from typing import Any, Dict, List, Optional

from fastapi import APIRouter, HTTPException, Query
from pydantic import BaseModel

from services.option_chain import get_chain_slice
from services.vol_surface import build_surface, apply_scenario
from services.strategy_engine import payoff_curves, DEFAULT_CONTRACT_SIZE
from services.strategy_optimizer import optimize as run_optimizer
from services.database import (
    save_scenario, get_scenarios, delete_scenario,
    save_strategy, get_saved_strategies, delete_saved_strategy,
)

router = APIRouter(prefix="/api/strategy-builder", tags=["strategy-builder"])


class LegIn(BaseModel):
    expiry_date: str
    days_to_expiry: int
    strike: float
    option_type: str  # "call" | "put"
    position: str  # "long" | "short"
    quantity: int = 1


class ScenarioIn(BaseModel):
    symbol: str
    horizon_days: int = 8  # scenario P&L evaluation date — NOT the expiry filter, see dte_min/dte_max
    spot_shock_pct: float = 0.0
    iv_level_shift: float = 0.0  # parallel IV shift — applies to every strike/expiry uniformly
    skew_tilt: float = 0.0
    term_slope_shift: float = 0.0  # term-structure slope, per 30 days (0 at days=0)
    rate_shock_bps: float = 0.0
    manual_grid: Optional[List[Dict[str, Any]]] = None
    rate: float = 0.05
    n_expiries: int = 3
    contract_size: float = DEFAULT_CONTRACT_SIZE
    # Which expiries the chain/optimizer may pick legs from — independent of horizon_days,
    # so a short-horizon scenario (e.g. 8 days) can still be evaluated with longer-dated
    # options (e.g. dte_min=20, dte_max=60) instead of horizon_days doing double duty.
    dte_min: Optional[int] = None
    dte_max: Optional[int] = None

    @property
    def shocked_rate(self) -> float:
        return self.rate + self.rate_shock_bps / 10000.0


class PriceRequest(BaseModel):
    scenario: ScenarioIn
    legs: List[LegIn]


class ConstraintsIn(BaseModel):
    max_legs: int = 4
    delta_threshold: Optional[float] = 0.15
    max_loss_cap: Optional[float] = None
    objective: str = "net_pnl"  # "net_pnl" | "return_on_risk" | "prob_weighted"
    top_n: int = 20


class GreekTargetIn(BaseModel):
    """One Greek's desired behavior — deliberately NOT a numeric slider (see project memory,
    Strategy Builder Greeks plan): a qualitative state the optimizer resolves against the
    actual candidate pool, so "strongly positive" means "top of what's achievable for this
    instrument/scenario right now" rather than a guessed absolute number."""
    state: str = "free"  # "strong_negative"|"negative"|"neutral"|"positive"|"strong_positive"|"free"
    tolerance: str = "normale"  # "etroite"|"normale"|"large" — etroite hard-filters sign mismatches
    weight: float = 50.0  # 0-100, importance relative to the base objective (net_pnl/return_on_risk/...)


class GreekProfileIn(BaseModel):
    """Layer B of the scenario/profile/constraints split: the behavior the user wants,
    kept separate from the scenario (Layer A, what's anticipated) and from ConstraintsIn
    (Layer C, hard construction limits)."""
    delta: GreekTargetIn = GreekTargetIn()
    gamma: GreekTargetIn = GreekTargetIn()
    theta: GreekTargetIn = GreekTargetIn()
    vega: GreekTargetIn = GreekTargetIn()
    rho: GreekTargetIn = GreekTargetIn()


class OptimizeRequest(BaseModel):
    scenario: ScenarioIn
    constraints: ConstraintsIn
    greek_profile: Optional[GreekProfileIn] = None


class ScenarioSaveRequest(BaseModel):
    symbol: str
    label: Optional[str] = ""
    horizon_days: int
    spot_shock_pct: float
    iv_level_shift: float
    skew_tilt: float
    term_slope_shift: float
    rate_shock_bps: float = 0.0
    dte_min: Optional[int] = None
    dte_max: Optional[int] = None
    manual_grid: Optional[List[Dict[str, Any]]] = None


