systemr 1.0.1

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

systemr

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

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