Data and System Tools

3 data tools and 5 system tools for P&L, compliance, equity curves, signals, and margin.

Data Tools

calculate_pnl

Cost: $0.003 | Calculate profit and loss for a completed trade.

Parameter	Type	Required	Description
`entry_price`	string	Yes	Entry price.
`exit_price`	string	Yes	Exit price.
`quantity`	int	Yes	Number of shares/contracts.
`direction`	string	Yes	`"LONG"` or `"SHORT"`.
`stop_price`	string	No	Stop loss price. If provided, calculates R-multiple.

result = client.call_tool(
    "calculate_pnl",
    entry_price="185.50",
    exit_price="192.00",
    quantity=100,
    direction="LONG",
    stop_price="180.00",
)

Returns: gross_pnl, net_pnl, return_pct, r_multiple (if stop_price provided), one_r_dollars.

calculate_expected_value

Cost: $0.004 | Calculate expected value of a trade setup.

Parameter	Type	Required	Description
`win_rate`	string	Yes	Historical win rate as decimal (e.g. `"0.55"`).
`avg_win`	string	Yes	Average win in R-multiples.
`avg_loss`	string	Yes	Average loss in R-multiples (positive number).
`one_r_dollars`	string	No	Dollar value of 1R for dollar EV calculation.

result = client.call_tool(
    "calculate_expected_value",
    win_rate="0.55",
    avg_win="2.0",
    avg_loss="1.0",
    one_r_dollars="1000",
)

Returns: expected_value_r, expected_value_usd (if one_r_dollars provided), edge_present, trades_to_confidence.

check_compliance

Cost: $0.004 | Run compliance checks on a proposed trade.

Parameter	Type	Required	Description
`symbol`	string	Yes	Instrument symbol.
`direction`	string	Yes	`"long"` or `"short"`.
`quantity`	string	Yes	Number of shares/contracts.
`equity`	string	Yes	Account equity.
`existing_positions`	object[]	No	Current positions for concentration check.

result = client.call_tool(
    "check_compliance",
    symbol="AAPL",
    direction="long",
    quantity="500",
    equity="100000",
)

Returns: compliant, checks[] each with rule, passed, detail.

System Tools

calculate_equity_curve

Cost: $0.004 | Generate an equity curve from R-multiples.

Parameter	Type	Required	Description
`r_multiples`	string[]	Yes	R-multiples from trade history.
`starting_equity`	string	No	Starting equity. Default `"100000"`.
`risk_per_trade`	string	No	Risk fraction per trade. Default `"0.01"`.

result = client.call_tool(
    "calculate_equity_curve",
    r_multiples=["1.5", "-1.0", "2.3", "-0.5", "1.8"],
    starting_equity="100000",
)

Returns: equity_values[], total_return, cagr, max_drawdown, final_equity.

score_signal

Cost: $0.003 | Score a trading signal for quality and confidence.

Parameter	Type	Required	Description
`conditions_met`	int	Yes	Number of conditions met.
`total_conditions`	int	Yes	Total conditions checked.
`regime_aligned`	bool	Yes	Whether the trade is aligned with the market regime.
`indicator_confluence`	int	Yes	Number of indicators confirming the signal.
`volume_confirmed`	bool	Yes	Whether volume confirms the signal.
`risk_reward_ratio`	string	Yes	Risk/reward ratio (e.g. `"2.0"`).

result = client.call_tool(
    "score_signal",
    conditions_met=4,
    total_conditions=5,
    regime_aligned=True,
    indicator_confluence=3,
    volume_confirmed=True,
    risk_reward_ratio="2.5",
)

Returns: quality_score (0 to 100), confidence (HIGH, MEDIUM, LOW), score_breakdown.

analyze_trade_outcome

Cost: $0.003 | Analyze a completed trade's outcome quality.

Parameter	Type	Required	Description
`realized_pnl`	string	Yes	Net P&L in dollars.
`realized_r`	string	Yes	Actual R-multiple.
`mfe`	string	Yes	Maximum favorable excursion in dollars.
`one_r_dollars`	string	Yes	Dollar value of 1R.
`expected_value_r`	string	No	Expected value in R (for comparison).

result = client.call_tool(
    "analyze_trade_outcome",
    realized_pnl="1300",
    realized_r="1.3",
    mfe="2000",
    one_r_dollars="1000",
)

Returns: outcome_quality, capture_efficiency (realized / MFE), r_efficiency, above_expected (if expected_value_r provided).

calculate_margin

Cost: $0.002 | Calculate margin requirements for a position.

Parameter	Type	Required	Description
`symbol`	string	Yes	Instrument symbol.
`quantity`	string	Yes	Number of shares/contracts.
`price`	string	Yes	Current price.
`asset_type`	string	Yes	`"equity"`, `"option"`, `"future"`.
`margin_rate`	string	No	Margin rate override (e.g. `"0.25"` for 25%).

result = client.call_tool(
    "calculate_margin",
    symbol="AAPL",
    quantity="1000",
    price="185.50",
    asset_type="equity",
)

Returns: initial_margin, maintenance_margin, margin_pct, notional_value.

evaluate_scanner

Cost: $0.005 | Evaluate a market scanner configuration against market data.

Parameter	Type	Required	Description
`scanner_config`	object	Yes	Scanner with `scanner_id`, `name`, `scanner_type`, `symbols[]`, `timeframes[]`, `conditions[]`.
`market_data`	object	Yes	Market data keyed by symbol.

result = client.call_tool(
    "evaluate_scanner",
    scanner_config={
        "scanner_id": "scan-1",
        "name": "Momentum Scan",
        "scanner_type": "TECHNICAL",
        "symbols": ["AAPL", "MSFT", "GOOGL"],
        "timeframes": ["1h"],
        "conditions": ["rsi_oversold", "volume_spike"],
    },
    market_data={
        "AAPL": {"rsi": "28", "volume_ratio": "2.5", "price": "185.50"},
        "MSFT": {"rsi": "45", "volume_ratio": "1.2", "price": "410.00"},
        "GOOGL": {"rsi": "25", "volume_ratio": "3.1", "price": "175.00"},
    },
)

Returns: results[] each with symbol, direction, confidence, conditions_met[], suggested_entry, suggested_stop.

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`
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Privacy	Privacy Policy
Security	Security Policy

Data and System Tools

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