Fewshot Attempt 01

Asteria Investment Office — Operational Skill

1. Environment & Data Sources

Base URL: GDPEVO_ENV_BASE_URL (from environment_access.md). All data comes from the remote HTTP API at this URL. Never read local env source files or use localhost.

Key endpoints:

Workflow rule: Always start by fetching the relevant endpoints to establish the current book of record. Use requests (Python) or curl — prefer Python for any calculation-heavy work.

2. Data Precedence Rule

The live API always beats local payloads. Every task provides a local request payload (.json in input/payloads/) that may contain stale marks, outdated holdings, or old preference lists. The local payload tells you what to compute, but current API data tells you the values to use.

• Portfolio holdings → use GET /api/portfolios/<id>, not stale snapshots
• Bond attributes (YTM, duration, rating, watchlist) → use GET /api/instruments/bonds, not desk labels
• Issuer status → use GET /api/issuers
• Prior views → use GET /api/allocation/prior-views
• Signal scores → use GET /api/macro-signals
• Policy thresholds → use GET /api/policies

When the API has fresher/different data than a local payload, set "data_precedence": "current_environment_over_stale_payload" in the output.

3. Correlation Calculations (Pearson, Monthly Simple Returns)

Used in equity correlation reviews and multi-asset committee tasks.

Procedure:

1. Fetch monthly index levels for the review window from GET /api/index-levels. The window is defined by level_start_date and level_end_date.
2. Compute monthly simple returns: r_t = (level_t − level_{t−1}) / level_{t−1}
3. If you have N level observations, you get N−1 return observations.
4. For each pair of indices, compute the Pearson correlation coefficient over the overlapping return series.
5. Round to 3 decimal places.

Pearson formula:

r = Σ((x_i − x̄)(y_i − ȳ)) / sqrt(Σ(x_i − x̄)² × Σ(y_i − ȳ)²)

Best diversifier = the pair with the lowest (most negative) correlation. Highest concentration = the pair with the highest positive correlation.

4. Portfolio Metrics — How to Compute

Post-trade HY allocation (%)

HY_pct = (Σ MV of HY-rated holdings after trades) / (total portfolio MV after trades) × 100

Round to precision declared in the answer template (typically 2 decimal places).

Weighted modified duration (years)

Wtd_Dur = Σ (MV_i / total_MV) × mod_duration_i

Each bond's modified duration comes from GET /api/instruments/bonds. Round to 2 decimals.

Weighted yield to maturity (%)

Wtd_YTM = Σ (MV_i / total_MV) × YTM_i

Each bond's YTM comes from GET /api/instruments/bonds. Round to 2 decimals.

Total market value (USD millions)

Total_MV = Σ market_value_i   (after applying all trades)

Round to template precision.

HY reduction (percentage points)

HY_reduction = pre_trade_HY_pct − post_trade_HY_pct

Watchlist exposure

Sum the market values (post-trade) of any holdings whose issuer is on the watchlist (from GET /api/issuers). Target: zero.

5. Allocation Views — View, Change, Conviction

Signal score → View (UW / N / OW)

Derive from macro signal scores at GET /api/macro-signals:

The exact boundary may vary slightly; the principle is: strongly positive → OW, strongly negative → UW, near zero → N.

View → Conviction (LOW / MEDIUM / HIGH)

Based on the absolute value of the signal score:

Change vs prior quarter (UP / DOWN / UNCHANGED)

Compare the current view to the prior quarter's view from GET /api/allocation/prior-views:

Important nuance: The change field reflects directional momentum, not just a diff. If prior was N and current is OW, that's UP. If prior was OW and current is N, that's DOWN. If prior was N and current is UW, that's DOWN. UW → N is UP (less bearish).

Rationale codes

Choose from the enum allowed in the answer template. Match the rationale to the signal direction and macro context:

• Positive equity signals → GROWTH_IMPROVES, EUROPE_RECOVERY, INDIA_OFFSET, LATAM_DIVERSIFIER
• Negative equity signals → CHINA_DEPENDENCE, JAPAN_POLICY_RISK
• Duration positive → DURATION_SUPPORT, RATE_CUT_SUPPORT
• Credit negative → HY_VALUATION_RISK, CREDIT_SPREAD_RISK
• Currency → DOLLAR_DEFENSIVE, NEUTRAL_BALANCE

Risk overlay

Synthesize from the dominant themes across all allocation views:

• If OW duration + UW HY → DURATION_QUALITY_TILT
• If UW credit broadly → CREDIT_RISK_REDUCTION
• If broad equity OW → EQUITY_BETA_EXTENSION
• If defensive currency views → CURRENCY_DEFENSIVE_HEDGE
• If all neutral → NO_OVERLAY

The rationale_codes list should include the rationale codes that drove the overlay decision, ordered by business priority (highest priority first).

