"""
Retro Alpha market simulation engine.
"""

from dataclasses import dataclass, field
from typing import Dict, List

import numpy as np

ASSETS = ["cash", "fd", "gov_bonds", "nifty_50", "nifty_it", "real_estate", "crypto", "gold"]

REGIMES = [
    "bull_market", "bear_market", "market_crash", "recovery", "high_inflation",
    "rate_hike", "rate_cut", "election_year", "monsoon_shock", "fii_exit",
    "tech_boom", "real_estate_boom", "crypto_frenzy", "gold_rush", "stagnation"
]

# Annualized expected returns and volatilities (calibrated for simulation)
ASSET_PARAMS = {
    "cash":      {"mean": 0.00, "vol": 0.01},
    "fd":        {"mean": 0.065, "vol": 0.005},
    "gov_bonds": {"mean": 0.07, "vol": 0.06},
    "nifty_50":  {"mean": 0.12, "vol": 0.16},
    "nifty_it":  {"mean": 0.15, "vol": 0.28},
    "real_estate":{"mean": 0.10, "vol": 0.18},
    "crypto":    {"mean": 0.20, "vol": 0.65},
    "gold":      {"mean": 0.08, "vol": 0.14},
}

CORRELATION = 0.3


@dataclass
class GameState:
    month: int = 0
    year: int = 1
    prices: Dict[str, float] = field(default_factory=lambda: {a: 1.0 for a in ASSETS})
    portfolio: Dict[str, float] = field(default_factory=lambda: {a: 0.0 for a in ASSETS})
    cash_balance: float = 1_000_000.0
    news: Dict = field(default_factory=dict)
    agent_actions: List[Dict] = field(default_factory=list)
    ledger: List[Dict] = field(default_factory=list)
    game_over: bool = False
    won: bool = False

    def total_value(self) -> float:
        return self.cash_balance + sum(self.portfolio[a] * self.prices[a] for a in ASSETS)


def new_game(starting_cash: float = 1_000_000.0) -> GameState:
    state = GameState(cash_balance=starting_cash)
    state.portfolio = {a: 0.0 for a in ASSETS}
    return state


def price_shock(state: GameState, impact: Dict[str, float]):
    """Apply a news-driven price shock."""
    for asset in ASSETS:
        if asset in impact:
            state.prices[asset] *= (1 + impact[asset])


def random_walk(state: GameState):
    """Apply monthly random price drift correlated across assets."""
    n = len(ASSETS)
    corr_matrix = np.full((n, n), CORRELATION) + np.eye(n) * (1 - CORRELATION)
    shocks = np.random.multivariate_normal(np.zeros(n), corr_matrix)
    for i, asset in enumerate(ASSETS):
        params = ASSET_PARAMS[asset]
        monthly_mean = params["mean"] / 12
        monthly_vol = params["vol"] / np.sqrt(12)
        ret = monthly_mean + monthly_vol * shocks[i]
        state.prices[asset] *= (1 + ret)


def apply_agent_trades(state: GameState, agent_actions: List[Dict]):
    """Apply agent trades to prices via order-flow pressure."""
    pressure = {a: 0.0 for a in ASSETS}
    for action in agent_actions:
        for item in action.get("actions", []):
            asset = item["asset"]
            amt = item["amount_pct"] * (1 if item["action"] == "buy" else -1)
            pressure[asset] += amt
    for asset in ASSETS:
        # Agent flow moves price by up to 3%
        state.prices[asset] *= (1 + pressure[asset] * 0.03)


def execute_player_trade(state: GameState, asset: str, action: str, amount_pct: float):
    """Execute a player trade. amount_pct is relative to total portfolio value."""
    total = state.total_value()
    trade_value = total * amount_pct

    if action == "buy":
        if state.cash_balance < trade_value:
            trade_value = state.cash_balance
        shares = trade_value / state.prices[asset]
        state.cash_balance -= trade_value
        state.portfolio[asset] += shares
    elif action == "sell":
        current_value = state.portfolio[asset] * state.prices[asset]
        sell_value = min(trade_value, current_value)
        shares = sell_value / state.prices[asset]
        state.portfolio[asset] -= shares
        state.cash_balance += sell_value

    state.ledger.append({
        "month": state.month,
        "year": state.year,
        "asset": asset,
        "action": action,
        "amount_pct": amount_pct,
        "value": trade_value,
    })


def advance_month(state: GameState, news: Dict, agent_actions: List[Dict]):
    """Advance the simulation by one month."""
    state.month += 1
    if state.month > 12:
        state.month = 1
        state.year += 1

    state.news = news
    state.agent_actions = agent_actions

    if news.get("impact"):
        price_shock(state, news["impact"])

    apply_agent_trades(state, agent_actions)
    random_walk(state)

    if state.year > 10:
        state.game_over = True
        state.won = state.total_value() >= 1_000_000


def year_end_summary(state: GameState) -> Dict:
    """Compute year-end stats for the mentor."""
    year_ledger = [t for t in state.ledger if t["year"] == state.year]
    values = [state.total_value()]  # simplified
    returns = np.diff(values) / values[:-1] if len(values) > 1 else [0.0]
    sharpe = (np.mean(returns) / (np.std(returns) + 1e-9)) * np.sqrt(12)

    total = state.total_value()
    allocations = {}
    for asset in ASSETS:
        val = state.portfolio[asset] * state.prices[asset]
        allocations[asset] = round(val / total, 3) if total > 0 else 0.0

    return {
        "year": state.year,
        "starting_value": 1_000_000,
        "ending_value": total,
        "max_drawdown": -0.25,  # placeholder
        "sharpe_ratio": round(sharpe, 2),
        "allocations": allocations,
        "ledger": year_ledger,
    }