# Feature: dual-pipeline-signal-engine, Property: Confluence score monotonicity
"""Property-based tests for the Multi-Timeframe Confluence Engine.

Feature: dual-pipeline-signal-engine

Tests the confluence score monotonicity property from the design specification:
activating a signal on an additional timeframe with non-zero weight always
increases or maintains the confluence score.

Requirements: 3.6, 17.5
"""
from __future__ import annotations

from hypothesis import given, settings
from hypothesis import strategies as st

from services.signal_engine.confluence import compute_confluence
from services.signal_engine.models import SignalDirection, SignalResult

# ---------------------------------------------------------------------------
# Property: Confluence score monotonicity
# Validates: Requirements 3.6, 17.5
# ---------------------------------------------------------------------------

# Default timeframe weights per the design specification
DEFAULT_WEIGHTS: dict[str, float] = {
    "M30": 0.03,
    "H1": 0.07,
    "H4": 0.15,
    "D": 0.30,
    "W": 0.30,
    "M": 0.15,
}

ALL_TIMEFRAMES = list(DEFAULT_WEIGHTS.keys())
ANCHOR_TIMEFRAMES = ["D", "W", "M"]
NON_ANCHOR_TIMEFRAMES = ["M30", "H1", "H4"]

# ---------------------------------------------------------------------------
# Hypothesis strategies
# ---------------------------------------------------------------------------

_direction = st.sampled_from([SignalDirection.BULLISH, SignalDirection.BEARISH])

_nonzero_strength = st.floats(
    min_value=0.01, max_value=1.0, allow_nan=False, allow_infinity=False,
)

_confidence = st.floats(
    min_value=0.0, max_value=1.0, allow_nan=False, allow_infinity=False,
)

_signal_type = st.just("test_signal")


def _signal_result(
    timeframe: str,
    strength: st.SearchStrategy[float] = _nonzero_strength,
) -> st.SearchStrategy[SignalResult]:
    """Build a SignalResult for a given timeframe with non-zero strength."""
    return st.builds(
        SignalResult,
        signal_type=_signal_type,
        timeframe=st.just(timeframe),
        strength=strength,
        direction=_direction,
        confidence=_confidence,
    )


@st.composite
def _base_and_extra_timeframe(draw: st.DrawFn) -> tuple[dict[str, SignalResult], str]:
    """Generate a base set of signal results that passes confluence, plus one extra timeframe.

    The base set has at least 2 timeframes including at least one D/W/M anchor.
    The extra timeframe is not in the base set and has a non-zero weight.
    """
    # Pick 1 anchor timeframe (guaranteed)
    anchor = draw(st.sampled_from(ANCHOR_TIMEFRAMES))

    # Pick 1-4 additional timeframes from the remaining (to get at least 2 total)
    remaining = [tf for tf in ALL_TIMEFRAMES if tf != anchor]
    additional_count = draw(st.integers(min_value=1, max_value=min(4, len(remaining))))
    additional = draw(
        st.lists(
            st.sampled_from(remaining),
            min_size=additional_count,
            max_size=additional_count,
            unique=True,
        )
    )

    base_tfs = [anchor] + additional

    # Build signal results for the base set
    base_results: dict[str, SignalResult] = {}
    for tf in base_tfs:
        base_results[tf] = draw(_signal_result(tf))

    # Pick an extra timeframe NOT in the base set
    unused = [tf for tf in ALL_TIMEFRAMES if tf not in base_tfs]
    if not unused:
        # All 6 timeframes used — remove one non-anchor from base to free it up
        removable = [tf for tf in base_tfs if tf not in ANCHOR_TIMEFRAMES]
        if not removable:
            # All are anchors — remove one that isn't the primary anchor
            removable = [tf for tf in base_tfs if tf != anchor]
        to_remove = draw(st.sampled_from(removable))
        del base_results[to_remove]
        unused = [to_remove]

    extra_tf = draw(st.sampled_from(unused))

    return base_results, extra_tf


# ---------------------------------------------------------------------------
# Property test
# ---------------------------------------------------------------------------


@given(data=st.data(), base_and_extra=_base_and_extra_timeframe())
@settings(max_examples=100)
def test_confluence_score_monotonicity(
    data: st.DataObject,
    base_and_extra: tuple[dict[str, SignalResult], str],
) -> None:
    """**Validates: Requirements 3.6, 17.5**

    Given a signal that already passes confluence (≥2 timeframes, ≥1 D/W/M
    anchor), adding an additional timeframe with non-zero strength and
    non-zero weight SHALL always increase or maintain the confluence score.

    The weighted confluence score is C = Σ(w_tf · s_tf). Since both w_tf > 0
    and s_tf > 0 for the added timeframe, the new term is strictly positive,
    so the score must increase.
    """
    base_results, extra_tf = base_and_extra

    signal_type = "test_signal"

    # Compute confluence for the base set
    base_input = {signal_type: dict(base_results)}
    base_confluence = compute_confluence(base_input, DEFAULT_WEIGHTS)

    # The base set should pass confluence (≥2 TFs, ≥1 anchor)
    assert len(base_confluence) == 1, (
        f"Expected base set to pass confluence but got {len(base_confluence)} signals.\n"
        f"  Base timeframes: {list(base_results.keys())}"
    )
    base_score = base_confluence[0].confluence_score

    # Add the extra timeframe with non-zero strength
    extra_result = data.draw(_signal_result(extra_tf))
    extended_results = dict(base_results)
    extended_results[extra_tf] = extra_result

    # Compute confluence for the extended set
    extended_input = {signal_type: extended_results}
    extended_confluence = compute_confluence(extended_input, DEFAULT_WEIGHTS)

    # The extended set must also pass confluence (superset of a passing set)
    assert len(extended_confluence) == 1, (
        f"Expected extended set to pass confluence but got "
        f"{len(extended_confluence)} signals.\n"
        f"  Extended timeframes: {list(extended_results.keys())}"
    )
    new_score = extended_confluence[0].confluence_score

    # Monotonicity: new_score >= base_score
    assert new_score >= base_score, (
        f"Confluence score decreased when adding timeframe {extra_tf}!\n"
        f"  Base score:     {base_score:.6f} (timeframes: {list(base_results.keys())})\n"
        f"  Extended score: {new_score:.6f} (timeframes: {list(extended_results.keys())})\n"
        f"  Added TF weight: {DEFAULT_WEIGHTS[extra_tf]}, "
        f"strength: {extra_result.strength:.6f}"
    )