OpenFin/backend/services/strategy_comparison.py

"""
Retrospective "what would have been optimal" comparison for an existing Portfolio position
— reuses the Strategy Builder optimizer (services.strategy_optimizer.optimize) against the
REAL historical option chain reconstructed as of the position's entry_date
(services.option_chain.get_chain_slice's `as_of` param, backed by
services.database.get_snapshot_rows_asof — the accumulated Saxo snapshot history, not
synthetic data), scored against the REALIZED spot/IV move between entry and the comparison
date rather than a guessed scenario — it's not a forecast, it's what actually happened.

Results are compared in PERCENTAGE terms (return on capital / return on risk), never raw
dollars: Strategy Builder's own pricer (services.strategy_engine, contract_size configurable,
FX-lot-style default 100_000) and Portfolio's pricer (services.portfolio_pricing, hardcoded
qty*100 equity-option-style) use different contract-size conventions for historical reasons —
comparing their dollar outputs directly would silently misstate the comparison by orders of
magnitude (the exact class of bug this project hit before with the COMEX copper scale issue).
A % return is convention-agnostic since the contract size cancels out of the ratio.
"""
from datetime import date, datetime
from typing import Any, Dict, Optional


def _reprice_actual_legs_pct(
    legs: list, chain_entry: Dict[str, Any], surface_entry, surface_realized,
    horizon_days: int, capital_invested: float, ib_fees_entry: float,
) -> Optional[float]:
    """% return of the position's REAL legs, priced at entry (services.portfolio_pricing's
    own qty*100 convention — mirrored here, not imported, since that module's functions are
    tied to "now" market data, not a historical `as_of` chain) then repriced under the
    realized move. Returns None if the position has no legs to price."""
    from services.options_pricer import black_scholes
    from services.option_chain import find_quote

    if not legs:
        return None
    r = 0.05
    S_entry = chain_entry["spot"]
    S_realized = surface_realized.spot
    T_remaining = max(horizon_days, 0) / 365

    pnl = -ib_fees_entry
    for leg in legs:
        K = leg.get("strike") or S_entry
        opt_type = leg.get("option_type", "call")
        qty = leg.get("quantity", 1)
        sign = 1 if leg.get("position", "long") == "long" else -1
        days_to_expiry = leg.get("days_to_expiry", 90)

        entry_premium = leg.get("premium_paid")
        if entry_premium is None:
            quote = find_quote(chain_entry, leg.get("expiry_date", ""), K, opt_type)
            if quote and quote.get("bid", 0) > 0 and quote.get("ask", 0) > 0:
                entry_premium = quote["mid"]
            else:
                sigma_entry = surface_entry.iv_at(K, days_to_expiry)
                entry_premium = black_scholes(S_entry, K, max(days_to_expiry, 1) / 365, r, sigma_entry, opt_type)["price"]

        remaining = max(days_to_expiry - horizon_days, 0.001)
        sigma_realized = surface_realized.iv_at(K, remaining)
        realized_price = (
            black_scholes(S_realized, K, remaining / 365, r, sigma_realized, opt_type)["price"]
            if T_remaining > 0 and remaining > 0.001
            else (max(0.0, S_realized - K) if opt_type == "call" else max(0.0, K - S_realized))
        )
        pnl += sign * qty * 100 * (realized_price - entry_premium)

    if not capital_invested:
        return None
    return round(pnl / capital_invested * 100, 2)


def compute_retrospective_comparison(pos: Dict[str, Any], as_of: Optional[str] = None) -> Dict[str, Any]:
    from services.database import get_saxo_option_symbol_for_ticker
    from services.option_chain import get_chain_slice
    from services.vol_surface import build_surface, apply_scenario
    from services.strategy_optimizer import optimize as run_optimizer

    underlying = pos["underlying"]
    saxo_symbol = get_saxo_option_symbol_for_ticker(underlying)
    if not saxo_symbol:
        return {"available": False, "reason": f"'{underlying}' n'est pas lié à un chain Saxo (Config → Instruments Watchlist)."}

    entry_date = pos["entry_date"][:10]
    as_of_date = (as_of or pos.get("close_date") or date.today().isoformat())[:10]
    if as_of_date <= entry_date:
        return {"available": False, "reason": "La date de comparaison doit être postérieure à la date d'entrée."}

    horizon_days = (
        datetime.strptime(as_of_date, "%Y-%m-%d").date() - datetime.strptime(entry_date, "%Y-%m-%d").date()
    ).days
    target_days_entry = pos.get("expiry_days", 90)

