when latest FCF is negative - in that case skip the FCF-yield claim and say "owner earnings negative this period; cyclical or reinvestment-heavy - check capex."