Fewshot Attempt 02

Asteria Investment Office — Operational Skill

Core principle: Environment is the book of record

The shared Asteria API is the current, authoritative source. Local payloads (desk requests, meeting memos, committee packets) are intake context only — they may contain stale marks, outdated worksheets, or pre-reconciled snapshots. When a local payload conflicts with the API, the API wins.

Data-precedence value: "current_environment_over_stale_payload" — use this whenever the task template includes a data_precedence field and the local payload carries snapshot data that the API supersedes.

API inventory (all GET, no auth)

Universal conventions

Dates

• The environment as_of_date lives in /api/policies[].as_of_date and in every portfolio response. Use it as the output as_of_date; do not carry forward a date from a local payload.
• Index level windows are given as level_start_date / level_end_date in the request. The number of monthly observations = count of levels − 1.

Numeric precision (follow the answer template exactly)

Sorting

• Instrument IDs in lists: ascending alphabetical (e.g. "BND_BLUEGAS_2030" < "BND_RIVER_2029").
• Index IDs in pair arrays: ascending alphabetical (e.g. ["IDX_CHINA","IDX_EM"]).
• Trade tickets: by action group (SELL before BUY), then ascending instrument_id within each group.
• Allocation/opportunity-set rows: preserve the order given in the request payload's focus list.
• Sleeve-action rows: follow the template's prescribed item order (usually the request's opportunity-set list order).

Watchlist

• Check /api/issuers → watchlist boolean. Bonds whose issuer is watchlisted carry WATCHLIST_RISK in their recommended_theme_tags.
• A buy that goes to a watchlisted issuer fails watchlist_avoidance_pass / buys_avoid_watchlist.
• Watchlist sell IDs are the instrument_ids of sold holdings whose issuer is on the watchlist.

Task-type workflows

A. Credit desk / bond selection (e.g. PF-EN-ALTA, PF-FI-LUMEN)

Step 1 — Load current state

• GET /api/portfolios/<id> → holdings, current market_value_usd_m, constraints policy_id.
• GET /api/policies → extract the relevant credit policy (POL_CREDIT_DEFAULT or POL_CREDIT_RISK_REDUCTION). Key thresholds:
  • max_hy_allocation_pct: 20.0
  • duration_band_years: [3.0, 5.0]
  • issuer_concentration_limit_pct: 12.0
  • subsector_min_count_for_diversified: 2
  • target_hy_reduction_pct (risk-reduction policy only): 4.0

Step 2 — Load bond & issuer universe

• GET /api/instruments/bonds → filter for candidate: true (unless an existing holding is a sell target, which may be candidate: false).
• GET /api/issuers → join on issuer_id for watchlist status.

Step 3 — Select trades

• For buys: pick candidate bonds that are energy-linked (if the portfolio is energy-focused), IG-rated, non-watchlist, with attractive carry (YTM) while keeping duration inside the band.
• For sells: target HY positions and watchlisted positions.
• Each trade has action, instrument_id, and quantity (USD millions, 1 decimal).
• Two BUY tickets usually means exactly two buy instruments, evenly split.

Step 4 — Compute post-trade metrics

post_market_value = pre_market_value + sum(buy quantities)   [sells change composition, not MV, when proceeds are reinvested]

For funded buys (new money): post_mv = pre_mv + sum(buys)
For rotation (sell to fund buys): post_mv = pre_mv (net zero), buys funded by sell proceeds

hy_pct = sum(quantities of HY-rated holdings) / post_market_value * 100

weighted_duration = sum(qty_i × duration_i) / sum(qty_i)     [over all post-trade holdings]

weighted_ytm = sum(qty_i × ytm_i) / sum(qty_i)               [over all post-trade holdings]

hy_reduction_pts = pre_trade_hy_pct - post_trade_hy_pct

Step 5 — Constraint checks (all boolean)

