quantcoder-research

A research workflow for turning quantitative trading papers into draft QuantConnect LEAN algorithms, where LEAN is QuantConnect's algorithm framework.

In plain words
What is it for?
Use it to find and download papers, summarize trading methods, draft LEAN algorithms, run local checks, and optionally validate or backtest them with QuantConnect.
Why use it?
It organizes paper search, downloading, summarization, code drafting, and optional checks so research can be turned into testable code.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/nutdnuy/quantcoder-plugin/quantcoder-research
Any agent
npx skills add nutdnuy/quantcoder-plugin --skill quantcoder-research
Clone the repo
git clone --depth 1 https://github.com/nutdnuy/quantcoder-plugin

Made for: Claude Code, Codex.

Per session 105 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 851 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00105 $0.00851
Opus 5 $0.00053 $0.00426
Sonnet 5 $0.00021 $0.00170
Haiku 4.5 $0.00011 $0.00085

Measured yesterday against content hash 336ffbde4953, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

quantcoder-research 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 yesterday.

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.

skills/quantcoder-research/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QuantCoder Research

QuantCoder plugin is an agent-native research workflow for turning quantitative papers into draft QuantConnect LEAN algorithms. Claude Code or Codex Agent does the summarization, reasoning, and code drafting; QuantCoder utilities provide paper search/download, local QC linting, and optional QuantConnect validation/backtesting. Treat it as a research scaffold, not a source of guaranteed trading performance.

Preconditions

  • Python 3.10+.
  • Install the package from this plugin root when CLI utilities are needed:
cd "${CLAUDE_PLUGIN_ROOT:-.}"
uv run quantcoder --help
  • Claude Code or Codex is the LLM layer. Do not require a separate local model server for the default plugin workflow.

  • QuantConnect validation/backtesting needs environment variables or ~/.quantcoder/.env:

QUANTCONNECT_API_KEY=...
QUANTCONNECT_USER_ID=...
  • Optional deep search and publishing: TAVILY_API_KEY, NOTION_API_KEY, NOTION_DATABASE_ID.

Core Workflow

  1. Search for papers:
quantcoder search "momentum trading" --num 5
quantcoder search "mean reversion" --deep --num 10
  1. Download source material:
quantcoder download 1
  1. Summarize with the active agent:
  • Read the PDF/text or user-provided paper summary.
  • Extract: strategy hypothesis, asset universe, signal formula, rebalance rule, risk controls, data needs, lookback windows, portfolio construction, and expected failure modes.
  • Separate facts from assumptions and interpretations.
  1. Generate a QuantConnect draft with the active agent:
  • Write LEAN Python code directly in the workspace.
  • Prefer simple, auditable implementation over clever abstraction.
  • Add only comments that clarify non-obvious paper-to-code translation choices.
  1. Verify before interpreting results:
quantcoder validate generated_code/algorithm_1.py --local-only
quantcoder validate generated_code/algorithm_1.py
quantcoder backtest generated_code/algorithm_1.py --start 2020-01-01 --end 2024-01-01

Read the full file on GitHub · 107 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. yesterday First seen · 107 lines · 105 tokens per session scan A 336ffbde4953

Subscribe to this mod's changes

quantcoder-research is a skill published in the GitHub repository nutdnuy/quantcoder-plugin (1 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 105 tokens to every session and 851 once invoked, about $0.0005 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.

Related

Other skills, from other repositories

brain-paper-to-alpha-plugin

Use for WorldQuant BRAIN alpha research, paper-to-alpha workflows, BrainAlpha M0-M5 candidate generation, BRAIN expression triage, failed-alpha repair, simulation handoff, low-correlation pool review, or when the user mentions Papertocode, WorldQuant BRAIN, Brain alpha mining, IQC, alpha examples, alpha mutation…

nutdnuy/brain-paper-to-alpha-plugin · 101 tokens

a2ui-renderer

Render A2UI (Agent-to-UI declarative surfaces) in CopilotKit v2. Enable the runtime via CopilotRuntime({ a2ui: {...} }), then enable the provider via . Auto-activates via /info — do NOT manually pass renderActivityMessages. createA2UIMessageRenderer ships from @copilotkit/react-core/v2; low-level primitives…

CopilotKit/CopilotKit · 175 tokens

copilotkit-develop

Use when building AI-powered features with CopilotKit v2 -- adding chat interfaces, registering frontend tools, sharing application context with agents, handling agent interrupts, and working with the CopilotKit runtime.

CopilotKit/CopilotKit · 46 tokens

copilotkit-upgrade

Use when migrating a CopilotKit v1 application to v2 -- updating package imports, replacing deprecated hooks and components, switching from GraphQL runtime to AG-UI protocol runtime, and resolving breaking API changes.

CopilotKit/CopilotKit · 48 tokens

adr-hshare

ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.

HKUDS/Vibe-Trading · 50 tokens

bottleneck-hunter

Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar…

HKUDS/Vibe-Trading · 145 tokens