class StrategySaveRequest(BaseModel):
    scenario_id: Optional[str] = None
    symbol: str
    template_name: Optional[str] = ""
    objective: Optional[str] = ""
    legs: List[LegIn]
    entry_cost: Optional[float] = None
    max_gain: Optional[float] = None
    max_loss: Optional[float] = None
    net_pnl_scenario: Optional[float] = None
    net_delta: Optional[float] = None
    notes: Optional[str] = ""


def _build_surfaces(scenario: ScenarioIn):
    chain_slice = get_chain_slice(
        scenario.symbol, scenario.horizon_days, scenario.n_expiries,
        dte_min=scenario.dte_min, dte_max=scenario.dte_max,
    )
    surface_now = build_surface(chain_slice)
    surface_scenario = apply_scenario(
        surface_now,
        spot_shock_pct=scenario.spot_shock_pct,
        iv_level_shift=scenario.iv_level_shift,
        skew_tilt=scenario.skew_tilt,
        term_slope_shift=scenario.term_slope_shift,
        manual_grid=scenario.manual_grid,
    )
    return chain_slice, surface_now, surface_scenario


@router.get("/chain")
def chain(
    symbol: str = Query(...),
    horizon_days: int = Query(8),
    n_expiries: int = Query(3),
    dte_min: Optional[int] = Query(None),
    dte_max: Optional[int] = Query(None),
    as_of: Optional[str] = Query(None, description="Reconstruct the chain as it stood at/before this date instead of now — e.g. to build legs against the same chain a past Replay window will walk, rather than today's."),
):
    try:
        return get_chain_slice(symbol, horizon_days, n_expiries, dte_min=dte_min, dte_max=dte_max, as_of=as_of)
    except ValueError as e:
        raise HTTPException(status_code=404, detail=str(e))


@router.get("/presets")
def presets(
    symbol: str = Query(...),
    horizon_days: int = Query(8),
    dte_min: Optional[int] = Query(None),
    dte_max: Optional[int] = Query(None),
    as_of: Optional[str] = Query(None),
):
    """The full strategy catalog (services.backtest_strategies.STRATEGIES) built from the
    REAL chain instead of Backtest's synthetic grid — so a preset click here seeds the leg
    editor with actually-quoted strikes/expiries, ready to price or replay as-is.
    n_expiries=20 (vs. Strategy Builder's own default of 3) so calendar/diagonal presets,
    which need two distinct expiries, reliably have a second one to draw from, and so a
    wide dte_min/dte_max window (e.g. hunting for a ~30d expiry) isn't silently narrowed
    back down to whatever's nearest horizon_days."""
    from services.backtest_strategies import STRATEGIES, build_legs
    try:
        chain_slice = get_chain_slice(symbol, horizon_days, 20, dte_min=dte_min, dte_max=dte_max, as_of=as_of)
    except ValueError as e:
        raise HTTPException(status_code=404, detail=str(e))

    expiries = chain_slice["expiries"]
    if not expiries:
        raise HTTPException(status_code=404, detail=f"Aucune échéance exploitable pour '{symbol}'.")
    near, far = expiries[0], expiries[1] if len(expiries) > 1 else None

    out = []
    for key, label, n_legs in STRATEGIES:
        legs = build_legs(key, chain_slice["spot"], 0.05, near, far)
        if not legs:
            continue  # e.g. calendar/diagonal with only one real expiry available right now
        out.append({"key": key, "label": label, "n_legs": n_legs, "legs": legs})
    return out


@router.post("/price")
def price(req: PriceRequest):
    if not req.legs:
        raise HTTPException(status_code=400, detail="Au moins une jambe est requise")
    if len(req.legs) > 4:
        raise HTTPException(status_code=400, detail="4 jambes maximum")

    try:
        chain_slice, surface_now, surface_scenario = _build_surfaces(req.scenario)
    except ValueError as e:
        raise HTTPException(status_code=404, detail=str(e))

    legs = [leg.model_dump() for leg in req.legs]
    result = payoff_curves(
        legs, chain_slice, surface_now, surface_scenario,
        req.scenario.horizon_days, req.scenario.shocked_rate,
        contract_size=req.scenario.contract_size,
    )
    result["spot"] = chain_slice["spot"]
    result["scenario_spot"] = surface_scenario.spot
    result["proxy"] = chain_slice["proxy"]
    return result


