System R AI

Compound tools chain multiple operations into a single call. They cost less than calling each tool individually and provide a unified response.

pre_trade_gate

Cost: $0.01 | Complete pre-trade validation in one call.

Combines:

Position sizing (G-formula) to calculate the optimal number of shares.
Risk validation (Iron Fist rules) to approve or reject the trade.
System health (optional, if R-multiples provided) to assess current edge status.

Use this before every trade.

Parameters

Parameter	Type	Required	Description
`symbol`	string	Yes	Instrument symbol (e.g. `"AAPL"`).
`direction`	string	Yes	`"long"` or `"short"`.
`entry_price`	string	Yes	Planned entry price.
`stop_price`	string	Yes	Stop loss price.
`equity`	string	Yes	Account equity in USD.
`daily_pnl`	string	No	Daily P&L so far. Default `"0"`.
`risk_percent`	string	No	Risk as decimal. Default `"0.02"` (2%).
`r_multiples`	string[]	No	Recent R-multiples. If provided, includes system health check.

Example

from systemr import SystemRClient
 
client = SystemRClient(api_key="sr_agent_...")
 
result = client.pre_trade_gate(
    symbol="AAPL",
    direction="long",
    entry_price="185.50",
    stop_price="180.00",
    equity="100000",
    r_multiples=["1.5", "-1.0", "2.3", "-0.5", "1.8", "-1.0", "3.2", "0.8"],
)
 
if result["gate_passed"]:
    shares = result["sizing"]["shares"]
    print(f"BUY {shares} shares of AAPL at $185.50, stop at $180.00")
else:
    print(f"Trade blocked: {result['risk']['errors']}")

Response

{
  "gate_passed": true,
  "sizing": {
    "shares": 363,
    "risk_amount": "2000.00",
    "risk_percent": "0.02",
    "notional": "67351.50",
    "one_r_dollars": "2000.00",
    "direction": "long"
  },
  "risk": {
    "approved": true,
    "score": 85,
    "errors": [],
    "warnings": [],
    "risk_amount": "2000.00",
    "risk_percent": "0.02"
  },
  "system_health": {
    "g": "0.0847",
    "expected_r": "0.7875",
    "verdict": "POSITIVE_EDGE",
    "trade_count": 8
  }
}

How gate_passed is determined

gate_passed is true when both conditions are met:

sizing.shares > 0 (position size is valid)
risk.approved == true (all Iron Fist rules pass)

System health is informational. A declining G does not block the gate. You can add your own logic to incorporate system health into your decision.

assess_trading_system

Cost: $2.00 | Comprehensive trading system assessment in a single call.

Chains 7 analyses together:

G-metric evaluation to measure geometric growth rate
Win/loss analysis for batting average and payoff ratio
Kelly criterion for optimal position sizing
Monte Carlo simulation to project future equity paths
Drawdown analysis to measure downside risk
What-if scenarios to find improvement opportunities
Variance killer detection to identify G-damaging trades

Parameters

Parameter	Type	Required	Description
`r_multiples`	string[]	Yes	R-multiples from trade history (10+ recommended).
`equity_values`	string[]	No	Equity values after each trade (for drawdown).
`starting_equity`	string	No	Starting equity. Default `"100000"`.
`pnl_values`	string[]	No	USD P&L per trade (for detailed win/loss).

Example

result = client.assess_system(
    r_multiples=[
        "1.5", "-1.0", "2.3", "-0.5", "1.8", "-1.0", "3.2", "0.8",
        "-0.7", "1.1", "-0.3", "2.5", "-1.2", "1.6", "0.9",
    ],
    starting_equity="100000",
)
 
print(f"Verdict: {result['verdict']}")
print(f"G: {result['g_metrics']['g']}")
print(f"Win rate: {result['win_loss']['win_rate']}")
print(f"Kelly (half): {result['kelly']['half_kelly']}")
print(f"Monte Carlo profit probability: {result['monte_carlo']['probability_of_profit']}")
print(f"Max drawdown: {result['drawdown']['max_drawdown_pct']}")

Response structure

{
  "verdict": "POSITIVE_EDGE",
  "g_metrics": {
    "g": "0.0623",
    "expected_r": "0.62",
    "variance": "1.87",
    "trade_count": 15
  },
  "win_loss": {
    "win_rate": "0.667",
    "avg_win_r": "1.75",
    "avg_loss_r": "0.78",
    "payoff_ratio": "2.24",
    "profit_factor": "4.49",
    "expectancy": "0.91"
  },
  "kelly": {
    "full_kelly": "0.35",
    "half_kelly": "0.175",
    "quarter_kelly": "0.0875",
    "risk_of_ruin": "0.001"
  },
  "monte_carlo": {
    "probability_of_profit": "0.93",
    "probability_20pct_gain": "0.71",
    "probability_20pct_drawdown": "0.08",
    "median_final_equity": "118500"
  },
  "drawdown": {
    "max_drawdown_pct": "7.2",
    "longest_duration": 3,
    "recovery_factor": "4.8"
  },
  "what_if": {
    "cap_losses_at_1r": "0.0891",
    "avoid_worst_10pct": "0.0945"
  },
  "variance_killers": [
    {"index": 5, "r_multiple": "-1.2", "g_impact": "-0.012"}
  ]
}

Calling all 7 tools individually would cost approximately $0.04. The compound tool costs $2.00 because it runs full-depth Monte Carlo (5,000 simulations) and comprehensive analysis at each step. Use individual tools when you need specific metrics; use assess_trading_system when you want the complete picture.

record_trade_journal

Cost: $0.003 | Record a completed trade to the agent's journal.

Parameter	Type	Required	Description
`symbol`	string	Yes	Instrument symbol.
`direction`	string	Yes	`"long"` or `"short"`.
`entry_price`	string	Yes	Entry price.
`exit_price`	string	Yes	Exit price.
`stop_price`	string	Yes	Stop loss price.
`quantity`	string	Yes	Position quantity.
`r_multiple`	string	No	Realized R-multiple.
`pnl`	string	No	Realized P&L in dollars.
`trade_date`	string	No	Trade date as ISO string (e.g. `"2026-03-25"`).
`notes`	string	No	Free-text notes.

result = client.record_trade(
    symbol="AAPL",
    direction="long",
    entry_price="185.50",
    exit_price="192.00",
    stop_price="180.00",
    quantity="363",
    r_multiple="1.18",
    pnl="2360.50",
    trade_date="2026-03-25",
    notes="Breakout entry, rode to first target.",
)

Returns: confirmation with trade_id, symbol, r_multiple, pnl, recorded_at.

System R AI

Python SDK	`pip install systemr`
MCP Server	agents.systemr.ai/mcp/sse
OpenAPI Spec	agents.systemr.ai/openapi.json
Machine Docs	agents.systemr.ai/llms.txt
GitHub	System-R-AI
X	@Systemrai
YouTube	@systemr_ai
Email	hello@systemr.ai
Phone	`628 333 6693`
Address	`7901 4TH ST N, STE 28529, ST PETERSBURG, FL 33702`
Terms	Terms of Service
Privacy	Privacy Policy
Security	Security Policy

Compound Tools

pre_trade_gate

Parameters

Example

Response

How gate_passed is determined

assess_trading_system

Parameters

Example

Response structure

Cost comparison

record_trade_journal

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