import asyncio
import dataclasses
import datetime as dt
from pathlib import Path
from unittest.mock import AsyncMock

import pandas as pd
import pytest

from ibkr_quant import cli
from ibkr_quant.broker import AccountState, Broker, Holding
from ibkr_quant.config import load_config
from ibkr_quant.rebalance import plan_orders


def test_closes_dropped_names_and_sells_before_buys():
    orders = plan_orders(
        target_weights={"AAPL": 0.5, "MSFT": 0.5},
        positions={"AAPL": 10, "NVDA": 7},
        prices={"AAPL": 100.0, "MSFT": 300.0, "NVDA": 50.0},
        investable=10_000,
        min_trade_value=200,
    )
    assert [(o.symbol, o.delta) for o in orders] == [("NVDA", -7), ("AAPL", 40), ("MSFT", 16)]


def test_skips_small_adjustments_but_not_full_exits():
    orders = plan_orders(
        target_weights={"AAPL": 1.0},
        positions={"AAPL": 99, "KO": 1},
        prices={"AAPL": 100.0, "KO": 60.0},
        investable=10_000,
        min_trade_value=200,
    )
    assert [(o.symbol, o.delta) for o in orders] == [("KO", -1)]


def test_refuses_to_plan_without_prices():
    with pytest.raises(ValueError, match="NVDA"):
        plan_orders({"AAPL": 1.0}, {"NVDA": 5}, {"AAPL": 100.0}, 10_000, 0)


def test_resize_band_keeps_near_target_holdings():
    orders = plan_orders(
        target_weights={"AAPL": 0.5, "MSFT": 0.5},
        positions={"AAPL": 46, "MSFT": 10},
        prices={"AAPL": 100.0, "MSFT": 300.0},
        investable=10_000,
        min_trade_value=50,
        resize_band=0.10,
    )
    # AAPL is 8% under target and stays; MSFT is 40% under and tops up.
    assert [(o.symbol, o.delta) for o in orders] == [("MSFT", 6)]


def test_rebalance_sells_a_holding_that_left_the_index_and_skips_unmanaged(monkeypatch, tmp_path):
    cfg = load_config(Path(__file__).resolve().parents[1] / "config.toml")
    cfg = dataclasses.replace(cfg, root=tmp_path, strategy=dataclasses.replace(cfg.strategy, top_n=2),
                              execution=dataclasses.replace(cfg.execution, unmanaged=["XYZ"]))
    idx = pd.bdate_range(end=dt.date(2026, 10, 13), periods=300)
    panel = pd.DataFrame(100.0, index=idx, columns=["AAA", "BBB", "CCC"])
    monkeypatch.setattr(cli, "load_universe", lambda _: list(panel.columns))
    monkeypatch.setattr(cli, "load_bars", lambda *_: (panel, panel))
    monkeypatch.setattr(cli, "sessions_until_rebalance", lambda *_: 0)
    monkeypatch.setattr(cli, "signal_overnight_recovery",
                        lambda o, c: pd.DataFrame({"AAA": 0.9, "BBB": 0.8, "CCC": 0.1}, index=o.index))
    holdings = [Holding("AAA", "STK", "USD", 44, 100.0, 4_400.0),
                Holding("OLD", "STK", "USD", 10, 30.0, 300.0),     # left the index: no bars
                Holding("XYZ", "STK", "USD", 10, 100.0, 1_000.0)]  # unmanaged
    broker = Broker(cfg.ib)
    monkeypatch.setattr(Broker, "__aenter__", AsyncMock(return_value=broker))
    monkeypatch.setattr(Broker, "__aexit__", AsyncMock(return_value=None))
    monkeypatch.setattr(cli, "Broker", lambda _: broker)
    monkeypatch.setattr(broker, "account_state", AsyncMock(return_value=AccountState("DU123", 10_000, 4_300, holdings)))

    asyncio.run(cli.rebalance(cfg, submit=False))

    plan = pd.read_csv(next((tmp_path / "runs").glob("*/plan.csv")))
    # investable = 10,000 * 0.98 - 1,000 (XYZ) = 8,800: 4,400 per name, so AAA is on target.
    assert list(zip(plan.symbol, plan.delta, plan.price)) == [("OLD", -10, 30.0), ("BBB", 44, 100.0)]