mirror of
https://github.com/xavierchuan/FX-ML-Trading-Engine.git
synced 2026-07-27 18:17:44 +00:00
xiaochuan
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
"""Lightweight risk engine enforcing exposure, leverage, and loss caps."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Dict, Tuple
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@dataclass
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class RiskLimits:
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max_position_notional: float
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max_gross_leverage: float
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max_daily_loss: float
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max_drawdown: float
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@dataclass
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class RiskState:
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equity: float = 0.0
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peak_equity: float = 0.0
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min_equity: float = float("inf")
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realized_pnl: float = 0.0
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gross_notional: float = 0.0
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exposures: Dict[str, float] = field(default_factory=dict)
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class RiskViolation(Exception):
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"""Raised when orders violate limits."""
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class RiskEngine:
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def __init__(self, limits: RiskLimits, starting_equity: float):
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self.limits = limits
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self.state = RiskState(equity=starting_equity, peak_equity=starting_equity, min_equity=starting_equity)
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def evaluate_order(self, symbol: str, side: str, notional: float) -> Tuple[bool, str]:
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exposure = self.state.exposures.get(symbol, 0.0)
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proposed = exposure + (notional if side.lower() == "buy" else -notional)
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if abs(proposed) > self.limits.max_position_notional:
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return False, f"symbol_exposure_limit:{symbol}"
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gross = self.state.gross_notional + abs(notional)
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leverage = gross / self.state.equity if self.state.equity else float("inf")
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if leverage > self.limits.max_gross_leverage:
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return False, "gross_leverage_limit"
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return True, "ok"
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def record_fill(self, symbol: str, side: str, notional: float, pnl: float) -> None:
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delta = notional if side.lower() == "buy" else -notional
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self.state.exposures[symbol] = self.state.exposures.get(symbol, 0.0) + delta
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self.state.gross_notional = sum(abs(v) for v in self.state.exposures.values())
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self.state.realized_pnl += pnl
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self.state.equity += pnl
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self.state.peak_equity = max(self.state.peak_equity, self.state.equity)
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self.state.min_equity = min(self.state.min_equity, self.state.equity)
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def check_loss_limits(self) -> Tuple[bool, str]:
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if -self.state.realized_pnl > self.limits.max_daily_loss:
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return False, "daily_loss_limit"
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drawdown = (self.state.equity - self.state.peak_equity) / self.state.peak_equity if self.state.peak_equity else 0.0
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if drawdown < -self.limits.max_drawdown:
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return False, "drawdown_limit"
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return True, "ok"
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def max_drawdown_pct(self) -> float:
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if not self.state.peak_equity:
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return 0.0
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trough = self.state.min_equity if self.state.min_equity != float("inf") else self.state.equity
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return abs((trough - self.state.peak_equity) / self.state.peak_equity)
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