"""Tests for scripts.manual_log — derivation logic + vision_schema compatibility."""

from __future__ import annotations

import pytest

from scripts.manual_log import build_extraction, OUTCOME_TO_BE_MOVED, OUTCOME_TO_MAX_REACHED, ro_to_utc
from scripts.vision_schema import M2DExtraction


def test_buy_happy_path():
    d = build_extraction(
        data="2026-05-13",
        ora_ro="17:33",
        directie="Buy",
        entry=497.42,
        sl=496.80,
        outcome_path="TP0→TP1",
    )
    # Satisfies pydantic
    M2DExtraction.model_validate(d)
    assert d["directie"] == "Buy"
    assert d["entry"] == 497.42
    assert d["sl"] == 496.80
    # tp0 = entry + 0.4 * 0.62 = 497.42 + 0.248 = 497.668
    assert abs(d["tp0"] - 497.668) < 0.01
    assert abs(d["tp1"] - 497.792) < 0.01
    assert abs(d["tp2"] - 498.04) < 0.01
    assert d["max_reached"] == "TP1"
    assert d["be_moved"] is True


def test_sell_happy_path():
    d = build_extraction(
        data="2026-05-13",
        ora_ro="17:33",
        directie="Sell",
        entry=492.47,
        sl=492.77,
        outcome_path="TP0→TP1",
    )
    M2DExtraction.model_validate(d)
    assert d["directie"] == "Sell"
    # For Sell: tp0 = entry - 0.4 * 0.30 = 492.47 - 0.12 = 492.35
    assert abs(d["tp0"] - 492.35) < 0.01
    assert abs(d["tp1"] - 492.29) < 0.01
    assert abs(d["tp2"] - 492.17) < 0.01
    assert d["sl"] > d["entry"] > d["tp0"] > d["tp1"] > d["tp2"]


def test_ora_utc_dst_summer():
    # May = EEST = UTC+3
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Sell",
        entry=492.47, sl=492.77, outcome_path="pending",
    )
    assert d["ora_utc"] == "14:33"


def test_ora_utc_dst_winter():
    # January = EET = UTC+2
    d = build_extraction(
        data="2026-01-15", ora_ro="17:00", directie="Buy",
        entry=400, sl=399, outcome_path="pending",
    )
    assert d["ora_utc"] == "15:00"


def test_outcome_sl_max_reached_consistent():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Buy",
        entry=400, sl=399, outcome_path="SL",
    )
    M2DExtraction.model_validate(d)
    assert d["max_reached"] == "SL_first"
    assert d["be_moved"] is False


def test_outcome_tp0_pending_max_reached_tp0():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Buy",
        entry=400, sl=399, outcome_path="TP0→pending",
    )
    M2DExtraction.model_validate(d)
    assert d["max_reached"] == "TP0"
    assert d["be_moved"] is True


def test_outcome_tp0_sl_be_moved_true():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Buy",
        entry=400, sl=399, outcome_path="TP0→SL",
    )
    M2DExtraction.model_validate(d)
    assert d["max_reached"] == "TP0"
    assert d["be_moved"] is True  # rule-enforced


def test_explicit_max_reached_override():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Buy",
        entry=400, sl=399, outcome_path="pending",
        max_reached="TP0",
    )
    M2DExtraction.model_validate(d)
    assert d["max_reached"] == "TP0"


def test_screenshot_file_generated():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Sell",
        entry=492.47, sl=492.77, outcome_path="pending",
        instrument="DIA",
    )
    assert d["screenshot_file"] == "2026-05-13-dia-1733.png"


def test_screenshot_file_explicit():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Sell",
        entry=492.47, sl=492.77, outcome_path="pending",
        screenshot_file="custom.png",
    )
    assert d["screenshot_file"] == "custom.png"


def test_risc_pct_computed():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Sell",
        entry=500.00, sl=501.00, outcome_path="pending",
    )
    assert abs(d["risc_pct"] - 0.20) < 0.001  # 1/500 = 0.002 = 0.20%


def test_entry_equals_sl_raises():
    with pytest.raises(ValueError, match="zero risk"):
        build_extraction(
            data="2026-05-13", ora_ro="17:33", directie="Buy",
            entry=500, sl=500, outcome_path="pending",
        )


def test_buy_inverted_ordering_raises():
    with pytest.raises(ValueError, match="Buy"):
        build_extraction(
            data="2026-05-13", ora_ro="17:33", directie="Buy",
            entry=400, sl=401, outcome_path="pending",  # sl > entry for Buy
        )


def test_sell_inverted_ordering_raises():
    with pytest.raises(ValueError, match="Sell"):
        build_extraction(
            data="2026-05-13", ora_ro="17:33", directie="Sell",
            entry=400, sl=399, outcome_path="pending",  # sl < entry for Sell
        )


def test_optional_fields_defaults():
    d = build_extraction(
        data="2026-05-13", ora_ro="17:33", directie="Buy",
        entry=400, sl=399, outcome_path="pending",
    )
    assert d["instrument"] == "DIA"
    assert d["tf_mare"] == "5min"
    assert d["tf_mic"] == "1min"
    assert d["calitate"] == "n/a"
    assert d["confidence"] == "high"
    assert d["ambiguities"] == []
    assert d["note"] == ""


def test_ro_to_utc_helper():
    assert ro_to_utc("2026-05-13", "17:33") == "14:33"
    assert ro_to_utc("2026-01-15", "10:00") == "08:00"