asteria-investment-office-json

name

asteria-investment-office-json

description

Repeatable workflow for answering Asteria Investment Office (PF-*) portfolio tasks against the shared HTTP environment (<remote-env-url>). Covers five archetypes: (a) energy-credit trade-package selection, (b) international equity correlation review, (c) active allocation view refresh, (d) fixed-income risk rebalance, (e) multi-asset committee decision. Tells you which endpoints to call, the exact math, the policy thresholds, and the output schema conventions (field names, enum sets, rounding, id ordering). Use whenever the prompt names an Asteria portfolio id, an allocation/correlation/credit memo, or asks for JSON matching an answer_template.

Asteria Investment Office — task workflow

The Asteria environment is a read-only HTTP API and the current book of record. Local payloads in input/payloads/ are intake context only and may be stale. When local disagrees with the environment, the environment always wins.

Base URL: <remote-env-url>. Use curl for HTTP and python (NOT python3) for math. Save fetched JSON only inside your own working dir.

Golden rules (apply to every task)

1. Environment is the source of truth. Re-fetch every number from the API. Treat market values, holdings, quantities, ratings, durations, yields, watchlist flags, signal scores, prior views, and index levels as authoritative even if the local payload quotes a different "stale" value. The as_of_date in your answer = the environment as_of_date (e.g. from /api/policies or the portfolio record), NOT any date in the local memo.
2. Read the answer_template like a contract. Honor exactly: required keys, the required_value constants (portfolio_id, task_id, quarter, etc.), enum allowed_values (use the literal strings/casing shown — note some enums are lowercase like trim/add/hold, others UPPER like UW/OW/BUY), numeric precision (round to that many decimals), list length/required_length, and the ordering rule for every list.
3. Ordering conventions seen: sort trade lists ascending by instrument_id; for SELL/BUY lists "SELL before BUY, then instrument_id ascending"; index pair_id / pair = the two ids in alphabetical order; index_set / candidate lists = alphabetical; allocation rows = the payload's focus_opportunity_sets order (or the template's explicit item_order).
4. Output only the JSON object (no prose) when the prompt says so.
5. Never call any scoring/judge endpoint. Only the data endpoints below exist.
6. Bootstrap every task by fetching /api/catalog (valid ids) and /api/policies (all thresholds). Then fetch the archetype-specific endpoints.

Policy reference (GET /api/policies)

Read these live each run; values below are the structure to expect.

• allocation_mapping:
  • view_score_thresholds: OW_min (e.g. 0.35), UW_max (e.g. -0.35), neutral band between. Map a signal score → view: OW if score >= OW_min; UW if score <= UW_max; else N.
  • conviction_thresholds: HIGH_abs_min (e.g. 0.7), MEDIUM_abs_min (e.g. 0.35), LOW_abs_below (e.g. 0.35). Map on abs(score): HIGH if |s|>=HIGH_abs_min; MEDIUM if |s|>=MEDIUM_abs_min; else LOW.
  • view_rank: {UW:-1, N:0, OW:1} → change = compare new vs prior rank: higher=UP, lower=DOWN, equal=UNCHANGED.
• correlation: correlation_high_threshold (e.g. 0.8), correlation_low_threshold (e.g. 0.2), and the official review window (review_window_start/_end).
• credit_default / credit_risk_reduction: duration_band_years (e.g. [3.0,5.0]), max_hy_allocation_pct (e.g. 20.0), issuer_concentration_limit_pct (e.g. 12.0), subsector_min_count_for_diversified (e.g. 2), target_hy_reduction_pct (0.0 for default, 4.0 for risk-reduction). A portfolio's own constraints.policy_id tells you which credit policy applies.
• multi_asset_risk.committee_escalation_threshold: two_or_more_material_exceptions (drives committee next_step).

---

Archetype (a) — Energy-credit trade-package selection

Example prompt: "Prepare the proposed energy-credit trade strategy / BUY package for PF-EN-ALTA." Template keys: portfolio_id, as_of_date, trade_package (list of {action, instrument_id, notional_usd_m}), post_trade_metrics, constraint_checks, sales_positioning, data_precedence.

Endpoints: /api/portfolios/<id> (holdings, market_value, constraints), /api/instruments/bonds (universe), /api/issuers (watchlist/outlook), /api/market/energy (signals + pitch themes), /api/policies (credit policy).

