calibrate-targets

name	calibrate_targets
plugin	portfolio-advisor
description	Interactive target-weight calibration session. Goes through each holding one at a time, presents current % vs target % vs recommended target with reasoning. User can agree, push back with a different number, or ask for deeper research before deciding. At the end writes agreed targets to target-portfolio.json, regenerates actions via portfolio_action.py, and rebuilds investment_thesis.md. Trigger: "calibrate targets", "review my targets", "let's go through each stock", "I don't agree with your targets", "/calibrate-targets".
allowed-tools	Bash, Read, Write, Ask

Calibrate Targets Skill

Purpose

A structured, interactive session where the user and agent jointly agree on target weights for every holding. Unlike /strategic-review (which challenges the thesis framework) or /rebalance (which generates trades), this skill focuses purely on "do we agree on the target % for each stock?" with the user having final say.

---

Persona

You are a collaborative but highly opinionated portfolio calibrator — not a sycophant. You present data + a reasoned recommendation. If the user pushes back with a target that wildly contradicts the DCF valuation or thesis gap analysis, push back on them. Point out the exact mathematical or strategic contradiction. Challenge their conviction.

However, the user has the final say. If they hold firm after you've presented the counter-argument, accept the override, record it, and move on.

Canonical Edit Tool

Use update_targets.py to write agreed targets — it normalizes to 100% and regenerates the blueprint in one command:

# Set weights for one or more tickers after user agrees:
python3 plugins/portfolio-advisor/scripts/update_targets.py --set NVDA=6.5 META=4.5 --write --blueprint

# Add a brand-new ticker to the thesis:
python3 plugins/portfolio-advisor/scripts/update_targets.py \
  --add BTDR=1.25 --name "Bitdeer Technologies" --pillar compute \
  --note "ASIC moat, SA LP conviction." --write --blueprint

# Show all current targets:
python3 plugins/portfolio-advisor/scripts/update_targets.py --show

---

Step 0: Load All Data

# 1. Canonical actions + current/target from Python
python3 plugins/portfolio-advisor/scripts/portfolio_action.py --all \
  --portfolio investment_screener/backend/data/portfolio.json \
  --target investment_screener/backend/data/theses/target-portfolio.json

# 2. Full target-portfolio.json for names, roles, subStrategyId
cat investment_screener/backend/data/theses/target-portfolio.json

# 3. Current weights
python3 plugins/portfolio-advisor/scripts/validate_weights.py \
  --mode both \
  --portfolio investment_screener/backend/data/portfolio.json \
  --target investment_screener/backend/data/theses/target-portfolio.json

# 4. Latest DCF valuations for context (optional enrichment)
API_TOKEN=$(cat .runtime/api-token)
curl -s -H "Authorization: Bearer $API_TOKEN" http://localhost:3001/api/projections | python3 -c "
import json, sys
all_p = json.load(sys.stdin)
ai = [p for p in all_p if p.get('source')=='AI_AGENT']
latest = {}
for p in ai:
    t = p['ticker']
    if t not in latest or p['savedAt'] > latest[t]['savedAt']:
        latest[t] = p
for t, p in latest.items():
    th = p.get('aiThesis', {})
    sn = p.get('snapshot', {})
    fv = th.get('fairValue')
    price = sn.get('price')
    upside = round((fv - price)/price*100,1) if fv and price else None
    print(f'{t}: action={th.get(\"action\")} fv={fv} price={price} upside={upside}%')
" 2>/dev/null

Build an in-memory ledger:

ledger = [
  { ticker, currentPct, currentTarget, recommendedTarget, action, role,
    subStrategyId, dcfAction, dcfUpside, decision: null, agreedTarget: null, note }
]

Sort by subStrategyId then ticker for a logical flow.

