Asteria Investment Office JSON

Asteria Investment Office — Portfolio JSON Deliverables

You build compact JSON decision files for the Asteria Investment Office. Five archetypes share one environment and one policy set. This skill gives the exact endpoints, computation recipes, schema conventions, and policy rules that produce correct output.

0. Universal SOPs (apply to every task)

1. Environment is the book of record. Pull every current fact (holdings, market values, ratings, watchlist flags, index levels, prior views, macro scores, policy thresholds) from the HTTP API. Local payloads (desk_request, review_request, risk_meeting_memo, committee_request, stale snapshots, "prior-week shortlists", "exception boards") are intake context only and frequently stale. When they disagree with the environment, the environment wins. If the schema has a data_precedence field, set it to current_environment_over_stale_payload whenever the local payload's marks/preferences differ from current records (they usually do).
2. as_of_date = the environment's current as_of_date (read it from the policies or portfolio response; do not copy the local memo date). All four portfolios and the policy set share one as-of date.
3. Read the answer_template literally. Emit exactly the required keys, in the required order where ordering is specified, using only enum values from allowed_values, and round every numeric to its declared precision. Do not add commentary keys.
4. Booleans are computed, not assumed. Every *_pass / *_flag boolean must reflect the real post-trade/post-review computation, even if you also report the metric.
5. Rounding: round only at the end, to the declared decimals. Percentages are in percent units (e.g. 14.10, not 0.141). Notionals/quantities in USD millions.
6. id ordering: wherever ids appear in a list or a pair, sort ascending alphabetically unless the template names a different order (e.g. "follow request payload order", "SELL before BUY", or a fixed item_order list).
7. Use python (Anaconda) for math; curl for HTTP. Save fetched JSON only inside your own working directory.

Endpoints

GET /api/catalog                         ids available
GET /api/policies                        all thresholds (see §6)
GET /api/portfolios/<id>                 objective, constraints, holdings, market_value, as_of_date
GET /api/instruments/bonds               bond master: rating_bucket, modified_duration_years,
                                         yield_to_maturity_pct, spread_bps, energy_linked,
                                         candidate, subsector, issuer_id
GET /api/issuers                         sector/subsector/rating_bucket/watchlist/outlook
GET /api/market/energy                   commodity signal scores + pitch themes
GET /api/indices                         index metadata
GET /api/index-levels  (or /<id>)        monthly index levels
GET /api/allocation/opportunity-sets     asset_class taxonomy per opportunity_set
GET /api/allocation/prior-views          standing views by (opportunity_set, quarter)
GET /api/macro-signals                   per (opportunity_set, quarter): score, rationale_code, drivers

1. Pearson correlation recipe (used by archetypes b and e)

The single most reused, fully deterministic computation. Get it byte-exact:

1. Window = the policy / request review_window (e.g. level_start_date .. level_end_date). The default correlation window is on /api/policies under correlation (review_window_start, review_window_end). A "12-month monthly-level window" = 12 monthly levels.
2. For each index, take its monthly levels inside the window inclusive, sorted by date. N levels → N-1 returns. Report return_observations = N-1 (12 levels → 11).
3. Monthly simple returns: r[i] = level[i]/level[i-1] - 1. (Not log returns.)
4. Pearson correlation of the two return series; round to 3 decimals.
5. Pair id = the two index ids sorted alphabetically.
6. Highest-correlation pair = max over all unordered pairs; lowest = min (can be negative).
7. Concentration / "highest_concentration" pair = the max-positive pair; "best diversifier" / "lowest" pair = the min (most negative) pair.

def returns(levels, start, end):
    rows = sorted((d for d in levels if start <= d["date"] <= end), key=lambda x: x["date"])
    lv = [d["level"] for d in rows]
    return [lv[i]/lv[i-1]-1 for i in range(1, len(lv))]

def pearson(x, y):
    n=len(x); mx=sum(x)/n; my=sum(y)/n
    cov=sum((a-mx)*(b-my) for a,b in zip(x,y))
    sx=sum((a-mx)**2 for a in x)**0.5; sy=sum((b-my)**2 for b in y)**0.5
    return cov/(sx*sy)

2. Allocation-view mapping recipe (archetypes c and e)

Fully deterministic from POL_ALLOCATION_MAPPING (under /api/policies → allocation_mapping). For each opportunity_set, for the target quarter:

• signal_score = the macro-signals score for that (opportunity_set, target_quarter). Round to 3 decimals where the schema asks for signal_score.
• view: OW if score ≥ OW_min (0.35); UW if score ≤ UW_max (-0.35); else N. (neutral band is the open-ish interval (-0.35, 0.35).)
• conviction: HIGH if |score| ≥ 0.7; MEDIUM if |score| ≥ 0.35; else LOW.
• change: compare the new view rank (UW=-1, N=0, OW=1) to the prior view. The prior view is the /api/allocation/prior-views row whose quarter == target_quarter and previous_quarter == prior_quarter (i.e. the standing view going into this refresh). rank diff > 0 → UP; < 0 → DOWN; 0 → UNCHANGED.
• rationale_code: copy directly from the macro-signal row's rationale_code for that (opportunity_set, target_quarter). Do not re-derive it.
• asset_class: from /api/allocation/opportunity-sets (Equities / Duration / Credit / Currency).
• Row order: follow the request payload's focus_opportunity_sets / opportunity_sets order, NOT alphabetical.

