import pytest
from kalshi_csv import KalshiCSV


def test_parse_returns_self(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    result = kalshi.parse()
    assert result is kalshi


def test_trade_count(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert kalshi.summary["trade_count"] == 3


def test_total_fees(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert abs(kalshi.summary["total_fees"] - 0.07) < 1e-6


def test_total_pnl_without_fees(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert abs(kalshi.summary["total_pnl_without_fees"] - (-0.20)) < 1e-6


def test_total_pnl_with_fees(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert abs(kalshi.summary["total_pnl_with_fees"] - (-0.27)) < 1e-6


def test_total_tax_basis(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert abs(kalshi.summary["total_tax_basis"] - 1.64) < 1e-6


def test_total_tax_proceeds(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert abs(kalshi.summary["total_tax_proceeds"] - 1.37) < 1e-6


def test_trades_list_length(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert len(kalshi.trades) == 3


def test_first_trade_data(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    trade = kalshi.trades[0]
    assert trade["ticker"] == "TESTMARKET-WIN"
    assert trade["side"] == "YES"
    assert trade["qty"] == 1.0
    assert trade["entry"] == 0.50
    assert trade["exit"] == 1.00
    assert abs(trade["pnl_no_fees"] - 0.50) < 1e-6
    assert abs(trade["pnl_with_fees"] - 0.47) < 1e-6


def test_irs_summary(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    irs = kalshi.irs_summary()
    assert irs["box"] == "C"
    assert irs["description"] == "Kalshi Event Contracts (Aggregate Summary)"
    assert irs["date_acquired"] == "07/07/2026"
    assert irs["date_sold"] == "07/07/2026"
    assert abs(irs["gross_proceeds"] - 1.37) < 1e-6
    assert abs(irs["cost_basis"] - 1.64) < 1e-6
    assert abs(irs["gain_or_loss"] - (-0.27)) < 1e-6


def test_date_tracking(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert kalshi.summary["earliest_open_date"] is not None
    assert kalshi.summary["latest_close_date"] is not None
    assert kalshi.summary["earliest_open_date"].strftime("%m/%d/%Y") == "07/07/2026"
    assert kalshi.summary["latest_close_date"].strftime("%m/%d/%Y") == "07/07/2026"


def test_file_not_found():
    kalshi = KalshiCSV("/nonexistent/path.csv")
    with pytest.raises(FileNotFoundError):
        kalshi.parse()


def test_market_breakdown_returns_list(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    breakdown = kalshi.market_breakdown()
    assert isinstance(breakdown, list)
    assert len(breakdown) > 0


def test_market_breakdown_structure(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    breakdown = kalshi.market_breakdown()
    for item in breakdown:
        assert "category" in item
        assert "trades" in item
        assert "win_rate" in item
        assert "net_pnl" in item


def test_market_breakdown_sorted_by_trades(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    breakdown = kalshi.market_breakdown()
    trade_counts = [item["trades"] for item in breakdown]
    assert trade_counts == sorted(trade_counts, reverse=True)


def test_sp500_price_tier_breakdown_groups_entry_prices_and_excludes_other_markets():
    kalshi = KalshiCSV("unused.csv")
    entries_and_pnls = [
        (0.399, 1.0),
        (0.40, -0.5),
        (0.4001, -0.25),
        (0.69, 0.0),
        (0.70, 2.0),
        (0.7001, -1.0),
        (0.85, 3.0),
        (0.8501, -2.0),
        (1.00, 4.0),
        (-0.01, 100.0),
        (1.01, 100.0),
    ]
    kalshi.trades = [
        {
            "entry": entry,
            "pnl_with_fees": pnl,
            "market_category": "S&P 500 (INXU Intraday)",
        }
        for entry, pnl in entries_and_pnls
    ]
    kalshi.trades.append({
        "entry": 0.50,
        "pnl_with_fees": 100.0,
        "market_category": "Other Markets",
    })

    breakdown = kalshi.sp500_price_tier_breakdown()

    assert [item["price_tier"] for item in breakdown] == [
        "<=$0.40 (Out-of-the-Money Speculative)",
        ">$0.40-$0.70 (At-the-Money / Coincident)",
        ">$0.70-$0.85 (Likely / Moderate ITM)",
        ">$0.85 (Deep ITM / High Probability)",
    ]
    assert [item["trades"] for item in breakdown] == [2, 3, 2, 2]
    assert [item["win_rate"] for item in breakdown] == pytest.approx([50.0, 100 / 3, 50.0, 50.0])
    assert [item["average_win"] for item in breakdown] == [1.0, 2.0, 3.0, 4.0]
    assert [item["average_loss"] for item in breakdown] == [-0.5, -0.25, -1.0, -2.0]
    assert [item["net_pnl"] for item in breakdown] == [0.5, 1.75, 2.0, 2.0]


def test_sp500_price_tier_breakdown_is_empty_without_sp500_trades():
    kalshi = KalshiCSV("unused.csv")
    kalshi.trades = [{
        "entry": 0.50,
        "pnl_with_fees": 1.0,
        "market_category": "Other Markets",
    }]

    assert kalshi.sp500_price_tier_breakdown() == []


def test_recent_closed_positions_returns_list(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    positions = kalshi.recent_closed_positions()
    assert isinstance(positions, list)


def test_recent_closed_positions_limit(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    positions = kalshi.recent_closed_positions(n=2)
    assert len(positions) <= 2


def test_recent_closed_positions_sorted_by_date(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    positions = kalshi.recent_closed_positions()
    if len(positions) > 1:
        timestamps = [p["close_timestamp"] for p in positions]
        assert timestamps == sorted(timestamps, reverse=True)


def test_summary_tracks_wins_losses_pushes(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert kalshi.summary["wins"] >= 0
    assert kalshi.summary["losses"] >= 0
    assert kalshi.summary["pushes"] >= 0
    assert kalshi.summary["wins"] + kalshi.summary["losses"] + kalshi.summary["pushes"] == kalshi.summary["trade_count"]


def test_summary_tracks_best_worst_trade(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    assert kalshi.summary["best_trade"] is not None
    assert kalshi.summary["worst_trade"] is not None
    assert "pnl_with_fees" in kalshi.summary["best_trade"]
    assert "pnl_with_fees" in kalshi.summary["worst_trade"]
    assert kalshi.summary["best_trade"]["pnl_with_fees"] >= kalshi.summary["worst_trade"]["pnl_with_fees"]


def test_trade_has_market_category(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    for trade in kalshi.trades:
        assert "market_category" in trade
        assert isinstance(trade["market_category"], str)


def test_trade_has_timestamps(sample_csv):
    kalshi = KalshiCSV(sample_csv)
    kalshi.parse()
    for trade in kalshi.trades:
        assert "open_timestamp" in trade
        assert "close_timestamp" in trade