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