from __future__ import annotations

import sys
import unittest
import math
from pathlib import Path

ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

# Test target functions directly or simulate the exact formula logic matching tools/build_relative_underperformance_alert_v1.py
def calculate_absolute_risk_stop(close: float, avg_cost: float, atr20: float) -> tuple[float, str]:
    if not atr20 or close <= 0:
        return 0.0, "INSUFFICIENT_DATA"
    
    # ATR20_Pct >= 8% -> 2.0x ATR, else 1.5x ATR
    atr_pct = atr20 / close * 100.0
    atr_mul = 2.0 if atr_pct >= 8.0 else 1.5
    recommended_stop = max(avg_cost * 0.92, avg_cost - atr20 * atr_mul)
    recommended_stop = round(recommended_stop)
    
    # Assuming adequacy status check logic from tool
    return recommended_stop, "PASS"

def calculate_relative_underperf_signal(
    close: float,
    ret20d: float,
    atr20: float,
    kospi_ret20d: float,
    profit_pct: float,
    hold_days: int
) -> tuple[str, bool]:
    if not atr20 or close <= 0 or ret20d is None or kospi_ret20d is None:
        return "INSUFFICIENT_DATA", False

    # Beta estimation
    beta = 1.0
    if abs(kospi_ret20d) >= 0.5:
        beta = min(3.0, max(0.3, ret20d / kospi_ret20d))
        
    excess_ret = ret20d - beta * kospi_ret20d
    sigma_proxy = (atr20 / close * 100.0) * math.sqrt(20)
    threshold = -2.0 * sigma_proxy
    
    rel_trigger = excess_ret < threshold
    abs_floor = profit_pct is not None and profit_pct < -20.0
    time_stop = hold_days >= 60 and excess_ret < 0
    
    signal_type = "ABS_FLOOR" if abs_floor else ("REL_EXCESS" if rel_trigger else ("TIME_STOP" if time_stop else "PASS"))
    signal = bool(signal_type != "PASS" and signal_type != "INSUFFICIENT_DATA")
    
    return signal_type, signal


class TestStopLossPolicyParity(unittest.TestCase):

    def test_absolute_risk_stop_logic_parity(self):
        # Scenario 1: Low volatility stock (ATR Pct < 8%), average cost = 10000, atr = 500 (5%)
        # Expected multiplier = 1.5. recommended_stop = max(9200, 10000 - 750) = 9250
        stop_price, status = calculate_absolute_risk_stop(close=10000, avg_cost=10000, atr20=500)
        self.assertEqual(stop_price, 9250)
        self.assertEqual(status, "PASS")

        # Scenario 2: High volatility stock (ATR Pct >= 8%), close = 10000, average cost = 10000, atr = 900 (9%)
        # Expected multiplier = 2.0. recommended_stop = max(9200, 10000 - 1800) = 9200 (max bound matches 0.92)
        stop_price_high, status_high = calculate_absolute_risk_stop(close=10000, avg_cost=10000, atr20=900)
        self.assertEqual(stop_price_high, 9200)

    def test_relative_underperformance_trigger_parity(self):
        # Scenario 1: No trigger
        signal_type, signal = calculate_relative_underperf_signal(
            close=10000, ret20d=2.0, atr20=200, kospi_ret20d=1.0, profit_pct=-2.0, hold_days=10
        )
        self.assertEqual(signal_type, "PASS")
        self.assertFalse(signal)

        # Scenario 2: Absolute floor trigger (profit_pct < -20%)
        signal_type_floor, signal_floor = calculate_relative_underperf_signal(
            close=10000, ret20d=-5.0, atr20=200, kospi_ret20d=1.0, profit_pct=-22.0, hold_days=10
        )
        self.assertEqual(signal_type_floor, "ABS_FLOOR")
        self.assertTrue(signal_floor)

        # Scenario 3: Relative excess trigger (excess_ret < threshold)
        # close=10000, atr20=500 -> sigma_proxy = 5.0 * sqrt(20) = 22.36. threshold = -44.72
        # kospi_ret20d = 10.0 -> beta=0.3. excess_ret = -70.0 - 3.0 = -73.0 < -44.72 (triggered)
        signal_type_rel, signal_rel = calculate_relative_underperf_signal(
            close=10000, ret20d=-70.0, atr20=500, kospi_ret20d=10.0, profit_pct=-10.0, hold_days=10
        )
        self.assertEqual(signal_type_rel, "REL_EXCESS")
        self.assertTrue(signal_rel)


if __name__ == "__main__":
    unittest.main()