System R Risk Intelligence

Name: System R Risk Intelligence
Author: System-R-AI

System-R-AI/systemr-python

2 repo stars
Updated July 1, 2026
System-R-AI/systemr-python

System R Risk Intelligence is a MCP server that validates trades and sizes positions for AI trading agents using G-formula and Iron Fist.

About

System R Risk Intelligence is a Model Context Protocol server that exposes System R’s pre-trade risk stack to coding agents and autonomous traders. developers shipping agentic trading workflows often lack a single, agent-callable gate between signal generation and order placement; this server centralizes that step with G-formula-based position sizing and Iron Fist rule enforcement so agents cannot silently oversize or bypass limits. You connect via hosted SSE/streamable-http with an agent API key, or run the PyPI systemr stdio transport for local Claude Code and Cursor setups. It fits quant and fintech experiments where one person owns strategy code and risk policy. Register an agent key, point your MCP client at the remote URL or local package, and invoke validation before any live or paper execution path you automate.

Pre-trade risk validation API for AI trading agents
Position sizing via G-formula methodology
Iron Fist enforcement layer for trade constraints
Remote streamable-http and SSE at agents.systemr.ai plus PyPI stdio package
X-API-Key registration at agents.systemr.ai/v1/agents/register

System R Risk Intelligence by the numbers

Data as of Jul 7, 2026 (Skillselion catalog sync)

terminal

claude mcp add systemr -- uvx systemr

Add your badge

Show developers this MCP server is listed on Skillselion. Paste this into your README.

[![Listed on Skillselion](https://skillselion.com/badge/mcp/tool/io.github.System-R-AI/systemr-risk-intelligence.svg)](https://skillselion.com/mcp/tool/io.github.System-R-AI/systemr-risk-intelligence)

repo stars	★ 2
Package	systemr
Transport	STDIO, HTTP, SSE
Auth	Required
Last updated	July 1, 2026
Repository	System-R-AI/systemr-python ↗

What it does

Wire pre-trade G-formula and Iron Fist checks into autonomous trading agents before orders leave your strategy.

Who is it for?

Best when you're wiring Claude Code or custom agents into System R with mandatory pre-trade checks.

Skip if: Skip if you only need portfolio analytics or post-trade reporting without live execution gates.

What you get

Your agent calls MCP tools for structured pre-trade approval and position sizing before capital hits the market.

Agent-callable pre-trade risk validation responses
G-formula position sizing recommendations aligned to Iron Fist rules
Repeatable MCP wiring for local or hosted agent stacks

By the numbers

Server version 1.1.0; PyPI package systemr 1.0.2 with stdio transport
Two remote endpoints (streamable-http and SSE) on https://agents.systemr.ai/mcp/sse

README.md

systemr

Python SDK for the System R AI API Toolkit.

System R AI is a decision intelligence system for trading and investing. The API Toolkit gives developers access to finance tools for agents, Python workflows, notebooks, and backend services.

The SDK is designed for structured decision-support workflows: position sizing, risk checks, performance diagnostics, market structure analysis, journal records, memory search, and tool discovery.

System R is software for decision support. It is not financial advice, not a broker, not a signal service, and not a guarantee of profits.

Install

pip install systemr

Requires Python 3.9 or higher.

Quick example

from systemr import SystemRClient

client = SystemRClient(api_key="sr_agent_...")

gate = client.pre_trade_gate(
    symbol="AAPL",
    direction="long",
    entry_price="185.50",
    stop_price="180.00",
    equity="100000",
)

print(gate)

pre_trade_gate combines position sizing, risk validation, and supplied system-health context into a single decision-support response.

Chat workflows

from systemr import SystemRClient

client = SystemRClient(api_key="sr_agent_...")

resp = client.chat(
    "Review these R-multiples and tell me what changed in the system: 1.5, -1.0, 2.0, -0.5, 1.8"
)

print(resp["text"])

LLM-backed workflows may use credits depending on the live billing rules. Check the live pricing and billing surfaces before building production workflows.

Tool access

Every current tool should be discovered from the live catalog before use:

tools = client.list_tools()

Generic tool calls:

result = client.call_tool(
    "calculate_position_size",
    equity="100000",
    entry_price="185.50",
    stop_price="180.00",
    direction="long",
)

Common tool areas include:

Risk and sizing
Performance diagnostics
Market structure analytics
Scenario planning
Journal and memory workflows
Asset or research context where supported or supplied
Billing and usage visibility where enabled

MCP and REST

The same API Toolkit can be used through MCP-compatible clients and REST integrations.

Docs: https://docs.systemr.ai/
Agents: https://agents.systemr.ai/
OpenAPI: https://agents.systemr.ai/v1/openapi.json
MCP overview: https://docs.systemr.ai/mcp/overview

Pricing

System R AI uses usage-based credits for paid workflows. Current rates and billing rules should be checked through the live pricing endpoint and the System R billing page.

Do not assume every tool, data path, or LLM-backed workflow has the same pricing behavior. Use live discovery and billing responses as the source of truth.