• hy_cap_pass: post_trade HY % ≤ max_hy_allocation_pct
• duration_band_pass: duration_band_years[0] ≤ weighted_duration ≤ duration_band_years[1]
• selected_issuer_diversification_pass: no single issuer's total post-trade quantity exceeds issuer_concentration_limit_pct of post_market_value
• selected_subsector_diversification_pass: at least subsector_min_count_for_diversified distinct subsectors among selected (bought) instruments
• watchlist_avoidance_pass: no buy goes to a watchlisted issuer
• target_hy_reduction_met: hy_reduction_pts ≥ target_hy_reduction_pct (risk-reduction policy)

Step 6 — Watchlist handling

• watchlist_sell_ids: list of sold instrument_ids whose issuer watchlist is true, sorted ascending.
• buys_avoid_watchlist: true if no buy-side instrument has a watchlisted issuer.

B. Equity correlation review (e.g. PF-INT-NEXVEN, PF-MA-HELIO correlation portion)

Step 1 — Fetch index levels

• GET /api/index-levels → filter to the requested index IDs and the date window [level_start_date, level_end_date] inclusive.

Step 2 — Compute monthly simple returns

For each index, for each consecutive pair of monthly levels:
  return_t = (level_t / level_{t-1}) - 1

return_observations = count of levels - 1   (e.g. 12 monthly levels → 11 return observations)

Step 3 — Compute Pearson correlation for every index pair

• Standard Pearson r on the paired monthly return series.
• Correlation values rounded to 3 decimal places.
• Pair identifiers: array of two index IDs, sorted alphabetically.

Step 4 — Identify extreme pairs

• highest_positive: the pair with the largest positive correlation (closest to +1.0).
• lowest: the pair with the most negative correlation (closest to −1.0). This is labeled lowest in the template, not "most negative".

Step 5 — Concentration analysis

• Thresholds from POL_CORRELATION_DEFAULT in /api/policies:
  • correlation_high_threshold: 0.8
  • correlation_low_threshold: 0.2
• If China↔Asia Pacific ex-Japan correlation exceeds the high threshold → china_asia_dependence_flag: true, primary_code: "CHINA_ASIA_DEPENDENCE", high_threshold_breached: true.
• Otherwise primary_code: "NO_MATERIAL_CONCENTRATION".

Step 6 — Diversification candidates

• From the subset ["IDX_EM_EX_CHINA", "IDX_INDIA", "IDX_LATAM"], include any index whose pairwise correlations against the concentration pair members are below the high threshold. Sorted alphabetically.

Step 7 — Sleeve actions

• Sleeves with high concentration → "trim".
• Diversifying sleeves with low/negative correlation → "add".
• Match sleeve names to index IDs using the portfolio's holding sleeve labels.
• Sort by sleeve name alphabetically (template says "ascending by sleeve").

C. Allocation view refresh (e.g. train_003, train_005 allocation portion)

Step 1 — Load taxonomy and signals

• GET /api/allocation/opportunity-sets → maps each opportunity_set to its asset_class.
• GET /api/macro-signals → filter to the target quarter (e.g. Q2_2026). Each entry has: opportunity_set, score, rationale_code.
• GET /api/allocation/prior-views → filter to rows where quarter = target quarter (these rows carry previous_quarter = prior quarter's view in view). Important: the prior-views endpoint returns rows keyed by quarter (the target quarter), with previous_quarter naming the comparison quarter and view giving that prior quarter's view. Each row's view field IS the prior view for the target quarter.

Step 2 — Determine current view from signal score Using policy thresholds from /api/policies → allocation_mapping.view_score_thresholds:

score ≥ OW_min (0.35)       → "OW"
score ≤ UW_max (-0.35)      → "UW"
-0.35 < score < 0.35        → "N"

Step 3 — Determine conviction from |score| Using allocation_mapping.conviction_thresholds:

|score| ≥ HIGH_abs_min (0.7)     → "HIGH"
|score| ≥ MEDIUM_abs_min (0.35)  → "MEDIUM"
|score| < LOW_abs_below (0.35)   → "LOW"

Step 4 — Determine change vs prior quarter Using allocation_mapping.view_rank: OW=1, N=0, UW=-1.

new_rank > prior_rank  → "UP"
new_rank < prior_rank  → "DOWN"
new_rank == prior_rank → "UNCHANGED"

Step 5 — Rationale code

• Use the rationale_code from the macro-signal entry for that opportunity_set and quarter. Do not invent or reassign.