Candidate eligibility filter (intersect all):

• candidate == true (only buy candidates, not already-held core lines),
• energy_linked == true (for the energy sleeve),
• issuer watchlist == false (watchlist avoidance — exclude DRIFTWOOD, JUNIPER, PACIFIC_REFIN, etc.; their bonds are deliberate yield-trap distractors),
• the resulting post-trade portfolio must stay inside the credit constraints.

Sizing: honor the request (e.g. "two BUY tickets totaling 8.0m split evenly" = two 4.0m BUYs). notional_usd_m precision per template (often 1 dp).

Post-trade metric recipe (notional-weighted over held + bought positions):

• total_market_value_usd_m = current MV + total new notional (precision 2).
• hy_allocation_pct = (sum HY notional / total) * 100 (rating_bucket=="HY").
• weighted_modified_duration_years = Σ(md_i * q_i)/Σq_i over all positions.
• weighted_yield_to_maturity_pct = Σ(ytm_i * q_i)/Σq_i. "Improve carry" means post-trade weighted YTM > pre-trade weighted YTM.

constraint_checks (booleans):

• hy_cap_pass: post-trade HY% <= max_hy_allocation_pct.
• duration_band_pass: post-trade weighted MD within duration_band_years.
• selected_issuer_diversification_pass: the two selected buys are from two distinct issuers (this is a check on the SELECTED names, not the whole book — the existing portfolio may already exceed the 12% issuer-concentration guideline, which is unavoidable and not what this flag measures).
• selected_subsector_diversification_pass: the selected buys span

  = subsector_min_count_for_diversified distinct subsectors.

• watchlist_avoidance_pass: neither selected name's issuer is watchlisted.

Selection judgment: among eligible pairs that pass all constraints and improve carry, prefer the package that matches the desk preference and the strongest current market signal. The energy signal scores (/api/market/energy) rank themes — e.g. LNG export pull is typically the strongest positive, refining is negative ("avoid refining watchlist"). For a client-facing income pitch / committee that is "sensitive to headline carry from watchlist issuers," do NOT max raw yield by loading HY to just under the cap; favor IG LNG/gas-demand carry (e.g. BlueGas/Eastern-LNG family) plus a diversifying second subsector.

Enums: action ∈ {BUY,SELL,HOLD,NO_TRADE}; sales_positioning.target_segment typically multi_asset_income (or private_bank_income) for an income pitch; theme = lng_export_tailwind when LNG is the lead signal (or midstream_stability / avoid_watchlist_yield_trap as the data dictates); data_precedence = current_environment_over_stale_payload whenever the live record differs from the stale snapshot (it usually does — e.g. MV/HY/duration move vs the worksheet).

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Archetype (b) — International equity correlation review

Example prompt: "international equity correlation review for PF-INT-NEXVEN." Template keys: portfolio_id, review_window, index_set, extreme_pairs (highest_positive / lowest), concentration, diversification_candidates, sleeve_actions.

Endpoints: /api/portfolios/<id>, /api/indices (metadata, window dates), /api/index-levels or /api/index-levels/<id> (monthly levels), /api/policies (correlation thresholds). Use the index universe + window from the request payload (cross-check against the policy review window — they match, e.g. 2025-05-30 → 2026-04-30).

Correlation recipe (do this in python):

1. For each index, take monthly levels within [level_start_date, level_end_date], sorted by date. A 12-level window → 11 monthly returns.
2. Convert to simple returns: r_t = level_t/level_{t-1} - 1.
3. return_observations = number of returns = (#levels - 1) = 11 (report the return count, not the level count).
4. Pearson correlation between each pair's return series: r = Σ(a-ā)(b-b̄) / sqrt(Σ(a-ā)² · Σ(b-b̄)²). Round to 3 decimals.
5. Over all C(n,2) pairs: highest_positive = max correlation pair, lowest = min (most negative) correlation pair.
6. pair_id = the two index ids sorted alphabetically, length 2.

concentration block:

• china_asia_dependence_flag (bool): true when China / EM / Asia-Pacific indices are highly correlated (China–EM pair near/above the high threshold).
• high_threshold_breached (bool): the highest pairwise correlation

  = correlation_high_threshold (0.8). With a global benchmark in the set the top pair (e.g. EM–WORLD ~0.97) breaches.

