What is quantforge AGENTS.md?

A set of instructions for coding agents building QuantForge, an open-source research system for testing investment strategies and analysing portfolios and risk.

Use it when developing or changing QuantForge's data pipeline, strategy backtests, portfolio optimisation, analytics, AI research agent, or user interface.

It defines required research safeguards, such as avoiding look-ahead bias, accounting for trading costs, and keeping test data separate from development.

How kars0311/quantforge configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to kars0311/quantforge. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/kars0311/quantforge/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/kars0311/quantforge

Made for: Codex, OpenCode.

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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/kars0311/quantforge/agents-md/github.svg)](https://agentmods.dev/instructions/kars0311/quantforge/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/kars0311/quantforge/agents-md"><img src="https://agentmods.dev/badge/instructions/kars0311/quantforge/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/kars0311/quantforge/agents-md"><img src="https://agentmods.dev/badge/instructions/kars0311/quantforge/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 1,180 This file is loaded in full into every session.

When invoked 1,180 The same file — it is already loaded in full.

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.

Model	Per session	Once invoked
Fable 5.1	$0.01180	$0.01180
Opus 5.5	$0.00472	$0.00472
Sonnet 5.5	$0.00236	$0.00236
Haiku 4.5	$0.00118	$0.00118

Measured 2mo ago against content hash a577b9a783dc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

quantforge AGENTS.md 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 2mo 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.

AGENTS.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — build guide for this repo

You (an AI coding agent) are building out this project from a scaffold. This file is the operating manual. Read docs/PROJECT_BRIEF.md, docs/architecture.md, and docs/TEN_WEEK_PLAN.md before starting.

What this is

An open-source, AI-driven, polyglot quant research pipeline: data → strategies → backtest → portfolio optimization → risk/perf analytics → interactive UI → cloud, with an AI research agent and a natural-language interface driving the pipeline over MCP tools.

Non-negotiable rules

Statistical rigor (this is the whole point):
- No look-ahead bias. A position decided using data through day t earns day t+1's return. The engine enforces this with positions.shift(1). Never let a signal peek at same-day data it couldn't have known at decision time.
- Transaction costs modeled on turnover.
- Survivorship bias acknowledged (fixed historical universe; document the caveat).
- Out-of-sample: the AI agent gets train/validation only; the holdout is scored once and never optimized against. An LLM iterating on the test set is p-hacking — prevent it.
- Prove correctness: validate the custom engine against backtesting.py (tests/test_engine_vs_backtestingpy.py).
Safety / cost (a public URL with paid AI hooks is a liability):
- All Claude API calls go through ai/budget.py (hard AI_BUDGET_USD_* caps + kill-switch).
- PUBLIC_MODE=on ⇒ parameter-only: never execute LLM-generated code on the server; only vetted strategies + whitelisted params. Gate live AI behind DEMO_PASSCODE; rate-limit.
Architecture discipline:
- Everything depends on the Strategy/Engine interfaces in engine/base.py. Freeze them.
- Cross-stage / cross-language hand-offs use the Arrow/Parquet contract in interchange.py.
- metrics/performance.py is the single source of truth for metrics; the R layer must match it.
Defensibility: add docstrings that explain why, not just what. The human author must be able to explain every component in an interview. Prefer clear, conventional code over cleverness.

Read the full file on GitHub · 89 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.

2mo ago First seen · 89 lines · 1,180 tokens per session scan A a577b9a783dc

Subscribe to this mod's changes

quantforge AGENTS.md is an instructions file published in the GitHub repository kars0311/quantforge (0 stars, last pushed 16d ago), licensed MIT. It adds 1,180 tokens to every session, about $0.0047 per session on Opus 5.5. 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.

Finance and fintech

quantforge AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
quantforge AGENTS.mdkars0311/quantforge	Instructions file	0	16d ago	MIT	A	1,180	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	yesterday	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

quantforge: Instructions file for Codex