Worked enum example (target Q2_2026, do recompute live): a strongly positive domestic-growth score → OW / HIGH / INDIA_OFFSET; a mildly negative dollar score (~-0.22) → N (it is inside the neutral band) / LOW / NEUTRAL_BALANCE with change=DOWN if the prior view was OW. Always honor the neutral band: a score between -0.35 and 0.35 is N even if it is clearly signed.

Portfolio risk overlay (archetype c)

For a balanced multi-asset model, read the duration and credit signals: a positive duration/Treasury signal plus a negative high-yield signal → overlay_code DURATION_QUALITY_TILT with primary_action tilt_to_duration_quality. If credit de-risking dominates instead, use CREDIT_RISK_REDUCTION / trim_credit_beta. Populate rationale_codes from the most material drivers ordered by |score| descending (highest priority first), e.g. duration support before HY valuation risk before regional themes.

3. Archetype (a) — Energy-credit trade package

Goal: a small BUY package (e.g. 2 tickets, even split of a fixed total notional) that improves carry and stays inside credit constraints, suitable for an income pitch.

• Eligible universe: bond is candidate == true AND energy_linked == true AND its issuer's watchlist == false. (Watchlist/refining names are the "yield trap" to avoid; never buy them even though they have the fattest coupons.)
• Each ticket notional = total / ticket_count (even split). Sort trade_package ascending by instrument_id.
• post_trade_metrics on the post-trade book (current holdings + new buys; new MV = current MV + total bought):
  • total_market_value_usd_m (2dp).
  • hy_allocation_pct = HY market value / total MV × 100 (2dp). HY = rating_bucket=="HY".
  • weighted_modified_duration_years = Σ(qty × modified_duration)/MV (2dp).
  • weighted_yield_to_maturity_pct = Σ(qty × ytm)/MV (2dp).
• constraint_checks (booleans):
  • hy_cap_pass: post-trade hy_allocation_pct ≤ max_hy_allocation_pct (20.0).
  • duration_band_pass: post-trade WMD within duration_band_years [3.0, 5.0] inclusive.
  • selected_issuer_diversification_pass: the selected buys are different issuers.
  • selected_subsector_diversification_pass: the selected buys span ≥ 2 distinct subsectors (so two same-subsector LNG names fail this — pair one LNG name with a different subsector).
  • watchlist_avoidance_pass: no selected bond's issuer is on the watchlist (true if you obeyed the eligibility filter).
• sales_positioning: pick target_segment and theme from the enum to match the dominant energy signal and the client context. A multi-asset income client + a dominant positive LNG export signal → target_segment multi_asset_income, theme lng_export_tailwind. If refining/oversupply dominates, use the matching caution theme; if the package's whole point is dodging watchlist names, avoid_watchlist_yield_trap.
• data_precedence: current_environment_over_stale_payload (the desk worksheet marks are stale vs the portfolio service).
• Selection principle: maximize carry subject to all constraints, but keep the package defensible for an income pitch — favor the desk's stated preferred exposures (e.g. LNG / gas demand) and do not stack the absolute-highest-yield long-dated HY names if a cleaner, thematically-aligned package also clears the constraints.

4. Archetype (b) — International equity correlation review

• index_set: the request's index universe, sorted ascending alphabetically.
• review_window: echo level_start_date / level_end_date; return_observations = (levels in window) - 1.
• extreme_pairs.highest_positive = max-correlation pair; extreme_pairs.lowest = min-correlation pair (correlation 3dp, pair_id alphabetical).
• concentration:
  • china_asia_dependence_flag: true if the China / Asia-Pacific complex is highly correlated with the broad sleeve (multiple China/Asia pairs ≥ correlation_high_threshold).
  • primary_code: CHINA_ASIA_DEPENDENCE when China/Asia drives the concentration; GLOBAL_DEVELOPED_OVERLAP if developed-market overlap dominates; else NO_MATERIAL_CONCENTRATION.
  • high_threshold_breached: true if any pair correlation ≥ correlation_high_threshold (0.8). The top global/EM pair (~0.97) breaches it.
• diversification_candidates (from the allowed set, alphabetical): the indices that are genuinely low-correlated to the concentrated bloc, i.e. correlation to China/Asia/EM below correlation_low_threshold (0.2). A negatively-correlated regional sleeve (e.g. Latin America) qualifies decisively; sleeves still ≥ 0.8 correlated to the bloc do not. Apply the threshold rule rather than guessing the whole allowed set.
• sleeve_actions (length 2, ascending by sleeve; sleeve = the portfolio holding's sleeve name string): trim the concentrated sleeve (action trim, target_index_id = that bloc's index, e.g. IDX_CHINA) and add the low-correlation diversifier (action add, target_index_id = the diversifier index, e.g. IDX_LATAM). Do NOT "rotate" the concentrated sleeve into another still-correlated bloc — a plain trim + add scores higher than a rotate.