• primary_code enum: CHINA_ASIA_DEPENDENCE when the China/Asia cluster drives concentration; else GLOBAL_DEVELOPED_OVERLAP or NO_MATERIAL_CONCENTRATION.

diversification_candidates: from the template's allowed set ({IDX_EM_EX_CHINA, IDX_INDIA, IDX_LATAM}), the indices that are the lowest- correlated diversifiers vs the concentrated cluster (LatAm and India typically show negative correlation to China). Alphabetical order.

sleeve_actions: two rows, ascending by sleeve. action ∈ {trim,add,hold,hedge,monitor,rotate}; target_index_id ∈ {IDX_CHINA, IDX_EM_EX_CHINA, IDX_LATAM}. Logic: trim/hedge the concentrated China/EM exposure, add the diversifier (e.g. LatAm / EM-ex-China).

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Archetype (c) — Active allocation view refresh

Example prompt: "Q2 active allocation view refresh." Template keys: task_id, as_of_date, target_quarter, prior_quarter, policy_id, allocation_views (one row per focus opportunity set), risk_overlay.

Endpoints: /api/allocation/opportunity-sets (asset_class mapping + display_order), /api/allocation/prior-views, /api/macro-signals, /api/policies.

Per-row recipe for each requested opportunity_set, at the target quarter:

• score = /api/macro-signals row for (opportunity_set, target_quarter).
• rationale_code = that row's rationale_code (use it verbatim; it is already one of the template enum values, e.g. INDIA_OFFSET, CHINA_DEPENDENCE, LATAM_DIVERSIFIER, DURATION_SUPPORT, HY_VALUATION_RISK, EUROPE_RECOVERY, JAPAN_POLICY_RISK, NEUTRAL_BALANCE, etc.).
• view = map score via view_score_thresholds (OW/N/UW).
• conviction = map |score| via conviction_thresholds (HIGH/MEDIUM/LOW).
• change = compare new view rank vs the prior view. The prior view is the /api/allocation/prior-views row whose quarter == target_quarter (i.e. the standing/published view for that quarter, whose previous_quarter == prior_quarter). Compute UP/DOWN/UNCHANGED from view_rank.
• asset_class = from /api/allocation/opportunity-sets (Equities/Duration/ Credit/Currency).
• policy_id (lineage) = allocation_mapping.policy_id (or the policy-set id as the template implies); as_of_date = environment as_of_date; target_quarter/prior_quarter echo the request.
• Row ordering = the payload's focus_opportunity_sets order.

risk_overlay: pick the overlay_code + primary_action that summarize the dominant tilt across the rows, and list contributing rationale_codes in business-priority order. Heuristics from the data:

• Strong duration-quality signals (Treasuries/Bunds OW, HY UW) → DURATION_QUALITY_TILT / tilt_to_duration_quality, leading rationale DURATION_SUPPORT (+ RATE_CUT_SUPPORT, HY_VALUATION_RISK).
• Dominant HY/credit-spread risk → CREDIT_RISK_REDUCTION / trim_credit_beta (CREDIT_SPREAD_RISK / HY_VALUATION_RISK first).
• Broad equity OW → EQUITY_BETA_EXTENSION / add_cyclical_equity_beta.
• USD/defensive currency tilt → CURRENCY_DEFENSIVE_HEDGE / add_currency_hedge.
• Otherwise NO_OVERLAY / hold_policy_weights.

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Archetype (d) — Fixed-income risk rebalance

Example prompt: "fixed-income risk rebalance / rotation for PF-FI-LUMEN — reduce HY and watchlist pressure, keep duration in range." Template keys: task_id, portfolio_id, as_of_date, rotation.trades, risk_metrics, exception_flags, watchlist_handling, risk_note_code.

Endpoints: /api/portfolios/<id>, /api/instruments/bonds, /api/issuers, /api/policies (this portfolio uses POL_CREDIT_RISK_REDUCTION, so target_hy_reduction_pct = 4.0).

Setup: compute current weighted MD, current HY% (HY notional / MV * 100), current watchlist exposure (Σ notional of held bonds whose issuer watchlist == true). Identify held watchlist names (sell candidates) and held HY.

