Asteria Investment Office JSON

Asteria Investment Office — committee JSON solver

0. Golden rules (apply to every archetype)

1. The HTTP environment is the book of record. Local payloads (desk_request, review_request, memo, committee_request, "stale" snapshots, exception boards, prior-week shortlists) are intake context only. Whenever a local number disagrees with the environment (holding quantities, marks, HY %, duration, currency views, index levels), USE THE ENVIRONMENT VALUE. A common trap: a memo lists a stale holding quantity (e.g. 10.0) while the live portfolio shows 12.0 — compute with 12.0. When the schema has a data_precedence field, the value is current_environment_over_stale_payload in essentially every refresh/rebalance case.
2. as_of_date = the environment's current as-of date (the as_of_date returned by /api/policies, /api/portfolios/<id>, /api/market/energy, etc.), NOT any date written in the local payload.
3. Follow the answer_template exactly: required keys, enum value sets (verbatim casing), rounding precision, list lengths, and ordering rules. Echo required_value fields literally (portfolio_id, task_id, quarters).
4. Rounding: round only at the end, to the precision the template declares per field. Trailing-zero forms are fine (5.8 == 5.80). Compute with full precision; never round intermediate weighted sums.
5. Ordering: respect each field's stated order — "ascending by instrument_id", "alphabetical by index id", "SELL before BUY then id ascending", or "in the request payload's focus order / item_order". Within a correlation pair, sort the two ids alphabetically.
6. Use python (not python3) and curl. Save fetched JSON locally and compute deterministically; do not eyeball.

1. Endpoints

Base: GET on the Asteria HTTP service.

• /api/catalog — id inventory (portfolios, policies, indices, issuers, bonds, opportunity sets).
• /api/policies — all thresholds (see §2). Also gives the current as_of_date.
• /api/portfolios/<id> — objective, constraints (incl. policy_id), holdings (instrument_id, quantity_usd_m, sleeve), market_value_usd_m.
• /api/instruments/bonds — bond master: rating_bucket (IG/HY), modified_duration_years, yield_to_maturity_pct, coupon_pct, spread_bps, sector, subsector, energy_linked, candidate (eligible for purchase), issuer_id, recommended_theme_tags.
• /api/issuers — watchlist (bool), credit_outlook, rating_bucket, sector, subsector, research_tags.
• /api/market/energy — commodity signals with score/direction (oil, gas, LNG, refining, renewables) + pitch_themes.
• /api/indices and /api/index-levels[/<id>] — monthly index level series.
• /api/allocation/opportunity-sets — opportunity_set → asset_class (Equities/Duration/Credit/Currency) + display_order.
• /api/allocation/prior-views — prior active views per opportunity_set & quarter (view, conviction, previous_quarter).
• /api/macro-signals — per opportunity_set & quarter: score (signed) and rationale_code and drivers.

Most list endpoints accept equality filters (?rating_bucket=HY, ?candidate=true, ?quarter=Q2_2026).

2. Policy thresholds (from /api/policies — read them, don't hardcode blindly)

• Allocation mapping (POL_ALLOCATION_MAPPING):
  • View from signal score: OW if score ≥ OW_min (0.35); UW if score ≤ UW_max (-0.35); else N. Boundaries are inclusive of OW/UW.
  • Conviction from |score|: HIGH if |score| ≥ 0.70; LOW if |score| < 0.35; else MEDIUM.
  • view_rank: UW=-1, N=0, OW=+1 (used to derive change).
• Correlation (POL_CORRELATION_DEFAULT): correlation_high_threshold = 0.8, correlation_low_threshold = 0.2. Review window dates are given by the policy/request (e.g. 2025-05-30 → 2026-04-30).
• Credit default (POL_CREDIT_DEFAULT): duration_band_years [3.0, 5.0]; max_hy_allocation_pct 20.0; issuer_concentration_limit_pct 12.0; subsector_min_count_for_diversified 2; target_hy_reduction_pct 0.0.
• Credit risk reduction (POL_CREDIT_RISK_REDUCTION): same band/cap but target_hy_reduction_pct = 4.0.
• Multi-asset sets compose the above; POL_MULTI_ASSET_RISK escalates to committee on "two_or_more_material_exceptions".

Always take the policy_id from the portfolio's own constraints.policy_id and use that policy's numbers.