Step 6 — Risk overlay

• Synthesize an overlay from the pattern of views. Common overlay choices map to:
  • DURATION_QUALITY_TILT / tilt_to_duration_quality: when OW duration, UW HY, concerns about credit/EM
  • CREDIT_RISK_REDUCTION / trim_credit_beta: when HY is UW with high conviction
  • CURRENCY_DEFENSIVE_HEDGE / add_currency_hedge: when USD signal is negative and EUR/JPY diverge
  • NO_OVERLAY / hold_policy_weights: when no strong tilt exists
• rationale_codes list: the rationale codes that support the overlay choice, ordered by business priority (most important first).

Step 7 — Policy ID

• From the portfolio's constraints or /api/policies top-level policy_id.

D. Multi-asset committee (combines B + C, e.g. train_005)

• Run the correlation review on the requested index subset (Step B) and the allocation refresh on the requested opportunity sets (Step C) within the same answer.
• target_sleeve_actions: map each opportunity_set to an action (trim/add/hold/hedge/monitor/rotate) informed by both correlation signals and allocation views. For currency sleeves (e.g. USD), hedge is valid.
• rebalance_trigger: pick from the enum — correlation_cap_breach when a correlation threshold is crossed; otherwise match the dominant risk theme.
• portfolio_risk_concentration_flag: true when a high-correlation pair involving a held sleeve breaches the threshold.
• next_step: approve_with_monitoring when all checks pass but a flag is raised; approve_rotation when clean; defer_pending_risk_review when uncertain.

Common pitfalls

1. Using stale local dates instead of the environment as_of_date. Always read /api/policies or the portfolio endpoint and use that date.
2. Computing returns from raw levels without de-synchronizing dates. All index level arrays share the same monthly date grid — verify that every pair uses the same set of observation dates.
3. Sorting pair IDs incorrectly. Always alphabetical within a pair: ["IDX_CHINA","IDX_EM"] not ["IDX_EM","IDX_CHINA"].
4. Confusing lowest with "lowest positive". lowest means the most negative (or least positive) correlation value — the minimum, not the smallest absolute value.
5. Mixing up prior vs current quarter in allocation views. The prior-views endpoint returns rows where quarter = target quarter and previous_quarter = comparison quarter; the view field in each row is the prior view (from previous_quarter), not the current one.
6. Not filtering bonds by candidate status. Holdings in a portfolio may have candidate: false — they can be sold but should not be bought.
7. Forgetting to join issuers for watchlist checks. A bond's own tags may hint at watchlist risk, but the definitive source is issuers[issuer_id].watchlist.
8. Duration band is inclusive. duration_band_years: [3.0, 5.0] means duration must be ≥ 3.0 AND ≤ 5.0.
9. Rounding before all calculations are complete. Compute with full precision, round only the final output values to the template's declared precision.
10. Using the wrong policy. Check the portfolio's constraints.policy_id — it may be POL_CREDIT_DEFAULT, POL_CREDIT_RISK_REDUCTION (adds a target HY reduction), POL_CORRELATION_DEFAULT, POL_MULTI_ASSET_DEFAULT, or POL_MULTI_ASSET_RISK.

Quick-reference: key thresholds

Task-agnostic output rules

• Always match the answer template's required keys exactly — no extra keys, no missing keys.
• Enum fields: use only the allowed_values from the template.
• List fields: match the declared length and ordering.
• task_id field (when present): use the exact value declared in the template's required_value.
• Return only the JSON object — no markdown fences, no narrative.