backtest/tools.py

from __future__ import annotations

import json

from smolagents import tool

from market_data.providers import get_price_history
from strategy.payoff import expiration_payoff, strategy_summary
from strategy.schemas import OptionLeg, OptionStrategy

from .option_backtest import backtest_long_straddle_from_quotes, load_option_quotes_csv
from .vol_backtest import backtest_realized_vol_signal


def parse_legs(legs_json: str) -> list[OptionLeg]:
    payload = json.loads(legs_json)
    if isinstance(payload, dict) and "legs" in payload:
        payload = payload["legs"]
    return [OptionLeg(**leg) for leg in payload]


@tool
def analyze_strategy_payoff(legs_json: str, min_price: float, max_price: float, steps: int = 25) -> str:
    """Analyze expiration payoff for an option strategy.

    Args:
        legs_json: JSON list of option legs from build_volatility_strategy.
        min_price: Minimum underlying price scenario.
        max_price: Maximum underlying price scenario.
        steps: Number of scenario steps.
    """
    try:
        legs = parse_legs(legs_json)
        points = [
            min_price + (max_price - min_price) * index / max(steps, 1)
            for index in range(max(steps, 1) + 1)
        ]
        rows = [
            {"underlying_price": round(price, 2), "pnl": round(expiration_payoff(legs, price), 2)}
            for price in points
        ]
        temp_strategy = OptionStrategy(
            name="custom_strategy",
            volatility_view="unknown",
            directional_view="unknown",
            legs=legs,
            rationale="custom payoff analysis",
            risks=[],
            max_profit=None,
            max_loss=None,
            breakevens=[],
            net_debit_or_credit=round(sum(leg.premium * leg.signed_quantity() * 100 for leg in legs), 2),
            score=0.0,
        )
        return json.dumps(
            {
                "status": "success",
                "payoff_rows": rows,
                "payoff_summary": strategy_summary(temp_strategy),
            },
            ensure_ascii=False,
            indent=2,
        )
    except Exception as exc:
        return json.dumps({"status": "error", "message": str(exc)}, ensure_ascii=False, indent=2)


@tool
def backtest_volatility_signal(
    symbol: str,
    signal: str = "long_vol",
    period: str = "2y",
    short_window: int = 10,
    long_window: int = 30,
    holding_days: int = 5,
) -> str:
    """Backtest a simple realized-volatility expansion/compression signal on the underlying.

    Args:
        symbol: Yahoo Finance ticker.
        signal: long_vol or short_vol.
        period: Yahoo Finance history period.
        short_window: Short realized volatility lookback.
        long_window: Long realized volatility lookback.
        holding_days: Holding period after entry.
    """
    try:
        history = get_price_history(symbol, period=period, interval="1d")
        result = backtest_realized_vol_signal(
            history["Close"],
            short_window=short_window,
            long_window=long_window,
            holding_days=holding_days,
            signal=signal,
        )
        return json.dumps({"status": "success", "symbol": symbol.upper(), **result}, ensure_ascii=False, indent=2)
    except Exception as exc:
        return json.dumps({"status": "error", "symbol": symbol, "message": str(exc)}, ensure_ascii=False, indent=2)


@tool
def backtest_long_straddle_csv(
    csv_path: str,
    symbol: str,
    target_dte: int = 30,
    holding_days: int = 5,
    entry_every_days: int = 5,
    price_field: str = "trade",
) -> str:
    """Run a real option-quote backtest for repeated ATM long straddles.

    This is a true option PnL backtest when supplied with historical option quotes.
    Required CSV columns: date, underlying_symbol, underlying_price, contract_symbol,
    option_type, expiration, strike, bid, ask. Optional columns include mid, delta,
    gamma, theta, vega, implied_volatility, volume, open_interest.

    Args:
        csv_path: Path to historical option quotes CSV.
        symbol: Underlying ticker.
        target_dte: Target days to expiration at entry.
        holding_days: Number of calendar days to hold each straddle.
        entry_every_days: Minimum days between new entries.
        price_field: trade for buy-at-ask/sell-at-bid, or mid for mid-price marks.
    """
    try:
        quotes = load_option_quotes_csv(csv_path)
        result = backtest_long_straddle_from_quotes(
            quotes=quotes,
            symbol=symbol,
            target_dte=target_dte,
            holding_days=holding_days,
            entry_every_days=entry_every_days,
            price_field=price_field,
        )
        return json.dumps({"status": "success", **result}, ensure_ascii=False, indent=2)
    except Exception as exc:
        return json.dumps(
            {
                "status": "error",
                "symbol": symbol,
                "message": str(exc),
                "note": "A real option backtest requires historical option quote data. yfinance does not provide reliable historical option chains.",
            },
            ensure_ascii=False,
            indent=2,
        )