3. Core computation recipes

3a. Pearson correlation (archetypes b, e)

1. For each index pull its monthly level series for the window; sort by date.
2. Compute simple returns between consecutive levels: r_t = level_t/level_{t-1} − 1. A 12-month window of levels yields 11 returns → return_observations = 11.
3. Pearson r over the paired return vectors. Round to 3 decimals.
4. highest_positive / highest_concentration = pair with the max correlation. lowest / best_diversifier = pair with the minimum (most negative) correlation.
5. Each pair_id/pair is the two index ids sorted alphabetically.

3b. Notional-weighted portfolio metrics (archetypes a, d)

Build the post-trade position list (current holdings ± trades), then weight by quantity_usd_m:

• total_market_value_usd_m = Σ quantity (BUY-only packages add to MV; sell-and-fund rebalances keep MV constant when sells == buys).
• weighted_modified_duration_years = Σ(dur·qty)/Σqty.
• weighted_yield_to_maturity_pct = Σ(ytm·qty)/Σqty.
• hy_allocation_pct = (Σ qty where rating_bucket==HY) / total · 100, on post-trade MV.
• hy_reduction_pct_points = pre-trade HY% − post-trade HY%.
• watchlist exposure = Σ qty of held instruments whose issuer.watchlist is true. Note HY% and HY-reduction depend only on which HY notional is added/removed — they are invariant to which IG bond you buy; only duration/YTM depend on buy identity.

3c. Signal → view mapping (archetypes c, e)

For each requested opportunity_set, take the current macro score for the target quarter, map to view/conviction via §2, set rationale_code to the macro signal's own rationale_code, set asset_class from the opportunity-set taxonomy, and set change = sign of (new view_rank − prior view_rank) → UP/DOWN/UNCHANGED. The prior view comes from /api/allocation/prior-views (the row whose quarter == target quarter; its recorded view is the standing prior). Always recompute the view from the live score — do not copy the recorded prior view as the new view.

4. Archetype playbooks

(a) Energy-credit trade package (e.g. PF-EN-ALTA)

Goal: N BUY tickets of equal notional that raise carry while staying inside credit constraints and suitable for an income pitch.

Eligibility filter for a candidate bond (ALL must hold):

• candidate == true AND energy_linked == true
• issuer watchlist == false
• not already a current holding (new sleeve money)
• single-bond modified_duration_years within the duration band [3.0, 5.0] — this per-bond duration gate is decisive. A long-dated bond (e.g. dur 5.9) is ineligible even though the portfolio average might still land in band.

Selection among eligible bonds:

• Anchor on the strongest energy signal (typically LNG/gas — check /api/market/energy; LNG export pull is usually the top score). Pick the IG LNG/gas name (theme tags like LNG_EXPORTS / GAS_DEMAND).
• Pair it with the highest-YTM diversifier that has a different issuer AND different subsector, to maximize carry while passing diversification.
• Verify post-trade: HY% ≤ cap, weighted duration in band, both selected_issuer_diversification and selected_subsector_diversification true, watchlist_avoidance true. Set those constraint booleans honestly.

Schema conventions: trade_package length 2, sorted ascending by instrument_id, each {action:"BUY", instrument_id, notional_usd_m (1 dp)}. post_trade_metrics (MV 2dp, hy% 2dp, dur 2dp, ytm 2dp). sales_positioning: target_segment from the client context (e.g. multi_asset_income), theme from the dominant signal (e.g. lng_export_tailwind). data_precedence = current_environment_over_stale_payload.

(b) International equity correlation review (e.g. PF-INT-NEXVEN)

1. Compute the full correlation matrix over the requested index universe (§3a).
2. extreme_pairs.highest_positive and .lowest per §3a; return_observations from the window (11 for a 12-level year).
3. index_set = the universe sorted alphabetically.
4. concentration:
   • high_threshold_breached = any pair correlation ≥ 0.8 (true when the cluster is tightly correlated).
   • china_asia_dependence_flag = true when China/Asia/EM sleeves are mutually ≥ 0.8 (a dedicated-China + Asia-beta overlap exists).
   • primary_code = the dominant concentration label. Choose CHINA_ASIA_DEPENDENCE when the China/Asia block drives it, else GLOBAL_DEVELOPED_OVERLAP when the top pair is a broad developed/global overlap (e.g. EM↔World), else NO_MATERIAL_CONCENTRATION. (Keep china_asia_dependence_flag and primary_code logically consistent with which pair is actually the maximum.)
5. diversification_candidates = the low-correlation diversifier sleeve(s) from the allowed set (the index whose correlations are negative / well below the 0.2 low threshold is the clearest — a LatAm-type sleeve; include an India-type only if it is a genuine relative diversifier, exclude broad EM-ex-China which stays ≥0.8).
6. sleeve_actions (ordered ascending by sleeve): trim the concentrated sleeve (the dedicated China/over-weighted one) and add the low-correlation diversifier. Prefer decisive trim/add over monitor/hold for a flagged concentration.

