Parallax House View Attribution

House View Attribution

When not to use

• Is the view stale vs current signals? → use /parallax-judge-house-view
• Is the view internally consistent? → use /parallax-stress-house-view
• LP letter with period attribution → use /parallax-cio-letter-prep
• "Why is my portfolio down?" → use /parallax-explain-portfolio
• Ex-ante "did the view change the output at all?" → the paired-leg protocol in /parallax-house-view-diff

Gotchas

• READ-ONLY against the view: never writes view.yaml / prose.md / provenance.yaml. Output is one report bundle + one action="attribution" audit row (applied=false ALWAYS) via audit_chain.append_entry.
• JIT-load _parallax/house-view/loader.md §6 for the attribution action's conditional fields (attributed_view_id, attributed_version_id, attribution_window, attribution_summary, report_hash).
• Reconstruction source is the reasoning-chain archive (~/.parallax/reasoning-chains/), NOT audit.jsonl — §6.3 forbids holdings arrays on audit rows, so consume rows carry no weights. If consumer runs emitted no chains in the window, coverage is honestly zero: report "N of M chains usable; cannot attribute" rather than inventing a portfolio.
• "Neutral" is pinned as same holdings, all loader.md §3 weight multipliers at 1.00× (score-proportional when per-holding base scores are recoverable, else equal-weight tagged approximate). It is NOT a market benchmark — do not substitute one; that would make the number unfalsifiable against the view's actual decision surface.
• export_price_series is trailing-365-days only (no start/end anchor). Windows starting >365 days ago: refuse with "window start > 365 days ago — exportpriceseries cannot reach it; attribute a sub-window or archive prices externally."
• A single-window ex-post diff is directional evidence, NOT statistical validation. Render the §5.1a heuristic_phase0 calibration disclosure verbatim in the report and never claim significance. Promotion to empirical_phase1 is a signed-manifest operator action (the manifest cites this run's report_hash as backtest_ref) — this skill never touches calibration_status.
• Factor and style tilts affect composite re-ranking / universe filtering, not weight multipliers — their effect lands in selection_residual_bps by construction. Say so in the report; do not fold the residual into a tilt group.
• The reference portfolio is whatever the chains recorded — do not let the caller cherry-pick a subset of chains without recording the exclusion in the report ("chains_used" vs chains found).

Usage

/parallax-house-view-attribution                       # attribute the active view over its effective window
/parallax-house-view-attribution --view       # attribute an archived view (from .archive/)
/parallax-house-view-attribution --window 2026-04-01 2026-06-30   # explicit sub-window

Workflow

Call ToolSearch with query "+Parallax" to load the deferred MCP tool schemas. JIT-load _parallax/house-view/loader.md (§6 audit format) and this skill's attribution.py (pure math layer — all reconstruction, counterfactual, and Shapley logic lives there; do not re-derive it inline).

Phase 0 — Resolve target view + window

Default: the active view (~/.parallax/active-house-view/view.yaml, validated per loader.md §2 — an expired view is still attributable; validation here is integrity, not applicability). With --view : resolve from .archive/-/ (latest version unless one is named). Window = [effective_date, min(valid_through_or_computed_expiry, today)], clipped by --window. Refuse windows starting >365 days ago (exportpriceseries limit).

Phase 1 — Gather chains

attribution.load_window_chains(view_id, window_start, window_end). Cross-check against audit.jsonl consume rows (count of applied=true consumes in-window vs chains found) and report the coverage fraction. Zero usable chains → halt with InsufficientProvenance message; append the audit row with attribution_summary.segments = 0 and a notes explanation.

Phase 2 — Segment by version

attribution.segment_by_version(chains) — a view superseded mid-window is attributed per-segment and summed. Note each segment's version_id in the report.

Phase 3 — Market data (MCP)

For every holding across segments: export_price_series (total-return closes — same TR convention as parallax-cio-letter-prep; never mix raw closes) over each segment's sub-window; compute per-holding period returns. analyze_portfolio on the chain weights is the server-side cross-check when available — flag if local math diverges materially, mirroring the cio-letter-prep canonical-server rule. Classify each holding via get_peer_snapshot / get_company_info into schema keys (sector, region, themes) for holding_meta; unclassifiable holdings get multiplier 1.0 (their effect lands in the residual — never guess a sector). Holdings with no price data are dropped with weights renormalized (§3b partial-result semantics) and counted in holdings_dropped.

Phase 4 — Attribute

Per segment: attribution.attribute_segment(chain, returns, holding_meta) (uses the latest chain per segment as the decision record; note in the report when a segment had multiple chains and which was used — or attribute each chain and average, stating the choice). Then attribution.merge_segments(...) for the run-level summary: tilted-vs-neutral active bps, per-tilt-group Shapley contributions, selection residual, counterfactual quality.

Phase 5 — Render + audit

Write the bundle to ~/.parallax/attribution-reports/-_-/ (dir 0700, files 0600):

• report.md — evidence-shaped: window + segments table, per-tilt contribution table (group, bps, direction vs the tilt's intent), selection residual with its explanation, coverage/drops, counterfactual quality, and the §5.1a calibration disclosure verbatim.
• report.json — the attribution_summary object plus per-segment detail.
• audit_entry.json — copy of the appended row.

Compute report_hash = sha256(report.md bytes). Append ONE audit row per loader.md §6 via audit_chain.append_entry: action="attribution", applied=false, attributed_view_id, attributed_version_id, attribution_window, attribution_summary (with reference_portfolio_hash = sha256 of the sorted chain run_ids used, and chains_used), report_hash. Emit a reasoning chain via chain_emit.emit_phase_0_chain (skill_version="parallax-house-view-attribution@1.0.0", final_portfolio={"weights": {}}).

Output Format

Lead with the verdict sentence: "Over [window], the view's tilts contributed [±N] bps vs neutral ([quality] counterfactual; [K] segments, [C] holdings covered, [D] dropped)." Then the per-tilt table, the residual line, coverage notes, and the report-bundle path as a file:// URL.

AI-interaction disclosure: render parallax-conventions.md §9.2 immediately above the disclaimer (this is an operator-facing analysis report — same reasoning as the judge's §9.2 treatment; no config-artifact exemption).

Use the view-aware disclaimer per loader.md §5 rule 5 (the analyzed view is named even when expired), with one added sentence: "Attribution is a single-window ex-post estimate under a heuristic multiplier model; it is not statistical validation of the view or the tilt calibration."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: bencharoenwong
• Source: bencharoenwong/parallax-workflows
• License: MIT

Install and usage instructions live in the source repository linked above.