fx_quant_engine/options_quant_engine/options_quant_engine/schemas.py

from __future__ import annotations

from dataclasses import asdict, dataclass, field
from datetime import datetime, timezone
from typing import Any

import pandas as pd


@dataclass
class DataFetchResult:
    data: pd.DataFrame
    source: str
    reliability: str
    latency_ms: int
    success: bool = True
    error: str | None = None


@dataclass
class RegimeState:
    trend: str
    volatility: str
    dollar: str
    risk: str
    combined: str
    stability: float


@dataclass
class SignalPayload:
    engine: str
    timestamp: str
    asset: str
    signal_type: str
    signal_direction: str
    signal_strength: float
    confidence: float
    regime: dict[str, Any]
    expected_volatility: float
    risk_flags: list[str]
    drivers: dict[str, Any]
    recommended_action: str
    position_sizing_multiplier: float

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class EvaluationReport:
    generated_at: str
    hit_rate: float
    avg_forward_return: float
    regime_performance: dict[str, float]
    signal_decay: dict[str, float]
    feature_importance: dict[str, float]
    drift_flags: list[str]
    calibration_score: float


@dataclass
class BacktestResult:
    pair_level_returns: dict[str, float]
    portfolio_return: float
    max_drawdown: float
    turnover: float
    metrics: dict[str, float]


@dataclass
class SourceUsageRecord:
    asset: str
    source: str
    usage_type: str
    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
    success: bool = True
    error: str | None = None