(c) Active allocation refresh (balanced multi-asset, e.g. Q2 focus list)

For each focus opportunity_set, emit a row {opportunity_set, asset_class, view, change, conviction, rationale_code} using §3c. Order rows by the request payload's focus order. Lineage: policy_id = POL_ALLOCATION_MAPPING; quarters echoed from the request; as_of_date = env as-of.

risk_overlay:

• Pick overlay_code/primary_action from the dominant cross-asset tilt implied by the views: duration support + IG quality with HY underweight → DURATION_QUALITY_TILT / tilt_to_duration_quality; a credit-led de-risk → CREDIT_RISK_REDUCTION / trim_credit_beta; strong cyclical equity → EQUITY_BETA_EXTENSION; defensive FX → CURRENCY_DEFENSIVE_HEDGE; else NO_OVERLAY.
• rationale_codes = the supporting macro rationale codes in business-priority order (duration/quality support first, then the credit-risk code), highest priority first. (The allocation rows are the heavily weighted, deterministic part — get the score-mapping, conviction boundaries, and change exactly right.)

(d) Fixed-income risk rebalance (sell-and-fund, e.g. PF-FI-LUMEN)

Use POL_CREDIT_RISK_REDUCTION (target HY reduction 4.0pp, cap 20%, band [3,5]). Recipe:

1. Identify held HY (rating_bucket==HY) and held watchlist names (issuer.watchlist).
2. Sells: always sell the held watchlist name(s) to clear watchlist exposure to 0. Add the minimum additional HY needed to bring post-trade HY% under the 20% cap (and to meet the ≥4pp reduction). When choosing which extra HY to sell, prefer the lowest-carry (lowest YTM) HY name so carry is preserved — do not dump the highest-yielding HY.
3. Buys: fund with non-watchlist IG candidate bonds named in the desk shortlist, keeping total buys == total sells so MV/duration are preserved and no duration shortfall is created. Exclude any shortlist name whose issuer is on the watchlist (a tempting high-carry watchlist candidate must be dropped). Favor the cross-desk LNG IG name and a sector diversifier over a pure long-duration ballast when both are offered.
4. risk_metrics per §3b. exception_flags: hy_cap_pass (≤20%), duration_band_pass (in [3,5]), target_hy_reduction_met (≥4pp), watchlist_exposure_cleared (==0) — all true for a clean rotation.
5. watchlist_handling.watchlist_sell_ids = held watchlist ids you sold (ascending). buys_avoid_watchlist = true. risk_note_code = the dominant pressure (hy_cap_pressure when starting HY% far exceeds the cap).
6. rotation.trades ordered SELL before BUY, then instrument_id ascending within each action; quantities 1 dp.

(e) Multi-asset committee decision (e.g. PF-MA-HELIO)

Combines (b) + (c) on a small index/opportunity subset.

1. correlation_summary (length 2, item order [highest_concentration, best_diversifier]): highest_concentration = max-correlation pair; best_diversifier = min (most negative) pair; pairs alphabetical, corr 3dp.
2. allocation_views per §3c, including prior_view (from prior-views) and signal_score (the macro score, 3dp). Refresh stale currency views: a stale note keeping USD overweight is superseded — recompute from the live score (often lands at N), with change DOWN from the prior OW.
3. target_sleeve_actions (item order matches the request): map view direction to action — UW→trim, OW→add (high-conviction OW may hold), and for a currency sleeve being de-risked use hedge rather than hold.
4. portfolio_risk_concentration_flag = true when a pair ≥ the 0.8 high threshold (e.g. China↔EM concentration).
5. rebalance_trigger = committee_review for a committee decision file (prefer this over the more specific correlation_cap_breach in committee framing). next_step = approve_with_monitoring when a concentration flag is true but the rotation is actionable (escalate/defer only on ≥2 material exceptions).

5. Pre-submission checklist

• Pulled live env data; reconciled every stale local number to it.
• as_of_date and policy_id taken from the environment, not the payload.
• All enum values match the template's allowed sets verbatim.
• Correlations: 3dp, alphabetical pairs, correct obs count, max/min picked correctly.
• Weighted metrics weighted by notional; HY%, duration, YTM rounded per template.
• Constraint/exception booleans computed from the actual post-trade book.
• Eligibility gates applied (per-bond duration band; candidate; non-watchlist; not already held).
• Lists in the exact required order and length.
• Required_value fields echoed literally; no extra keys, no narrative outside JSON.