ai-trade/signal_v2/tests/test_signal_generator.py

"""Tests for signal_generator."""
from unittest.mock import MagicMock

import pytest

from signal_v2.signal_generator import generate_signals
from signal_v2.state import PollState


def _settings(**overrides):
    """Build a Settings-like object for tests (avoid env)."""
    defaults = dict(
        stop_loss_pct=-0.07,
        take_profit_pct=0.15,
        chronos_spread_threshold=0.6,
        asking_bid_ratio_threshold=0.6,
        confidence_threshold=0.7,
        min_momentum_for_buy="strong_up",
    )
    defaults.update(overrides)
    m = MagicMock()
    for k, v in defaults.items():
        setattr(m, k, v)
    return m


def _make_state_with_buy_candidate(
    ticker="005930", name="삼성전자",
    chronos_median=0.02, chronos_q10=-0.01, chronos_q90=0.04, chronos_conf=0.85,
    momentum="strong_up", bid_ratio=0.7, current_price=78500,
):
    state = PollState()
    state.screener_preview = {"items": [{"ticker": ticker, "name": name}]}
    state.chronos_predictions[ticker] = {
        "median": chronos_median, "q10": chronos_q10, "q90": chronos_q90,
        "conf": chronos_conf, "as_of": "2026-05-17T16:00:00+09:00",
    }
    state.minute_momentum[ticker] = momentum
    state.asking_price[ticker] = {
        "bid_total": int(bid_ratio * 1000),
        "ask_total": int((1 - bid_ratio) * 1000),
        "bid_ratio": bid_ratio,
        "current_price": current_price,
        "as_of": "2026-05-17T16:00:01+09:00",
    }
    return state


def _make_state_with_holding(
    ticker="005930", name="삼성전자",
    pnl_pct=0.0, avg_price=75000, current_price=75000,
):
    state = PollState()
    state.portfolio = {"holdings": [{
        "ticker": ticker, "name": name,
        "avg_price": avg_price, "current_price": current_price,
        "pnl_pct": pnl_pct, "profit_rate": pnl_pct * 100,
        "quantity": 100, "broker": "키움",
    }]}
    state.screener_preview = {"items": []}
    return state


@pytest.fixture
def dedup_mock():
    d = MagicMock()
    d.is_recent.return_value = False
    return d


def test_buy_signal_when_all_conditions_pass_and_confidence_high(dedup_mock):
    state = _make_state_with_buy_candidate()
    generate_signals(state, dedup_mock, _settings())
    assert "005930" in state.signals
    sig = state.signals["005930"]
    assert sig["action"] == "buy"
    assert sig["confidence_webai"] > 0.7
    dedup_mock.record.assert_called()


def test_silent_when_chronos_median_negative(dedup_mock):
    state = _make_state_with_buy_candidate(chronos_median=-0.01)
    generate_signals(state, dedup_mock, _settings())
    assert "005930" not in state.signals


def test_silent_when_distribution_spread_too_wide(dedup_mock):
    # spread = q90 - q10 = 0.5 - (-0.5) = 1.0 > 0.6 → hard gate fails
    state = _make_state_with_buy_candidate(
        chronos_median=0.001, chronos_q10=-0.5, chronos_q90=0.5,
    )
    generate_signals(state, dedup_mock, _settings())
    assert "005930" not in state.signals


def test_silent_when_momentum_not_strong_up(dedup_mock):
    state = _make_state_with_buy_candidate(momentum="weak_up")
    generate_signals(state, dedup_mock, _settings())
    assert "005930" not in state.signals


def test_silent_when_bid_ratio_below_threshold(dedup_mock):
    state = _make_state_with_buy_candidate(bid_ratio=0.5)
    generate_signals(state, dedup_mock, _settings())
    assert "005930" not in state.signals


def test_silent_when_confidence_below_threshold(dedup_mock):
    # chronos_conf low + rank=20 → confidence < 0.7
    state = _make_state_with_buy_candidate(chronos_conf=0.3)
    # add 19 fake items to push 005930 rank to 20
    state.screener_preview["items"] = (
        [{"ticker": f"FAKE{i:03d}"} for i in range(19)]
        + [{"ticker": "005930", "name": "삼성전자"}]
    )
    generate_signals(state, dedup_mock, _settings())
    # confidence_webai = 0.3*0.5 + 1.0*0.3 + 0.05*0.2 = 0.46 < 0.7
    assert "005930" not in state.signals


def test_sell_signal_when_stop_loss_triggered(dedup_mock):
    state = _make_state_with_holding(pnl_pct=-0.08, current_price=69000, avg_price=75000)
    generate_signals(state, dedup_mock, _settings())
    assert "005930" in state.signals
    sig = state.signals["005930"]
    assert sig["action"] == "sell"
    assert sig["confidence_webai"] == 1.0
    assert sig["pnl_pct"] == -0.08


def test_sell_signal_when_take_profit_triggered(dedup_mock):
    state = _make_state_with_holding(pnl_pct=0.16, current_price=87000, avg_price=75000)
    generate_signals(state, dedup_mock, _settings())
    assert "005930" in state.signals
    sig = state.signals["005930"]
    assert sig["action"] == "sell"
    assert sig["confidence_webai"] == 0.6


def test_silent_when_dedup_recently_sent(dedup_mock):
    state = _make_state_with_buy_candidate()
    dedup_mock.is_recent.return_value = True
    generate_signals(state, dedup_mock, _settings())
    assert "005930" not in state.signals
    dedup_mock.record.assert_not_called()


def test_sell_signal_triggers_on_anomaly_path(dedup_mock):
    """Anomaly sell: median < -1%, momentum strong_down, low bid_ratio, confidence > threshold."""
    state = PollState()
    state.portfolio = {"holdings": [{
        "ticker": "005930", "name": "삼성전자",
        "avg_price": 75000, "current_price": 70000,
        "pnl_pct": -0.067,  # within stop_loss tolerance (default -0.07): NOT triggering stop_loss
        "quantity": 100, "broker": "키움",
    }]}
    state.screener_preview = {"items": []}
    state.chronos_predictions["005930"] = {
        "median": -0.025, "q10": -0.05, "q90": 0.005, "conf": 0.85,
    }
    state.minute_momentum["005930"] = "strong_down"
    state.asking_price["005930"] = {"current_price": 70000, "bid_ratio": 0.30}
    # bid_ratio 0.30 < (1 - 0.6) = 0.4 → anomaly bid_ratio gate passes
    # confidence = 0.85*0.5 + 1.0*0.3 + 1.0*0.2 = 0.425 + 0.3 + 0.2 = 0.925 > 0.7

    generate_signals(state, dedup_mock, _settings())

    assert "005930" in state.signals
    sig = state.signals["005930"]
    assert sig["action"] == "sell"
    assert sig["context"]["sell_reason"] == "anomaly"
    assert sig["confidence_webai"] > 0.7