6. Trade Selection & Constraint Checks

Bond eligibility for BUY

• Bonds must be current (not matured) as of the as_of_date
• Issuer must not be on the watchlist (check GET /api/issuers)
• Bond rating comes from GET /api/instruments/bonds
• For energy-linked tasks, bonds must appear in GET /api/market/energy
• Prefer bonds with higher YTM (carry) subject to constraints
• Diversify across issuers and subsectors

Constraint checks (boolean flags)

• HY cap: Post-trade HY allocation % ≤ policy HY cap (from GET /api/policies)
• Duration band: Post-trade weighted modified duration within CIO min/max range (from policies)
• Issuer diversification: Selected BUY bonds must not share the same issuer
• Subsector diversification: Selected BUY bonds must be from different subsectors
• Watchlist avoidance: No BUY on any watchlisted issuer or bond

Trade sorting

• BUY-only packages: Sort ascending by instrument_id
• Mixed SELL/BUY rotations: SELL entries first, then BUY entries; within each action group, sort ascending by instrument_id

7. Output Formatting Conventions

Numeric precision — follow the template

Each answer template declares precision per field. Common defaults:

• notional_usd_m / quantity_usd_m: 1 decimal place
• Percentages (_pct, _pct_points): 2 decimal places
• Duration in years: 2 decimal places
• Pearson correlation: 3 decimal places
• Signal scores: 3 decimal places
• Total market value: 2 decimal places

String ordering

• Index IDs in pairs: Alphabetically ascending (e.g., ["IDX_CHINA", "IDX_LATAM"])
• Index sets: Alphabetically ascending
• Trade lists: By action group then instrument_id ascending
• Allocation views: In the order specified by the request payload's focus_opportunity_sets array
• Sleeve actions: By the order specified in the template's item_order
• Watchlist sell IDs: Ascending instrument_id order

Date format

All dates: YYYY-MM-DD string format. The as_of_date should be the date of the current environment data, not the request date.

Task ID

When a template requires task_id, use the value from the template's required_value field.

Return format

Return only the JSON object. No markdown fences, no commentary. The answer must validate against the answer template's schema.

8. Workflow Checklist

For any Asteria task, follow this sequence:

1. Read the local payload to understand the request (portfolio ID, window, focus sets, preferences).
2. Read the answer template to know the exact output shape, field names, and allowed enum values.
3. Fetch current data from the API — portfolio, bonds, issuers, policies, indices, signals, prior views, etc. — whatever the task needs.
4. Compute — correlations, portfolio metrics, views from signals, constraint checks.
5. Assemble output — fill every required field, order lists correctly, apply precision rules.
6. Validate — check that every enum value matches the template's allowed values, every boolean is true/false (not strings), and numeric precision matches the template.

9. Common Pitfalls

• Using stale local data instead of API data. Always refresh from the API. If the local payload says a bond has 10.0M and the portfolio API says 12.0M, use 12.0M and flag the precedence.
• Wrong correlation direction for diversifier. "Best diversifier" means LOWEST correlation, not highest negative absolute value. A correlation of −0.825 is a better diversifier than +0.100, but between −0.825 and +0.050, −0.825 is the best diversifier.
• Including a pair against itself in the correlation matrix. Exclude self-correlations (always 1.0) from the extreme-pair scan.
• Reversing change direction. When prior=N and current=UW, change is DOWN (became more bearish). When prior=UW and current=N, change is UP (became less bearish).
• Forgetting to recompute post-trade metrics — total MV must reflect trades: BUYs add to MV, SELLs subtract.
• Exceeding template enum values. Only use values listed in the answer template's allowed_values arrays — don't invent new ones.
• Wrong ordering of trade lists. SELL-before-BUY grouping only applies when the template explicitly says so; otherwise sort by instrument_id.
• Misidentifying IG vs HY. Check the bond's actual rating from the API, not the desk label in the local payload.
• Forgetting that notional/quantity must match trade constraints. If the desk says "exactly two BUY tickets totaling $8M split evenly," each must be exactly $4.0M.
• Correlation rounding. Always round to 3 decimal places for correlation values; 2 for most percentages and ratios.