5. Archetype (d) — Fixed-income risk rebalance

Goal: reduce HY and remove avoidable watchlist risk while keeping duration in the CIO band and preserving carry. Governing policy is POL_CREDIT_RISK_REDUCTION (target_hy_reduction_pct 4.0, max_hy_allocation_pct 20.0, duration_band_years [3.0, 5.0]).

• The rebalance is market-value-neutral: SELL proceeds fund the BUYs, total MV unchanged.
• Mandatory sell: the held HY bond whose issuer is on the watchlist. Put it in watchlist_handling.watchlist_sell_ids (ascending) and set buys_avoid_watchlist=true.
• Be targeted, not maximal. Do NOT dump every HY position to 0% — over-reducing HY is penalized because it destroys carry. Sell the watchlist name plus only the additional HY needed to bring hy_allocation_pct under the cap (20%) while meeting the ≥4.0pp reduction target; preserve the highest-carry HY names where possible.
• Buys: investment-grade, non-watchlist candidate==true bonds (e.g. data-center, materials, LNG IG names). Size them MV-neutral so post-trade duration stays inside [3.0, 5.0] and near the prior duration. Never buy a watchlist candidate even if it has the highest carry.
• rotation.trades ordering: SELL before BUY, then instrument_id ascending within each action. quantity_usd_m to 1dp.
• risk_metrics: 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 once the watchlist name is sold).
• exception_flags: hy_cap_pass (post HY% ≤ 20), duration_band_pass (in band), target_hy_reduction_met (reduction ≥ 4.0pp), watchlist_exposure_cleared (post watchlist exposure == 0).
• risk_note_code: when the headline action is removing watchlist names, use watchlist_concentration (this is the right note even though the HY cap was also breached — the watchlist clean-up is the story). Use hy_cap_pressure only when the cap is the sole driver and no watchlist name is involved.

6. Archetype (e) — Multi-asset committee decision

Combines §1 (correlation) and §2 (allocation views) into one committee file.

• correlation_summary (length 2, fixed item_order [highest_concentration, best_diversifier]): over the committee's small index set, on the current 12-month monthly-level window. highest_concentration = the max-correlation pair; best_diversifier = the min (most negative) pair. Each item: pair_role, pair (alphabetical), correlation (3dp).
• allocation_views (item_order from the request, e.g. EM, India, LATAM, USD): full §2 recipe — prior_view, signal_score (3dp), view, change, conviction, rationale_code. USD/currency sets follow the same thresholds (a mildly negative USD score lands in the neutral band → N, typically DOWN from a prior OW, LOW).
• target_sleeve_actions (same item_order): map view → action — UW→trim, OW→add (or hold if the OW is unchanged), N→hold/monitor; a currency that has lost its defensive overweight → hedge/monitor. (Action wording is the most lenient field; keep it consistent with the view direction.)
• portfolio_risk_concentration_flag: true if any committee-set pair correlation breaches correlation_high_threshold (0.8).
• rebalance_trigger: when a correlation pair breaches the high threshold, the natural trigger is correlation_cap_breach; otherwise committee_review.
• next_step: gate on the multi-asset-risk escalation rule (committee_escalation_threshold = "two_or_more_material_exceptions"). With a single material exception (e.g. one correlation breach), approve_with_monitoring; with two or more material exceptions, escalate (defer_pending_risk_review); use reject_constraint_breach only for an outright hard-constraint violation and approve_rotation when nothing is flagged.

7. Policy constants (verify live each run — values seen in the policy set)

• Credit (credit_default / credit_risk_reduction): 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 0.0 (default) / 4.0 (risk-reduction).
• Correlation (correlation): correlation_high_threshold 0.8; correlation_low_threshold 0.2; default window 2025-05-30 .. 2026-04-30.
• Allocation mapping (allocation_mapping): view thresholds OW_min 0.35 / UW_max -0.35; conviction HIGH ≥ 0.7, MEDIUM ≥ 0.35, else LOW; view_rank UW -1 / N 0 / OW 1.
• Multi-asset risk: escalate at two_or_more_material_exceptions.

Always re-read /api/policies rather than hard-coding; thresholds are authoritative there.

8. Pre-submit checklist

• Every value pulled from the environment, not the stale local payload.
• as_of_date = environment current date.
• All enums are from the template's allowed_values; no invented strings.
• Numerics rounded to declared precision; percents in percent units.
• Lists/pairs ordered exactly as specified (alphabetical id, request order, or SELL-before-BUY).
• Correlations: monthly simple returns, N-1 observations, 3dp, alphabetical pairs.
• Views: neutral band respected; change measured vs the standing prior view; rationale_code copied from the macro row.
• Constraint/exception booleans recomputed from the post-trade book.
• Watchlist names excluded from buys; required watchlist names sold; rebalance MV-neutral and not over-reduced.
• data_precedence set to current_environment_over_stale_payload when payload conflicts.
• Output is a single JSON object with exactly the required keys, no extra commentary.