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System-R-AI

System R Risk Intelligence

Official
by System-R-AI

systemr

Python SDK for agents.systemr.ai — AI-native risk intelligence for trading agents.

PyPI Python License: MIT

Install

pip install systemr

Quick Start

from systemr import SystemRClient

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

# Position sizing ($0.003)
result = client.calculate_position_size(
    equity="100000",
    entry_price="185.50",
    stop_price="180.00",
    direction="long",
)
print(result["shares"], result["risk_amount"])

# Risk validation ($0.004)
risk = client.check_risk(
    symbol="AAPL",
    direction="long",
    entry_price="185.50",
    stop_price="180.00",
    quantity="100",
    equity="100000",
)
print(risk["approved"], risk["score"])

# Strategy evaluation ($0.10 - $1.00)
eval_result = client.basic_eval(r_multiples=["1.5", "-1.0", "2.3", "-0.5", "1.8"])
print(eval_result["g_score"], eval_result["verdict"])

Get an API Key

import httpx

resp = httpx.post("https://agents.systemr.ai/v1/agents/register", json={
    "owner_id": "your-id",
    "agent_name": "my-trading-agent",
    "agent_type": "trading",
})
data = resp.json()
print(data["api_key"])  # sr_agent_... (save this, shown only once)

API Reference

Agent Management

Method

Description

client.get_info()

Get agent info

client.list_agents()

List owner's agents

client.update_mode(mode)

Change mode (sandbox/live/suspended/terminated)

Position Sizing

Method

Cost

client.calculate_position_size(equity, entry_price, stop_price, direction)

$0.003

Risk Validation

Method

Cost

client.check_risk(symbol, direction, entry_price, stop_price, quantity, equity)

$0.004

Evaluation

Method

Cost

Description

client.basic_eval(r_multiples)

$0.10

G metric + verdict

client.full_eval(r_multiples)

$0.50

G + rolling G + System R Score

client.comprehensive_eval(r_multiples)

$1.00

Full analysis + impact

Billing

Method

Description

client.get_pricing()

Operation prices (no auth)

client.get_balance()

Current USDC balance

client.deposit(amount)

Record deposit

client.get_transactions()

Transaction history

client.get_usage()

Usage summary

Error Handling

from systemr import SystemRClient, AuthenticationError, InsufficientBalanceError, SystemRError

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

try:
    result = client.calculate_position_size(...)
except AuthenticationError:
    print("Invalid API key or agent inactive")
except InsufficientBalanceError:
    print("Deposit USDC to continue")
except SystemRError as e:
    print(f"API error {e.status_code}: {e.detail}")

Context Manager

with SystemRClient(api_key="sr_agent_...") as client:
    result = client.check_risk(...)
# connection automatically closed

MCP (Model Context Protocol)

System R is also available as an MCP server for AI assistants like Claude and ChatGPT. See the MCP documentation for configuration.

License

MIT

-
security - not tested
A
license - permissive license
-
quality - not tested

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