@router.post("/suggested-profile")
def suggested_profile(scenario: ScenarioIn):
    """Mode 1 of the scenario/profile/constraints split: what Greek behavior this scenario
    already implies on its own, before the user sets any explicit target — see
    services.scenario_profile.infer_natural_greek_profile."""
    from services.scenario_profile import infer_natural_greek_profile
    return infer_natural_greek_profile(scenario.spot_shock_pct, scenario.iv_level_shift, scenario.horizon_days)


class ReplayRequest(BaseModel):
    symbol: str
    legs: List[LegIn]
    start_date: str
    end_date: str
    contract_size: float = DEFAULT_CONTRACT_SIZE


@router.post("/replay")
def replay(req: ReplayRequest):
    """Day-by-day mark-to-market of these exact legs against REAL accumulated Saxo
    history between two dates — not a scenario, a replay of what actually happened.
    See services.strategy_replay for why it's a distinct thing from /price's scenario
    pricing (which prices a hypothetical spot/IV shock, not real historical quotes)."""
    from services.strategy_replay import replay_position
    try:
        return replay_position(
            req.symbol, [leg.dict() for leg in req.legs], req.start_date, req.end_date,
            contract_size=req.contract_size,
        )
    except ValueError as e:
        raise HTTPException(status_code=404, detail=str(e))


@router.post("/optimize")
def optimize(req: OptimizeRequest):
    if req.constraints.max_legs > 4:
        raise HTTPException(status_code=400, detail="4 jambes maximum")
    from services.scenario_profile import detect_greek_contradictions
    warnings = detect_greek_contradictions(
        req.greek_profile.model_dump() if req.greek_profile else None,
        req.scenario.n_expiries, req.scenario.dte_min, req.scenario.dte_max,
    )
    try:
        results = run_optimizer(
            symbol=req.scenario.symbol,
            horizon_days=req.scenario.horizon_days,
            spot_shock_pct=req.scenario.spot_shock_pct,
            iv_level_shift=req.scenario.iv_level_shift,
            skew_tilt=req.scenario.skew_tilt,
            term_slope_shift=req.scenario.term_slope_shift,
            manual_grid=req.scenario.manual_grid,
            n_expiries=req.scenario.n_expiries,
            rate=req.scenario.rate,
            rate_shock_bps=req.scenario.rate_shock_bps,
            dte_min=req.scenario.dte_min,
            dte_max=req.scenario.dte_max,
            constraints=req.constraints.model_dump(),
            objective=req.constraints.objective,
            top_n=req.constraints.top_n,
            contract_size=req.scenario.contract_size,
            greek_profile=req.greek_profile.model_dump() if req.greek_profile else None,
        )
    except Exception as e:
        import traceback
        from services.database import log_system_event
        tb = traceback.format_exc()
        log_system_event(
            level="ERROR", source="strategy_optimizer",
            message=f"Optimize failed for {req.scenario.symbol}: {e}",
            ticker=req.scenario.symbol, details={"error": str(e), "traceback": tb},
        )
        status = 404 if isinstance(e, ValueError) else 500
        raise HTTPException(status_code=status, detail=f"{e}")
    return {"candidates": results, "warnings": warnings}


@router.post("/scenarios")
def create_scenario(req: ScenarioSaveRequest):
    scenario_id = save_scenario(req.model_dump())
    return {"id": scenario_id}


@router.get("/scenarios")
def list_scenarios(symbol: Optional[str] = Query(None)):
    return get_scenarios(symbol)


@router.delete("/scenarios/{scenario_id}")
def remove_scenario(scenario_id: str):
    if not delete_scenario(scenario_id):
        raise HTTPException(status_code=404, detail="Scénario non trouvé")
    return {"deleted": True}


@router.post("/saved")
def create_saved_strategy(req: StrategySaveRequest):
    payload = req.model_dump()
    payload["legs"] = [leg for leg in payload["legs"]]
    strategy_id = save_strategy(payload)
    return {"id": strategy_id}


@router.get("/saved")
def list_saved_strategies(symbol: Optional[str] = Query(None)):
    return get_saved_strategies(symbol)


@router.delete("/saved/{strategy_id}")
def remove_saved_strategy(strategy_id: str):
    if not delete_saved_strategy(strategy_id):
        raise HTTPException(status_code=404, detail="Stratégie non trouvée")
    return {"deleted": True}