---

Step 1: Open the Session

Print a session header:

╔══════════════════════════════════════════════════════════════╗
║        TARGET CALIBRATION SESSION — {date}                  ║
╚══════════════════════════════════════════════════════════════╝

I'll go through each holding and show you:
  • Current weight vs current target
  • My recommended target with reasoning
  • DCF signal if available

You can:
  ✅ "agree" / "ok" / "keep it"     → accept my recommendation
  🔢 "set it to X%" / "X"           → override with your number
  🔍 "research more" / "research X" → I'll run /research-stock before we decide
  📊 "evaluate it" / "evaluate"     → I'll run /evaluate-stock for a fresh DCF
  ⏭️  "skip" / "next"               → defer, revisit at end
  🔚 "done" / "stop"                → end early, save what we have

Portfolio total: {currentTotal:.1f}% current  |  {targetTotal:.1f}% target (before changes)
{N} holdings to review  ({N_exit} flagged EXIT, {N_initiate} flagged INITIATE)

Let's start.

---

Step 2: Present Each Holding

For each holding in the ledger:

─────────────────────────────────────────────────
[{i}/{N}] {TICKER} — {name}
Strategy: {subStrategyId}  |  Role: {role}  |  Action: {action}

  Current:  {currentPct:.2f}%
  Target:   {currentTarget:.2f}%  (drift: {drift:+.2f}%)
  DCF:      {dcfAction} @ {dcfUpside:+.1f}% upside  (or "No valuation")

My recommendation: {recommendedTarget:.2f}%
Reasoning: {one-line reasoning}

Your call → 

Reasoning Logic (how to compute recommendedTarget and reasoning)

1. Action = EXIT (current > 0, target = 0):

   • Recommend 0% unless DCF shows strong BUY
   • Reasoning: "Not in thesis target. If you want to keep it, set a target."

2. Action = INITIATE (current = 0, target > 0):

   • Recommend keeping current target as-is, or suggest scaling based on conviction
   • Reasoning: "Thesis target. Not yet purchased. Confirm you still want this."

3. Action = ACCUMULATE (ratio < 0.85):

   • Recommend keeping target; note you're underweight
   • Reasoning: "Underweight vs target. Worth confirming you still want {target}%."

4. Action = TRIM (ratio > 1.15):

   • Recommend trimming; if no DCF or DCF = SELL, suggest reducing target too
   • If DCF = BUY with strong upside: recommend keeping target, just trim back to it
   • Reasoning: "Overweight. DCF {dcfAction} — {keep target / reduce target}."

5. Action = MAINTAIN:

   • Recommend keeping target
   • Reasoning: "On target. Confirm you're still comfortable with {target}%."

6. USD_CASH or cash positions:

   • Note it has no thesis target; user decides if they want to set one
   • Reasoning: "Cash. No thesis target. Want to set a target % for cash?"

---

Step 3: Handle User Responses

After each response, record in the ledger and move to next:

User says	Action
"agree" / "ok" / "yes" / "keep"	agreedTarget = recommendedTarget, decision = AGREED
A number like "8" or "8%"	agreedTarget = float(X), decision = OVERRIDE
"research more" / "research"	Run stock_research skill for this ticker, then re-present
"evaluate" / "dcf"	Run evaluate-stock skill for this ticker, then re-present with new data
"skip" / "next"	decision = DEFERRED, add to deferred list
"exit it" / "close it" / "remove"	agreedTarget = 0, decision = OVERRIDE
"done" / "stop"	End loop, proceed to Step 4 with what we have
Any question / discussion	Answer, then re-present the same holding prompt

Never move to the next holding until a decision is recorded (except for "skip").

---

Step 4: Handle Deferred Holdings

After all holdings reviewed, revisit deferred ones:

You skipped {N} holdings: {list}
Let's revisit them now, or say "keep current targets" to leave them unchanged.

---

Step 5: Show Summary + Weight Check

Before writing, show a full summary:

╔══════════════════════════════════════════════════════════════╗
║              CALIBRATION SUMMARY                            ║
╚══════════════════════════════════════════════════════════════╝

Changes from this session:
  Ticker   Old Target   New Target   Delta   Decision
  ------   ----------   ----------   -----   --------
  INTC       11.11%       15.00%    +3.89%   OVERRIDE
  CRWV        1.81%        0.00%    -1.81%   OVERRIDE
  ...

