performance-attribution

performance-attribution is a skill for Claude Code, Codex from skloxo/TideTrading. It costs 32 tokens per session (2,420 once invoked), scanned A, original, MIT.

A method for explaining why an investment portfolio performed differently from its benchmark, which is a reference portfolio or index. It breaks the difference into sector allocation, stock selection, factor exposure and market timing.

In plain words
What is it for?
Use it to compare a portfolio with a benchmark, measure sector-weight and stock-selection effects, separate factor-based returns from other returns, and evaluate timing decisions.
Why use it?
A return percentage alone does not show what caused the result. This analysis connects gains or losses to specific investment decisions and market exposures.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to compare a portfolio with a benchmark, measure sector-weight and…

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Install with agentmods
npx agentmods add skills/skloxo/tidetrading/performance-attribution
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add skloxo/TideTrading --skill performance-attribution
Clone the repo
git clone --depth 1 https://github.com/skloxo/TideTrading

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for performance-attribution

README.md
[![agentmods](https://agentmods.dev/badge/skills/skloxo/tidetrading/performance-attribution.svg)](https://agentmods.dev/skills/skloxo/tidetrading/performance-attribution)
Your own site
<a href="https://agentmods.dev/skills/skloxo/tidetrading/performance-attribution"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/performance-attribution.svg" alt="Measured on agentmods" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,420 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00032 $0.02420
Opus 5 $0.00016 $0.01210
Sonnet 5 $0.00006 $0.00484
Haiku 4.5 $0.00003 $0.00242

Measured 6d ago against content hash 434df366d1ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

performance-attribution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agent/src/skills/performance-attribution/SKILL.md · 255 lines

How it starts

The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Attribution Analysis

Overview

Decompose portfolio excess returns into explainable sources: sector allocation, stock selection, factor exposure, timing contribution, and more. This helps explain why a strategy made or lost money, rather than only how much it made or lost.

Brinson Attribution Model

Single-Period Brinson-Fachler Model

Total excess return = portfolio return - benchmark return

Decomposed into three parts:
1. Allocation effect: sector-weight deviation × sector benchmark return deviation
2. Selection effect: stock selection within a sector × sector benchmark weight
3. Interaction effect: weight deviation × stock-selection deviation

Mathematical formulas:

Let w_p,i = portfolio weight of sector i
    w_b,i = benchmark weight of sector i
    r_p,i = portfolio return of sector i
    r_b,i = benchmark return of sector i
    R_b   = total benchmark return

Allocation_i = (w_p,i - w_b,i) × (r_b,i - R_b)
Selection_i  = w_b,i × (r_p,i - r_b,i)
Interaction_i = (w_p,i - w_b,i) × (r_p,i - r_b,i)

Total excess = Σ(Allocation_i) + Σ(Selection_i) + Σ(Interaction_i)

Example Brinson Attribution

### Brinson Sector Attribution

| Sector | Portfolio Weight | Benchmark Weight | Portfolio Return | Benchmark Return | Allocation | Selection | Interaction |
|------|---------|---------|---------|---------|---------|---------|---------|
| Food & Beverage | 20% | 10% | 15% | 8% | +0.3% | +0.7% | +0.7% |
| Electronics | 15% | 12% | 5% | 10% | -0.1% | -0.6% | +0.2% |
| Banks | 5% | 20% | 3% | 2% | +0.3% | +0.2% | -0.2% |
| Others | 60% | 58% | 8% | 7% | +0.0% | +0.6% | +0.0% |
| **Total** | 100% | 100% | 9.5% | 6.2% | **+0.5%** | **+0.9%** | **+0.7%** |

Excess return 3.3% = allocation effect 0.5% + selection effect 0.9% + interaction effect 0.7% + residual 0.2%

Multi-Period Attribution (Linked Brinson)

Directly summing single-period attribution creates residuals (compounding effect). Common approaches:

Method 1: arithmetic linking (simple sum of each period's attribution)
  - Advantage: simple
  - Disadvantage: residual remains

Method 2: Carino logarithmic linking
  - Advantage: no residual
  - Disadvantage: more complex

Practical recommendation: arithmetic linking is enough for monthly attribution; residuals are usually <0.1%

Read the full file on GitHub · 255 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 255 lines · 32 tokens per session scan A 434df366d1ed

Subscribe to this mod's changes

performance-attribution is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 2,420 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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