Fewshot Attempt 03

Skill: Structured Financial Portfolio JSON Generation

Purpose

Generate strictly-schema-compliant JSON answers for portfolio analysis, risk review, and allocation committee tasks. Input always contains a prompt, data payloads, and an answer_template.json that serves as the schema contract.

Prerequisites

• Read /workspace/environment_access.md for any environment URL overrides.
• Read input/prompt.txt to understand the high-level request.
• Read all files under input/payloads/ — there may be multiple JSON data sources.
• Read input/payloads/answer_template.json — this is the schema contract, not a fill-in-the-blanks form.

Step-by-Step Procedure

1. Discovery — Load All Inputs

Read every file in input/payloads/ before performing any calculation. Data sources may include:

• Raw index level histories (index_levels)
• Correlation matrices (correlation_matrix)
• Risk metric tables (risk_metrics)
• Carry/momentum/volatility signals (carry, momentum, vol_z)
• Prior views and constraints (prior_views, constraints)
• Meeting memos or review requests

2. Schema Extraction — Parse answer_template.json

The answer_template.json defines the exact output contract. Extract the following for every required top-level key:

• type: string, number, list, boolean, enum
• required_value: if present, the exact literal value required (e.g., "PF-MA-HELIO", "Q2_2026")
• format: e.g., "YYYY-MM-DD"
• length: for lists, exact expected count
• item_order: for lists, the exact ordering of items by a discriminant field (e.g., opportunity_set or pair_role)
• allowed_values: for enums, the closed set of permitted strings
• precision: for numbers, decimal places (typically 3)
• calculation: human-readable formula or method (e.g., "Pearson correlation of monthly simple returns", "signal_score = (carry + mom + vol_z) / 3")
• ordering_rule: special sorting rules (e.g., "Index ids within each pair must be sorted alphabetically")

3. Calculation — Derive Each Field from Payload Data

Perform calculations exactly as specified in the template. Common patterns:

Correlation from Index Levels

• Compute monthly simple returns: (level_t / level_{t-1}) - 1 for consecutive months.
• Compute Pearson correlation between two return series.
• Round to the precision specified (usually 3 decimal places).
• For pair fields, sort index IDs alphabetically before placing them in the list.

Signal Scores

• Formulas like signal_score = (carry + mom + vol_z) / 3 are common.
• Round to specified precision.

Z-Scores

• z_score = (value - mean) / std_dev across the cross-section of assets.
• Round to specified precision.

Rankings

• Rank assets by a computed metric (e.g., signal_score, z_score).
• rank is typically 1-based.

View Determination (UW / N / OW)

• Compare signal_score against thresholds or cross-sectional rankings.
• change is derived by comparing the new view against prior_view:
  • Same → "UNCHANGED"
  • Higher (e.g., UW → N, N → OW) → "UP"
  • Lower (e.g., OW → N, N → UW) → "DOWN"

Rebalance Trigger & Next Step

• These are enums. Select the value that matches the narrative in the prompt and data.
• rebalance_trigger examples: correlation_cap_breach, hy_cap_pressure, duration_drift, watchlist_concentration, committee_review.
• next_step examples: approve_rotation, defer_pending_risk_review, approve_with_monitoring, reject_constraint_breach.

Boolean Flags

• portfolio_risk_concentration_flag: set based on whether any correlation or concentration metric exceeds a threshold described in the prompt or evident from the data.

4. Assembly — Build the Output JSON

• Include all required_top_level_keys in the exact order specified (or at minimum, ensure none are missing).
• For list fields, order items exactly according to item_order in the template.
• Use only values from allowed_values for enum fields.
• Format dates as YYYY-MM-DD.
• Ensure numbers match the specified precision.
• Ensure string fields with required_value match exactly.

5. Validation — Self-Check Before Writing

Run a mental checklist against the template:

• All required_top_level_keys present?
• All enum values in allowed_values?
• All list lengths match length?
• All list items in item_order sequence?
• All numbers rounded to precision?
• All required_value fields exact?
• Date format correct?
• Pairs alphabetically sorted where required?

Common Pitfalls

• Ignoring answer_template.json: The prompt alone is insufficient; the template is the authoritative schema.
• Wrong list ordering: item_order is strict. Re-ordering items will fail validation.
• Missing precision: Financial metrics typically require 3 decimal places. Do not truncate or over-round.
• Alphabetical pair sorting: Index IDs inside correlation pairs must often be sorted alphabetically, even if the prompt mentions them in a different order.
• Enum values: Inventing rationale codes or trigger names that are not in allowed_values will fail.
• Skipping payloads: Some tasks have multiple payload files; missing one leads to incomplete calculations.

Example Workflow Summary

1. Read input/prompt.txt → understand narrative.
2. Read input/payloads/answer_template.json → extract schema contract.
3. Read input/payloads/*.json → load all data.
4. Compute correlations, signal scores, z-scores, rankings, views per template rules.
5. Assemble JSON with exact keys, ordering, enums, and precision.
6. Write to output/answer.json.