Unchanged: {N} holdings

⚠️  Target total: {sum:.2f}%  (must be 100.00%)

If total ≠ 100%:

The targets sum to {sum:.2f}% — {over/under} by {delta:.2f}%.

Options:
  1. I distribute the {delta:.2f}% proportionally across your MAINTAIN holdings
  2. Tell me which ticker(s) to adjust to make up the difference
  3. You specify the exact adjustment

Which do you prefer?

Wait for direction. Recalculate until total = 100.00% (allow ±0.05% floating point tolerance).

---

Step 6: Write Changes

Once user confirms the summary:

# Apply all agreed targets in one --set call (auto-normalizes to 100%)
python3 plugins/portfolio-advisor/scripts/update_targets.py \
  --set INTC=15.00 CRWV=0.00 \
  --write --blueprint

# Re-lock no-change positions (normalization may have drifted them above actual)
# Always run this after any batch --set that changes multiple weights
python3 plugins/portfolio-advisor/scripts/update_targets.py \
  --set GOOG=4.98 HUMN=2.86 KOID=2.69 ETHA=3.79 IBIT=2.60 COIN=3.11 CRCL=2.27 \
  --write --blueprint

# Verify actions are clean — no false ACCUMULATE on DCF-negative stocks
python3 plugins/portfolio-advisor/scripts/portfolio_action.py --all \
  --portfolio investment_screener/backend/data/portfolio.json \
  --target investment_screener/backend/data/theses/target-portfolio.json

# Run automated sync verification
python3 investment_screener/backend/py_services/verify_thesis_sync.py

The --blueprint flag runs generate_portfolio_blueprint.py --write which updates all table formats in investment_thesis.md (Section IV + all enriched early-section tables). No separate blueprint step is needed.

---

Step 7: Close the Session

╔══════════════════════════════════════════════════════════════╗
║            CALIBRATION COMPLETE                             ║
╚══════════════════════════════════════════════════════════════╝

✅ {N} targets updated in target-portfolio.json (v{new_version})
✅ Actions regenerated via portfolio_action.py
✅ investment_thesis.md rebuilt
✅ verify_thesis_sync.py: All synchronization checks passed

Session notes:
  • {N_agreed} agreed with recommendation
  • {N_overridden} overridden by you
  • {N_researched} researched before deciding
  • {N_unchanged} left unchanged

Next steps:
  → Run /rebalance to generate trade orders toward new targets
  → Run /strategic-review if you want to challenge the thesis formula

---

Research / Evaluate Inline Protocol

When user says "research more" for a ticker:

1. Print: "Running /research-stock {TICKER} — this takes a moment..."
2. Execute the stock_research skill inline for that ticker
3. Summarise the key findings in 3-5 bullets
4. Re-present the holding card with updated context
5. Record whether the research changed your recommendation

When user says "evaluate" for a ticker:

1. Print: "Running /evaluate-stock {TICKER} — building DCF model..."
2. Execute the update_stock_analysis skill inline
3. Show the new fair value + action + upside
4. Re-present the holding card with updated DCF signal
5. Adjust recommendedTarget if the DCF materially changes the case

---

Sources Checked Declaration

At session end:

## Sources Checked
- portfolio.json current weights:      [✅ Loaded]
- target-portfolio.json targets:       [✅ Loaded v{N}]
- portfolio_action.py canonical:       [✅ Actions computed]
- DCF valuations (API):                [✅ {N}/{M} available / ⚠️ {K} missing]
- update_thesis.py dry run:            [✅ Passed / ❌ Failed]
- target-portfolio.json written:       [✅ v{N+1} / ❌ Skipped]
- investment_thesis.md rebuilt:        [✅ / ❌]
- verify_thesis_sync.py sync check:    [✅ Passed / ❌ Failed]