    try:
        chain_entry = get_chain_slice(saxo_symbol, target_days=target_days_entry, n_expiries=3, as_of=entry_date)
    except ValueError as e:
        return {"available": False, "reason": f"Pas d'historique Saxo à la date d'entrée ({entry_date}) : {e}"}
    try:
        chain_realized = get_chain_slice(
            saxo_symbol, target_days=max(target_days_entry - horizon_days, 1), n_expiries=3,
            as_of=as_of_date if as_of else None,
        )
    except ValueError as e:
        return {"available": False, "reason": f"Pas d'historique Saxo à la date de comparaison ({as_of_date}) : {e}"}

    surface_entry = build_surface(chain_entry)
    surface_realized_base = build_surface(chain_realized)

    spot_entry = chain_entry["spot"]
    spot_realized = chain_realized["spot"]
    if not spot_entry or not spot_realized:
        return {"available": False, "reason": "Spot manquant dans l'historique Saxo à l'une des deux dates."}

    realized_spot_shock_pct = round((spot_realized - spot_entry) / spot_entry * 100, 2)
    iv_entry = surface_entry.iv_at(spot_entry, target_days_entry)
    iv_realized = surface_realized_base.iv_at(spot_realized, max(target_days_entry - horizon_days, 1))
    realized_iv_shift = round(iv_realized - iv_entry, 4)

    # The same realized shock, applied on top of the ENTRY surface — puts the "actual
    # position" and "optimal candidates" repricing on the exact same footing (both start
    # from what was really quoted at entry, both move by what really happened afterwards).
    surface_realized = apply_scenario(surface_entry, spot_shock_pct=realized_spot_shock_pct, iv_level_shift=realized_iv_shift)

    actual_return_pct = _reprice_actual_legs_pct(
        pos.get("legs", []), chain_entry, surface_entry, surface_realized,
        horizon_days, pos.get("capital_invested") or 0, pos.get("ib_fees_entry", 0),
    )

    optimal_candidates = run_optimizer(
        symbol=saxo_symbol, horizon_days=horizon_days,
        spot_shock_pct=realized_spot_shock_pct, iv_level_shift=realized_iv_shift,
        skew_tilt=0.0, term_slope_shift=0.0, manual_grid=None, n_expiries=3, rate=0.05,
        constraints={"max_legs": 4, "delta_threshold": None, "max_loss_cap": None},
        objective="net_pnl", top_n=5, as_of=entry_date,
    )

    # NOT "return on max_loss": check_bounded_risk's worst-case search is unreliable for
    # multi-expiry (calendar/diagonal) structures — the far leg is still alive and vol-
    # dependent at the near leg's expiry, so its "max loss" can come out implausibly small
    # regardless of precise=True/False, producing a nonsense ratio (verified empirically:
    # >9000% "return on risk" on a diagonal in testing). Comparing against the SAME capital
    # basis as the actual position (capital_invested) sidesteps that search entirely — "if
    # you'd put the same money into this instead" is also a more direct answer to "what
    # should I have done" than a max-loss ratio would be. Repriced at contract_size=100 to
    # match Portfolio's own per-contract convention (see module docstring) rather than
    # Strategy Builder's default FX-lot size, so the dollar P&L this produces is actually on
    # the same footing as capital_invested, not just a same-shaped ratio.
    from services.strategy_engine import price_combo
    capital = pos.get("capital_invested") or 0
    for c in optimal_candidates:
        try:
            precise = price_combo(
                c["legs"], chain_entry, surface_entry, surface_realized, horizon_days,
                r=0.05, contract_size=100, precise=True,
            )
            c["net_pnl"], c["max_gain"], c["max_loss"] = precise["net_pnl"], precise["max_gain"], precise["max_loss"]
            c["net_delta_now"] = precise["net_delta_now"]
            c["return_on_capital_pct"] = round(precise["net_pnl"] / capital * 100, 2) if capital else None
        except Exception:
            c["return_on_capital_pct"] = None

    return {
        "available": True,
        "underlying": underlying, "saxo_symbol": saxo_symbol,
        "entry_date": entry_date, "as_of": as_of_date, "horizon_days": horizon_days,
        "spot_entry": round(spot_entry, 6), "spot_realized": round(spot_realized, 6),
        "realized_spot_shock_pct": realized_spot_shock_pct,
        "iv_entry": round(iv_entry, 4), "iv_realized": round(iv_realized, 4), "realized_iv_shift": realized_iv_shift,
        "actual_return_pct": actual_return_pct,
        "optimal_candidates": optimal_candidates,
    }