Fewshot Attempt 02

Skill: Asteria Investment Office Portfolio Risk Solver

Purpose

Solve task_group_010 institutional portfolio risk tasks that require querying a shared Asteria Investment Office environment, reading local request payloads, and producing a structured JSON answer that conforms to a provided template.

Environment Rules

• Use only the remote environment base URL declared in environment_access.md (e.g., GDPEVO_ENV_BASE_URL).
• Do not start local env servers, run env/setup.sh, or read files under env/.
• Do not return narrative commentary outside the JSON object unless the prompt explicitly asks for it.

Step 1: Identify the Task Type and Portfolio

From the prompt, extract:

• Portfolio ID (e.g., PF-EN-ALTA, PF-INT-NEXVEN, PF-FI-LUMEN, PF-MA-HELIO).
• Task category: credit trade, correlation review, allocation view refresh, fixed-income rebalance, or multi-asset committee memo.

Step 2: Read Local Payloads and Answer Template

List and read every JSON file in input/payloads/:

• answer_template.json — defines the exact output schema, field names, nesting, and rounding rules.
• Request-specific payloads (e.g., committee_request.json, review_request.json, desk_request.json) — provide constraints such as review windows, index universes, trade sizes, target opportunity sets, or CIO memos.

Step 3: Query the Shared Environment

Use curl or an HTTP tool against the remote base URL. Start with GET /api/catalog to discover valid IDs.

Common endpoints

Step 4: Compute and Derive Values

Credit Trade Strategy

1. Compute current portfolio metrics from the portfolio’s holdings: total market value, HY allocation %, weighted modified duration, and weighted YTM.
2. Filter the bond universe using criteria from the prompt/payload:
   • energy_linked == true (if required)
   • candidate == true (if "eligible" is specified)
   • Issuer watchlist == false
   • Rating bucket and duration band aligned with the portfolio’s active policy.
3. Select the required number of bonds and notional amounts (respect even splits if the prompt specifies them).
4. Compute post-trade metrics:
   • hy_allocation_pct = (current HY market value) / (new total market value) × 100
   • weighted_modified_duration_years = Σ(quantity × duration) / new total
   • weighted_yield_to_maturity_pct = Σ(quantity × YTM) / new total
   • Issuer concentration for each selected issuer = (existing quantity + new quantity) / new total
   • Subsector count (must be ≥ subsector_min_count_for_diversified from the active policy).
5. Choose a client_pitch_theme that aligns with the selected bonds and has a positive market signal from /api/market/energy.

Correlation Review

1. Fetch monthly index levels for each index in the requested universe over the review window.
2. Compute simple monthly returns: (level_t − level_{t−1}) / level_{t−1}.
3. Compute the sample Pearson correlation for every pair. Round to the precision declared in the template (typically three decimals). Pair identifiers should be listed in alphabetical order.
4. Identify:
   • Highest concentration pair — the pair with the maximum correlation.
   • Best diversifier pair — the pair with the minimum correlation (ideally negative).
5. Map findings to sleeve actions per the template instructions (e.g., trim, add, hold, reduce).

Allocation View Refresh

1. For each requested opportunity set, fetch the macro signal record for the correct quarter from /api/macro-signals.
2. Map signal_score to the active view using the policy’s view_score_thresholds:
   • score > OW_min → OW
   • score < UW_max → UW
   • otherwise → N
3. Map the absolute score to conviction using the policy’s conviction_thresholds:
   • |score| ≥ HIGH_abs_min → HIGH
   • |score| ≥ MEDIUM_abs_min → MEDIUM
   • otherwise → LOW
4. Determine change by comparing the new view to the prior_view from /api/allocation/prior-views:
   • Use the implicit rank: OW (1) > N (0) > UW (−1).
   • UP if rank increases, DOWN if rank decreases, UNCHANGED if the same.
5. Carry over the rationale_code from the macro signal record.

Fixed-Income Rebalance

1. Calculate current HY % and weighted duration from the portfolio’s holdings.
2. Identify watchlist and HY holdings to sell.
3. Select IG, non-watchlist buys that keep:
   • post_trade_hy_allocation_pct ≤ max_hy_allocation_pct
   • post_trade_duration_years inside duration_band_years
   • post_trade_watchlist_exposure_usd_m == 0 (if the policy requires clearing watchlist exposure).
4. Ensure the rotation meets the policy’s target_hy_reduction_pct (interpret as percentage points or relative reduction per the template).
5. Populate exception flags (hy_cap_pass, duration_band_pass, watchlist_exposure_cleared, etc.) based on post-trade metrics.

Multi-Asset Committee Memo

1. Run the correlation analysis on the portfolio’s non-US equity holdings (or the index universe specified in the payload).
2. Run the allocation view refresh for the requested opportunity sets.
3. Set rebalance_trigger if the highest concentration correlation exceeds correlation_high_threshold from the policy (e.g., correlation_cap_breach).
4. Set portfolio_risk_concentration_flag accordingly.
5. Derive target_sleeve_actions from the allocation views:
   • UW → trim
   • OW → add
   • N → hedge or hold per the template instructions.
6. Choose next_step based on exceptions and triggers (e.g., approve_with_monitoring when triggers exist but are manageable).

Step 5: Populate the Answer Template

• Transfer every required field from answer_template.json.
• Use exact key names, array structures, and nesting.
• Apply rounding rules precisely (e.g., correlations to three decimals, percentages to two decimals, durations to two or three decimals as specified).
• Return only the JSON object. Do not wrap it in markdown code fences or add explanatory text.

Step 6: Validate

• Verify the JSON is well-formed (python3 -m json.tool).
• Cross-check every numeric bound against the active policy from /api/policies.
• Confirm all template fields are present and populated.
• Ensure no extra keys or commentary are included.