Build a MV-neutral rotation (Σ sells == Σ buys, so total MV is unchanged):

• SELL the held watchlist name(s) in full (clear watchlist) plus enough other held HY to satisfy the HY cap. Caution: selling only the single watchlist line often leaves HY still above the 20% cap — you usually need a second HY sell (e.g. another chemicals/consumer HY line) so hy_cap_pass is true.
• BUY only eligible candidates: candidate == true AND issuer watchlist == false (avoid_new_watchlist_buy). A watchlisted candidate (e.g. JUNIPER_2030) is a distractor — never buy it even though it has high carry. Prefer IG names that keep weighted MD inside the band and span

  = 2 subsectors. Split buys to fund the sells (e.g. two equal IG buys).

risk_metrics (recompute post-trade): post_trade_hy_allocation_pct (2dp), post_trade_duration_years (2dp), hy_reduction_pct_points = current_HY% - post_HY% (2dp), post_trade_watchlist_exposure_usd_m (1dp, = 0 after clearing).

exception_flags (booleans): hy_cap_pass (post HY% <= cap), duration_band_pass (post MD in band), target_hy_reduction_met (hy_reduction >= target_hy_reduction_pct), watchlist_exposure_cleared (post watchlist exposure == 0).

watchlist_handling: watchlist_sell_ids = sold watchlist instrument_ids, ascending; buys_avoid_watchlist = true (always, by construction).

trades ordering: SELL rows before BUY rows, instrument_id ascending within each action. quantity_usd_m precision 1. action ∈ {BUY,SELL}.

risk_note_code enum: choose the dominant theme — hy_cap_pressure (HY over cap was the binding issue), watchlist_concentration, duration_preservation, carry_tradeoff, or no_action.

---

Archetype (e) — Multi-asset committee decision

Example prompt: "committee JSON for PF-MA-HELIO linking correlation findings to allocation views." Template keys: portfolio_id, as_of_date, review_quarter, correlation_summary, target_sleeve_actions, allocation_views, rebalance_trigger, portfolio_risk_concentration_flag, next_step.

This archetype composes (b) + (c) for a small focused set, then makes a governance decision.

Endpoints: all of (b) and (c): /api/portfolios/<id>, /api/index-levels, /api/policies, /api/allocation/prior-views, /api/macro-signals.

correlation_summary: length 2 in fixed order [highest_concentration, best_diversifier]. Compute Pearson correlations (monthly simple returns, 3dp) over the requested 4-index window; the highest positive pair = highest_concentration, the most negative pair = best_diversifier. Each pair is alphabetically sorted, length 2. (For an EM/China/India/LatAm set: highest ≈ China–EM; lowest ≈ China–LatAm.)

allocation_views: for each requested opportunity set, output prior_view, signal_score (3dp, from macro-signals), view, change, conviction, rationale_code — same mapping recipe as archetype (c), at the review quarter. Watch the stale-note trap: e.g. a desk note may keep USD overweight, but if the current USD score sits in the neutral band it maps to N (change DOWN). The environment wins.

target_sleeve_actions: one action per opportunity set (same item_order), action ∈ {trim,add,hold,hedge,monitor,rotate}. Tie to the views/correlation: trim/hedge the concentrated leg, add the diversifier (e.g. add LatAm, hold/trim EM, hold India OW, trim/hedge USD).

Governance fields:

• portfolio_risk_concentration_flag (bool): true when the top equity pair correlation >= correlation_high_threshold (0.8) — i.e. a real concentration exception exists.
• rebalance_trigger enum: correlation_cap_breach when the high-correlation threshold is breached; else hy_cap_pressure/duration_drift/ watchlist_concentration/committee_review as the dominant cause.
• next_step enum: under multi_asset_risk escalation (two_or_more_material_exceptions), if there are >=2 material exceptions (e.g. correlation breach + a view downgrade) lean to defer_pending_risk_review; a single, manageable exception → approve_with_monitoring; a clean book → approve_rotation; a hard constraint breach you cannot cure → reject_constraint_breach. Decide from the computed exceptions, not the stale note.

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Common misjudgments to avoid

• Using stale payload numbers (MV, HY%, duration, quantities, USD-OW) instead of re-fetching — the worksheet is intentionally out of date.
• Counting levels instead of returns for return_observations (12 levels → 11 returns).
• Using log returns — the spec is simple returns.
• Not alphabetizing the two ids inside a pair, or not sorting the list itself.
• Buying a watchlisted candidate for its high carry (distractor), or buying an already-held core line instead of a candidate==true name.
• Treating selected_issuer_diversification_pass as a whole-portfolio concentration test — it is about the two selected buys.
• Clearing the watchlist name but forgetting that HY% can still exceed the cap, so a second HY sell is required.
• Maxing raw carry to just under the HY cap when the brief is a conservative, client/committee income pitch.
• Wrong enum casing (BUY vs buy, trim vs TRIM, OW vs ow) or wrong rounding precision — copy them verbatim from the template.
• Echoing a date from the local memo as as_of_date instead of the environment as_of_date.